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Adolescent rats engage
the orbitofrontal‑striatal
pathway dierently than adults
during impulsive actions
Aqilah M. McCane , Lo Kronheim , Alejandro Torrado Pacheco & Bita Moghaddam
*
Adolescence is characterized by increased impulsive and risk‑taking behaviors. To better understand
the neural networks that subserves impulsivity in adolescents, we used a reward‑guided behavioral
model that quanties age dierences in impulsive actions in adult and adolescent rats of both sexes.
Using chemogenetics, we identied orbitofrontal cortex (OFC) projections to the dorsomedial
striatum (DMS) as a critical pathway for age‑related execution of impulsive actions. Simultaneous
recording of single units and local eld potentials in the OFC and DMS during task performance
revealed an overall muted response in adolescents during impulsive actions as well as age‑specic
dierences in theta power and OFC–DMS functional connectivity. Collectively, these data reveal that
the OFC–DMS pathway is critical for age‑dierences in reward‑guided impulsive actions and provide
a network mechanism to enhance our understanding of how adolescent and adult brains coordinate
behavioral inhibition.
Adolescence is a developmental period associated with risky behaviors and increased sensation seeking. While
these phenotypes may be adaptive and help adolescents acquire new skills and facilitate independence, they may
lead to impulsivity and poor decision making. Our knowledge of neural networks that subserve impulsivity in
adolescent models remains limited1,2. Impulsive actions may directly aect the ability to withhold a response that
was previously rewarding, a behavior known as response inhibition. Response inhibition decits are greater in
adolescents than adults3,4. Understanding the neural mechanisms of response inhibition in adolescents is critical
for identifying those most at risk for reckless and impulsive decision making5.
e developmental period of adolescence to early adulthood is associated with considerable neuronal matura-
tion in frontal cortical regions such as the orbitofrontal cortex (OFC) and in striatal regions6,7. In adults, these
regions have been implicated in response inhibition and impulsive actions. For example, response inhibition
is associated with activation of the OFC in healthy adults8, and a dysfunctional OFC activity is observed in
patients with disorders characterized by decits in response inhibition such as obsessive compulsive disorder,
Tourette’s syndrome and attention decit hyperactivity disorder9–12. In preclinical rodent models, OFC lesions
impair response inhibition13,14, whereas optogenetic stimulation of the OFC reduces spontaneous compulsive
behaviors15. While OFC projects to most striatal subregions, its projections to the dorsomedial striatum (DMS)16
are hypothesized to mediate response inhibition17. is notion is supported by optogenetic stimulation of this
pathway reversing decits in response inhibition in a rodent model of compulsive behavior15 and DMS lesions
impairing behavioral inhibition similar to prefrontal cortex damage18. Finally, in adolescents, OFC and DMS
neurons encode the same rewarded actions dierently than adults19,20.
Here we asked if OFC–DMS projections process impulsive actions dierently in adolescents and adults.
Sparsity of preclinical data related to neural processing of behavioral inhibition in adolescents is primarily due
to limitations caused by the brief period of adolescence in rodents where behavioral and invivo measures have
to be completed in less than two weeks. We overcame these limitations by developing the cued response inhibi-
tion task (CRIT), an operant task which measures response inhibition in both adolescents and adults21 using the
same training duration. Here we combined this behavioral model with chemogenetics or electrophysiological
recording of units and local eld potentials (LFPs) and observe age-dependent roles for OFC–DMS projections
to support impulsive actions.
OPEN
Oregon Health and Science University, Portland, OR, USA. *email: bita@ohsu.edu
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Results
OFC to DMS projection is critical for expression of response inhibition in an age‑specic
manner
To assess the role of OFC–DMS projections in response inhibition, we quantied premature responses during
CRIT training (Fig.1A) while inhibiting OFC cells that project to DMS using chemogenetics. We infused a Cre
virus (Cav2Cre) into the DMS and an inhibitory DREADD (designer receptors exclusively activated by designer
drugs;AAV5-hSyn-DIO-hM4D(Gi)-mCherry) into the OFC (Fig.1B,C). To validate the eectiveness of this
approach to inuence OFC neuronal activity, we performed invivo electrophysiology recordings and observed
signicant modulation of OFC ring rate following clozapine-N-oxide (CNO) administration (t = 2.31, p = 0.03:
Fig.1D, E) compared to saline. Moreover, to control for nonspecic eects of virus or CNO injections, we per-
formed control experiments using a control virus (AAV-hSyn-DIO-mCherry) injected into the OFC and Cav2Cre
into the DMS as well as treated surgery naïve animals with CNO (Supplemental Figure1).
We next measured the eect of inhibition of DMS projecting OFC cells in adults and adolescents during CRIT
by injecting animals with CNO (10mg/kg) or saline for 10 consecutive days 30min before behavioral testing. All
animals were injected with saline during xed ratio 1 (FR1) training to become habituated to injections (Fig.2A).
Dierences were observed in premature actions (Fig.2B). As CRIT training progressed, premature actions ini-
tially increased [main eect of session: F(9,221) = 11.29, p = 1.70 × 10–14] as correct actions also increased [main
eect of session: F(9,221) = 46.63, p < 2 × 10–16]. CNO injection, as compared to saline, signicantly increased
Figure1. Experimental methods. (A) Experimental design of the cued response inhibition task (CRIT).
Sessions begin with simultaneous presentation of an inhibit action cue (variable time 5–30s), and an action
cue which remained lit for 10s following cessation of the inhibit action cue. Created with Biorender.com. (B)
Schematic of chemogenetic methods. DREADDs receptors were expressed in DMS projecting OFC neurons
by injecting CAV/2 Cre (green) bilaterally in the DMS and inhibitory DREADDs receptors (red) in the OFC.
(C) Robust expression of both viruses in their respective brain regions is observed. (D) Administration of CNO
(10mg/kg) suppresses ring rate (Z-scored) in a spontaneously active population of OFC neurons (not limited
to DREADDs expressing units) under isourane anesthesia. (E) Compared to saline control (light grey), CNO
injection (dark grey) produced a signicant and sustained modulation of OFC ring rate.
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premature actions [main eect of group: F(1,24) = 10.52, p = 0.003; Fig.2B] suggesting that OFC projections to
DMS play a key role in behavioral inhibition. Critically, an age-related dierence emerged when we compared
the eect of CNO and saline in adolescents vs adults. Whereas the number of premature actions in the saline
control group were inuenced by both age and session [age by session interaction: F(9,108) = 3.36, p = 0.001], with
adolescents performing more premature actions than adults, the CNO adult group became more adolescent-like
in that inhibition of DMS projecting OFC cells resulted in a statistically similar amount of premature actions
in adolescents and adults [main eect of age: F(1,7) = 0.22, p = 0.66; Fig.2C]. e number of correct trials was
similarly reduced in both age groups aer CNO administration [main eect of group: F(1,24) = 5.40, p = 0.03;
Fig.2D—main eect of session: F(9,108) = 31.004, p < 2 × 10–16; Fig.2E]. Collectively these data indicate that
while OFC cells that project to the DMS are critical for CRIT performance in both groups, silencing this pathway
ablates age dierences in response inhibition.
Adolescent OFC and DMS exhibit dierent electrophysiological correlates to response
inhibition
Given the critical role of OFC and DMS in response inhibition and observed age-related behavioral dierences,
we investigated the neural correlates of relevant behavioral events in CRIT while recording units in adults and
adolescents (Fig.3A). We classied cells as putative pyramidal cells or medium spiny neurons based on their r-
ing rates and spike widths. Baseline ring rate in the OFC and DMS was statistically similar between adolescents
and adults (independent samples t-test: p values > 0.05, Fig.3B,C). Consistent with untethered animals (Fig.2),
adolescents made more premature responses than adults during CRIT [main eect of age: F(1,21) = 6.27, p = 0.02;
Fig.3D] while the number of correct trials completed was comparable between age groups [main eect of age:
F(1,16) = 0.38, p = 0.55; Fig.3E]. Adolescents also executed more total actions compared to adults [main eect
of age: F(1,20) = 5.43, p = 0.03; supplemental Figure2].
We next investigated the physiological correlates of behavior in both age groups by isolating the ring activity
of single units in the OFC and DMS. Data presented are aggregated from the last three days of recording (PND
44–46 and 69–71). While some age-related small dierences were observed during tone exposure in either region,
the more robust phasic response dierences were observed during action (premature or correct) execution. In
response to premature actions, while dierent populations of OFC putative pyramidal cells in both age groups
were inhibited or excited (Fig.4A), the global ring rate was signicantly decreased only in adults (Fig.4C). In
contrast, no dierencesbetween adolescent and adult OFC ring rates were observed aer correct responses
(Fig.4F). In the DMS, while both adolescents and adults show a suppression of MSN ring rates aer premature
responding, this eect was more persistent in adults (Fig.4G–I). Compared to adults, adolescents exhibited a
dierent response to correct actions and the resulting reward in DMS MSNs (Fig.4L). While neurons in both
groups displayed a phasic response, the temporal prole of this response was dierentin each age group.
Figure2. Chemogenetic manipulation of corticostriatal circuits. (A) Timeline of CRIT showing animals’
age at each stage for adolescents (top) and adults (bottom). Arrows represent days animals were injected with
either saline or CNO. Created with Biorender.com. (B) CNO (10mg/kg) treatment resulted in increased
premature actions in both age groups during later sessions. (C) Number of premature actions is similar between
adults and adolescents treated with CNO, but adolescent premature actions dier from adults across sessions
following saline treatment. (D) CNO treatment resulted in reduced correct actions in both age groups. (E) Both
adolescents and adults increase the number of correct actions across session. Data are presented as mean + SEM.
*p < 0.05 main eect.
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Local eld potential recordings reveal age‑specic alterations in the theta oscillations
To further understand the neural correlates of CRIT, we also analyzed LFP activity in the OFC and DMS of the
same animals depicted in Figs.3 and 4. Power spectral densities were parametrized using the FOOOF toolkit22.
We focused our analysis on the theta frequency band (5–11Hz) because this frequency is associated with cog-
nitive control23,24, response inhibition25–27, and reward processing28,29. LFP analyses included power, aperiodic
exponent and phase synchrony.
Mean theta power was extracted from putative oscillatory activity (Fig.5A). OFC theta power was signi-
cantly inuenced by event [main eect of event: F(2,560) = 5.11, p = 0.006], where power during correct actions
was reduced, relative to baseline [Tukey Post hoc, p = 0.007; Fig.5B]. ere was no eect of age on OFC theta
power across events [main eect of age: F(1,560) = 0.27, p = 0.61]. In the DMS, theta power was signicantly
inuenced by both event and age [event by age interaction: F(2,767) = 3.15, p = 0.04; Fig.5C]. Compared to adults,
adolescents exhibited stronger DMS theta power at baseline (p = 2.12 × 10–6, Bonferroni post hoc) and following
premature actions (p = 1.04 × 10–10, Bonferroni post hoc; Fig.5C).
e aperiodic exponent or power spectrum density slope was extracted and used as an index of excitation-
inhibition balance30. Steeper slopes or larger exponents are hypothesized to reect greater inhibition, while atter
slopes or smaller exponents occur when excitation is greater than inhibition30. ere was a signicant event by age
interaction for aperiodic exponents in the OFC [F(2,506) = 9.01, p = 0.0001], followed with Bonferroni corrected
post hoc tests. Adolescents had smaller exponents in the OFC during premature actions (p = 0.001, B onferroni
post hoc; Fig.5D). In the DMS, adolescent aperiodic exponents were lower than adults across all events [main
eect of age: F(1,767) = 47.81, p = 9.89 × 10–12; Fig.5E]. Aperiodic exponents were also signicantly inuenced by
event [main eect of event: F(2,767) = 104.64, p < 2 × 10–16], where both ages exhibited larger aperiodic exponents
following correct actions (p < 0.001, Tukey post hoc).
Finally, we assessed functional connectivity between the OFC and DMS in the theta frequency band by com-
puting the phase locking index γ as reported previously31 (Fig.5F). Adolescents exhibited stronger OFC–DMS
synchrony during baseline [t(2591.6) = 28.50, p < 2.2 × 10–16] and aer premature responding [t(1979.8) = 24.62,
p < 2.2 × 10–16; Fig.5G]. Higher theta synchrony was positively correlated with total premature actions r(85) = 0.23,
p = 0.03 (Fig.5H).
Figure3. Simultaneous recording from OFC and DMS in adults and adolescents as they perform CRIT (A)
Schematic of the operant chamber where simultaneous recordings in the OFC and DMS were performed.
Created with Biorender.com. (B) Baseline ring rate in adolescent (orange) and adult (blue) putative pyramidal
cells (C), and medium spiny neurons (MSNs;2). Numbers reect total number of cells recorded across
all sessions. ere were no age dierences baseline ring rate. (D, E) Behavioral data for both age groups
performing CRIT. (D) Adolescents make more premature responses during CRIT. (E) Adolescents and adults
made similar number of correct responses. Data are presented as mean + SEM. *p < 0.05, main eect of age.
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Figure4. Single unit electrophysiology recordings in OFC and DMS during cue presentation, premature
action and correct actions in adults and adolescents. (A–F) OFC heatmaps for unit ring and corresponding
mean ring rate (G–L). DMS heatmaps for unit ring and corresponding mean ring rate. e bars on the
heatmaps in the le column depict the 500ms change in ring rate following the task event (cue on, or action).
Age dierences in line graphs on the right (Z-score responses) are depicted as adults in blue and adolescents
in orange. e panel events (tone on, premature or correct action) occurred at time 0s. Adults (blue) but not
adolescents (orange) show a suppression of OFC cell ring rate aer premature responding (A). In the DMS,
MSNs show age dierences in response to premature responding (B). OFC cell ring rate was not dierent
between adults and adolescents following correct responses (C). Adolescent DMS MSNs show a larger response
to reward compared to adults (D). Numbers at the top of y-axis reect cell counts for each group. Data are
presented as mean + SEM. Black signicant bars reect permutation testing between age groups, p < 0.05.
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Figure5. Simultaneous local eld potential recordings in OFC and DMS in adults and adolescents during CRIT
performance. (A) Schematic of LFP methods for determining age dierences in LFP measures. eta power, aperiodic
exponent (power spectral density slope) and theta phase synchrony during task performance in the OFC and DMS were
isolated from the power spectrum density in adults and adolescents (please see methods for detail). (B, C) Compared to
baseline, OFC theta power is reduced following correct actions. Adolescents exhibit greater DMS theta power than adults at
baseline and following premature actions. (D, E) e aperiodic exponent in OFC and DMS. e exponentwas larger in adults
than adolescents in the OFC during premature actions. In the DMS, the aperiodic exponent was increased following correct
actions and was larger in adults compared to adolescents for all events. (F, H) Schematic and results of phase synchrony
between the OFC and DMS. Phase synchrony between the OFC and DMS was stronger in adolescents compared to adults
at baseline and during premature actions (G). Synchrony during premature actions in both age groups was also correlated
with the total number of premature responses (H). Data are presented as mean + SEM. #p < 0.05, Tukey Post hoc, *p < 0.05
Bonferroni post hoc,
⸸
p < 0.05, main eect of age, $p < 0.05, Welch’s two sample t-test.
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Discussion
A wealth of adult-focused literature implicates cortico-striatal circuits in mediating response inhibition17,32,33. e
nature of the contribution of these cortico-striatal networks and circuits to adolescent impulsivity and response
inhibition is poorly understood. We have previously observed age-related dierences in reward processing in
the OFC and DMS19,20. We therefore hypothesized that the immaturity of adolescent cortical-striatal circuits
may underlie these age dierences in impulsivity. To test this hypothesis, we used a response inhibition task,
where adolescents learn similarly to adults to withhold an action to receive a reward21, but make more premature
actions than adults. We nd that while chemogenetic inhibition of OFC–DMS pathway increases impulsive
actions in both ages, it has a more robust inuence in adults making them more adolescent-like. e analyses
of unit recording form OFC and DMS further supported our hypothesis by demonstrating a dierent pattern of
neural engagement and coordination in these regions in adults and adolescents.
OFC–DMS projection is critical for expression of impulsive actions
e OFC is strongly implicated in response inhibition. Metabolic activity in the OFC is related to compulsivity
and impulsivity in Tourette’s syndrome9. Increased activation of OFC is observed when healthy adults are engaged
in response inhibition8 whereas reduced OFC activation is observed when response inhibition is impaired in
individuals with OCD10,12 or ADHD11. In rodent models, OFC lesions are associated with increased impul-
sive choices34, impaired response inhibition13,14 and impaired reversal learning35,36. Additionally, optogenetic
stimulation of the OFC reduces spontaneous compulsive behaviors15. Collectively this literature demonstrates
that aberrant activity in the OFC across a spectrum of disorders, and in both clinical and preclinical models, is
associated with compulsive and impulsive responding.
e OFC projects to the DMS16, and this pathway is hypothesized to play a role in compulsive behaviors37.
Similar to the OFC, the DMS plays a role in response inhibition17,38–40 and goal-directed actions41–43. e OFC
inuences actions via direct modulation of the DMS44. Lesions of DMS impaired behavioral inhibition similar
to prefrontal cortex damage18, suggesting that activity in both of these regions is necessary for expression of
response inhibition. Consistent with this notion, optogenetic stimulation of the OFC-striatum pathway reverses
decits in response inhibition in a rodent model of compulsive behavior15.
Our ndings here complement the above adult-focused literature by demonstrating that the OFC projections
to DMS are necessary for appropriate expression of response inhibition. Our results further demonstrate that
the OFC–DMS projection has a more robust inuence in controlling response inhibition in adults. Specically,
the observation that chemogenetic inhibition of OFC–DMS makes adults’ response inhibition more adolescent-
like provides strong evidence that maturation of this pathway is critical for better response inhibition in adults.
Adolescents OFC and DMS neurons are engaged dierently during CRIT
OFC population activity signicantly decreased aer premature actions in adults but not adolescents. Previ-
ous work in adult models has shown that OFC activity before an action execution is modulated by previous
actions and predicts subsequent response duration, suggesting that the OFC may use previous action-related
information to inuence current actions45. Consistent with this function of the OFC, we observed that OFC
activity was signicantly altered aer premature actions in adults but not in adolescents. ese age dierences
further suggest that actions informed by action-history develop with maturity. Adolescents may, therefore, have
reduced capacity to utilize prior events to inform current events, likely due to functionally immature cortical
networks. Alternatively, age dierences in behavioral performance and corresponding cortical-striatal dynamics
may reect impaired acquisition of the CRIT. e OFC is hypothesized to guide decision making via state repre-
sentation or the formation of a cognitive map46–48. As such, inactivation of the OFC disrupts learned behavioral
performance48,49, possibly via disruption of cognitive map formation. erefore, inhibition of OFC–DMS cells
in an immature OFC network may result in a dierent pattern of cognitive mapping during task performance
in adolescence.
DMS neurons are inuenced by the OFC and inactivation of the OFC impairs DMS state representation44
and decision-making50. In our single unit data, strong age dierences were observed in the DMS aer premature
actions where both adult and adolescent ring rates decreased. e temporal prole of this phasic response
was dierent in that adult neurons remained inhibited longer whereas adolescent neurons return to baseline
quickly, possibly reecting weaker cortical inhibition in adolescents. Consistent with previous ndings20, we
observe age dierences in the DMS aer correct actions with adolescents showing a stronger phasic response
to reward compared to adults. Post-action striatal responses may reect encoding of action outcome values45,51.
ese values may be updated from trial to trial, and in this way inuence future responding. In adolescents,
impaired encoding of outcomes may result in a failure to utilize feedback to guide future behaviors resulting in
persistent premature actions. e DMS is hypothesized to encode reward value51,52, which can also inuence
future actions. Moreover, DMS-projecting OFC neurons play a role in encoding reward value50. erefore, a
larger DMS response in adolescents to reward may suggest that they assign greater value to reward than adults.
Adolescent OFC and DMS network dynamics are dierent during CRIT
Coordinated changes in neuronal activity give rise to changes in net current ux and LFP oscillations53. ese
oscillations may provide information about event-driven or state-level global activity in the brain. Importantly,
LFPs cannot disambiguate the contributions of individual neurons but their activity may be an indirect measure
of network level dynamics54. We recorded LFPs in adults and adolescents during CRIT and utilized the tting
oscillations and one over f (FOOOF) algorithm22.is algorithm extracts the aperiodic component which is
hypothesized to reect the balance between excitation and inhibition. Specically, when excitation is greater than
inhibition, the exponent is lower30,55. e OFC aperiodic exponent in adolescents was lower than adults during
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premature actions, indicating less cortical inhibition. Reduced inhibition may be associated with impulsivity in
clinical populations. For example, adolescents with ADHD, a disorder characterized by increased impulsivity
and reduced response inhibition, have smaller exponents than control adolescents56,57. Moreover, an increased
exponent is observed during response inhibition58,59. Increased cortical inhibition in adolescents observed here
may, therefore, be associated with decits in response inhibition caused by immature OFC-mediated outcome
encoding.
In our analyses we focused on the theta frequency range because theta oscillations are hypothesized to
modulate neural activity by coordinating activity across networks60 and may be important for organizing activity
during active behavior61. Moreover, theta oscillations are hypothesized to reect active learning62 and are associ-
ated with reward outcome expectancy29 and anticipation63. In our LFP data we found that adolescents exhibit
stronger theta power in the DMS during premature actions. Stronger theta in adolescents following premature
responding may therefore be associated with aberrant reward-expectation. Collectively, our electrophysiology
data during CRIT demonstrate robust age dierences in DMS activity aer premature actions. ese age-specic
dierences may reect diminished capacity of the DMS to process action-outcome relationships in adolescents
and premature actions in CRIT.
Because the adolescent frontal cortex and striatum are immature relative to adults, we tested whether that
OFC–DMS synchrony would be reduced in adolescents. Unexpectedly, we observed stronger connectivity in
adolescents and a signicant positive relationship between neural synchrony and the number of premature
actions. Recent ndings in clinical populations suggest a positive relationship between impulsive behaviors and
cortico-striatal connectivity strength. Patients with obsessive compulsive disorder, a condition characterized
by increased compulsive actions, exhibit increased striatal-OFC connectivity and impulsivity when compared
to healthy controls64. Furthermore, Sanefuji etal.65 classied children with ADHD as impulsive and inattentive
subtypes and observed that impulsive but not inattentive children exhibit increased cortico-striatal connectiv-
ity. ese data suggest that interactions between the OFC and DMS may play a causal role in age dierences
in impulsive actions. is notion is consistent with our chemogenetic experiments, where inhibition of OFC
neurons that project to the DMS abolished age dierences in premature actions. Collectively, these data suggest
OFC–DMS connectivity plays a critical role in age associated dierences in premature actions and that ine-
cient processing of task state in the OFC may inuence the DMS’s ability to encode outcome-value information
in adolescents. Given the increased premature actions of adolescents in CRIT, our OFC–DMS synchrony data
may indicate that adolescents receive weaker feedback from their actions compared adults. In this way, their
performance may be more habitual and less goal-directed, as others have hypothesized66.
Limitations
Our current experimental design was guided by the hypothesis that adolescent and adult neurons are engaged dif-
ferently during response inhibition. Given the short period of rodent adolescence, we were limited to employing
a task that could be learned and completed in less than two weeks. We chose CRIT for this study, having exten-
sively characterized it in adults and adolescents21,67. We, however, acknowledge that CRIT may not dierentiate
between learning and performance. Premature actions may result from an inability to learn the task contingency
or a failure to inhibit a response, despite knowledge that inappropriate responding will not be rewarded. us,
while our previous work had not shown any associative learning decits in adolescents68, we cannot rule out
that learning decits in adolescents may have contributed to some of the observed behavioral age dierences.
Conclusions
We investigated the role of OFC–DMS in response inhibition in adolescent and adult rats. We observed a causal
and age-dependent relationship in OFC projections to DMS, and pronounced age-dependent neuronal dier-
ences in the OFC and DMS. We hypothesize that these data reect impaired state representation, likely due to
reduced inhibition in adolescent OFC.
Methods
Subjects subjects were male and female adolescent (PND 28–46; N = 19) and adult (PND 60+; N = 15) Long Evans
rats bred in-house. Rats were pair housed until surgery under temperature and humidity-controlled conditions
using a 12-h reverse light/dark cycle. Adolescents (PND 28) and adults (PND 60) were surgically implanted with
custom-made 8-channel electrode arrays (50-µm-diameter tungsten wire insulated with polymide, California
Fine Wire) in the lateral OFC (AP 3.2, ML 3.0, DV-4.0) and DMS (AP 0.7, ML 1.6,DV -4.0) under isourane
anesthesia as described previously19,20. All animals had one week to recover before behavioral testing. All experi-
ments were performed during the dark phase, in accordance with the National Institute of Health’s Guide to the
Care and Use of Laboratory Animals, were approved by the Oregon Health and Science University Institutional
Animal Care and Use Committee and were in compliance with the ARRIVE guidelines69. Aer the end of
each experiment, animals were anesthetized, perfused with paraformaldehyde, and histology was performed to
conrm probe placements (Supplementary Figure3). Animals with probes outside of the target regions were
excluded from analyses.
Statistical analyses all analyses were performed in MATLAB (MathWorks) and R (https:// www.r- proje ct.
org/). Unless otherwise specied, all comparisons were rst tested using analyses of variance (ANOVA) testing,
followed by post hoc tests for multiple comparisons procedures.
Behavior all recordings took place in an operant chamber (Coulbourne, Instruments) equipped with a food
trough and reward magazine opposite a nose-poke port with a cue light, infrared photo-detector unit, and a
tone-generating speaker. Adolescents (PND 35–36) and adults (PND 67–68) were food restricted, habituated to
the operant box and trained to nose poke in response to a light cue for a sucrose pellet (45mg, Bio-Serv) on a
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xed ratio one schedule over two days. Aer successful acquisition of cue-action responding, all animals begin
CRIT training as described previously21. All CRIT sessions last 60min. In each trial, in addition to the light cue
that signaled that an action would lead to reward, animals were presented with an inhibitory cue (auditory tone).
Responses made during the inhibitory cue were not rewarded and coded as “premature.” Following cessation
of the inhibitory cue (variable time 5–30s), the light cue remained lit for 10s. An action executed during this
time was rewarded and coded as “correct”. Failure to respond within 10s was coded as an omission. Following
either a response or omission, a variable length inter-trial interval of 10–12s preceded the start of a new trial. All
animals experienced ten CRIT sessions. Behavior data were analyzed using mixed design analyses of variance
(ANOVA) testing with between subjects factor age and within subjects factor session.
Electrophysiology recordings single units and local eld potentials (LFPS) were simultaneously recorded during
performance of CRIT over the course of 10 sessions using a Plexon recording system. Spikes were amplied at
1000× gain, digitalized at 40kHz, and single-unit data were band pass ltered at 300Hz. Single units were isolated
in Kilosort70. OFC neurons with baseline ring rates less than or equal to 10Hz and spike widths greater than
or equal to 0.30 were classied as putative pyramidal cells71. DMS neurons with baseline ring rates less than or
equal to 5Hz and spike widths greater than or equal to 0.25 were classied as putative medium spiny neurons72.
Firing rate analyses ring rates for all units were averaged across trials and Z-score normalized to each unit’s
baseline (ITI prior to trial) ring rate. Friedman’s ANOVA was computed to determine whether ring rate
changed during events of interest. Signicant ANOVA results were followed up using Bonferroni corrected
multiple comparisons. Age dierences were assessed using permutation tests73.
Spectral analyses spectral analysis were performed on LFP recordings using the Matlab wrapper for the
FOOOF toolkit22. A power spectrum density (psd) was computed using Welch’s power spectral density estimate
and putative theta oscillation power was extracted. We did not compute power from datasets which did not
exhibit putative theta oscillations, as indicated by a frequency specic peak. e aperiodic exponent or slope of
the power spectral density was measured for all subjects. We used a broad frequency range (1–50Hz) to assess the
aperiodic t and exponents as recommended22,57. e impact of age and event on the psd slope was rst assessed
using ANOVA testing. Signicant main eects were followed up using Tukey HSD post doc testing. Signicant
interactions were followed up with Bonferroni corrected planned comparisons.
Phase synchrony data were ltered in the theta band (5–11Hz) and segregated by behavioral epoch. To meas-
ure strength of OFC–DMS synchrony, the phase locking index γ was computed by taking the complex value of
the average of all points (1/N) where
ϕ1(t)
and
ϕ2(t)
are two phases from the ltered signals, the phase dierence
θ
t
j
=ϕ1
t
j
−ϕ2
t
j
,
tj
are the times of data points, and N is the number of all data points during the given
time interval31,74–76. Age-group dierences were assed using Welch’s two sample t-test. Person’s correlation was
used to compare premature actions and synchrony across both groups.
Chemogenetics DREADD receptors were expressed in DMS projecting OFC neurons by injecting 0.5µL of
CAV/2 Cre (Montpellier Vector Platform) bilaterally in the DMS and 0.5µL of the DREADD receptor AAV5-
EF1a-DIO-hM4D(Gi)-mCherry (Addgene) in the OFC under isourane anesthesia on PND 20 as reported
elsewhere33. Animals were divided into groups of either saline or CNO treatment and tested in either adolescence
or adulthood. Aer two days of nose-poke training (PNDs 35–36, PND 58–59) animals experienced 10days of
CRIT with either CNO (10mg/kg; Hello Bio) or saline treatments, 30min prior to behavior. Aer the end of
each experiment animals were anesthetized, perfused with paraformaldehyde, and the extent of virus expression
was determined using immunohistochemistry techniques.
Data availability
e datasets generated during and/or analyzed during the current study are available from the corresponding
author on reasonable request. Data is provided within the manuscript or supplementary information les. Codes
will be made available upon request.
Received: 20 December 2023; Accepted: 2 April 2024
References
1. Adriani, W. & Laviola, G. Commentary on the special issue “the adolescent brain”: How can we run operant paradigms in a pre-
clinical adolescent model? Technical tips and future perspectives. Neurosci. Biobehav. Rev. 70, 323–328 (2016).
2. Reynolds, L. M. et al. Early adolescence is a critical period for the maturation of inhibitory behavior. Cereb. Cortex 29, 3676–3686
(2019).
3. Andrzejewski, M. E. et al. A comparison of adult and adolescent rat behavior in operant learning, extinction, and behavioral
inhibition paradigms. Behav. Neurosci. 125, 93 (2011).
4. Romer, D. Adolescent risk taking, impulsivity, and brain development: Implications for prevention. Dev. Psychobiol. J. Int. Soc.
Dev. Psychobiol. 52, 263–276 (2010).
5. Blakemore, S.-J. & Robbins, T. W. Decision-making in the adolescent brain. Nat. Neurosci. 15, 1184–1191 (2012).
6. Lebel, C., Walker, L., Leemans, A., Phillips, L. & Beaulieu, C. Microstructural maturation of the human brain from childhood to
adulthood. NeuroImage 40, 1044–1055 (2008).
7. Huttenlocher, P. R. Synaptic density in human frontal cortex-developmental changes and eects of aging. Brain Res 163, 195–205
(1979).
8. Horn, N. R., Dolan, M., Elliott, R., Deakin, J. F. W. & Woodru, P. W. R. Response inhibition and impulsivity: an fMRI study.
Neuropsychologia 41, 1959–1966 (2003).
9. Braun, A. R. et al. e functional neuroanatomy of Tourette’s syndrome: An FDG-PET study. II: Relationships between regional
cerebral metabolism and associated behavioral and cognitive features of the illness. Neuropsychopharmacology 13, 151 (1995).
10. Chamberlain, S. R. et al. Orbitofrontal dysfunction in patients with obsessive-compulsive disorder and their unaected relatives.
Science 321, 421–422 (2008).
Content courtesy of Springer Nature, terms of use apply. Rights reserved
10
Vol:.(1234567890)
Scientic Reports | (2024) 14:8605 | https://doi.org/10.1038/s41598-024-58648-w
www.nature.com/scientificreports/
11. Rubia, K., Smith, A. B., Brammer, M. J., Toone, B. & Taylor, E. Abnormal brain activation during inhibition and error detection
in medication-naive adolescents with ADHD. Am. J. Psychiatry 162, 1067–1075 (2005).
12. Remijnse, P. L. et al. Reduced orbitofrontal-striatal activity on a reversal learning task in obsessive-compulsive disorder. Arch. Gen.
Psychiatry 63, 1225–1236 (2006).
13. Chudasama, Y. et al. Dissociable aspects of performance on the 5-choice serial reaction time task following lesions of the dorsal
anterior cingulate, infralimbic and orbitofrontal cortex in the rat: Dierential eects on selectivity, impulsivity and compulsivity.
Behav. Brain Res. 146, 105–119 (2003).
14. Eagle, D. M. et al. Stop-signal reaction-time task performance: role of prefrontal cortex and subthalamic nucleus. Cereb. Cortex
18, 178–188 (2007).
15. Burguière, E., Monteiro, P., Feng, G. & Graybiel, A. M. Optogenetic stimulation of lateral orbitofronto-striatal pathway suppresses
compulsive behaviors. Science 340, 1243–1246 (2013).
16. Hoover, W. B. & Vertes, R. P. Projections of the medial orbital and ventral orbital cortex in the rat. J. Comp. Neurol. 519, 3766–3801
(2011).
17. Eagle, D. M. & Baunez, C. Is there an inhibitory-response-control system in the rat? Evidence from anatomical and pharmacologi-
cal studies of behavioral inhibition. Neurosci. Biobehav. Rev. 34, 50–72 (2010).
18. Rieger, M., Gauggel, S. & Burmeister, K. Inhibition of ongoing responses following frontal, nonfrontal, and basal ganglia lesions.
Neuropsychology 17, 272 (2003).
19. Sturman, D. A. & Moghaddam, B. Reduced neuronal inhibition and coordination of adolescent prefrontal cortex during motivated
behavior. J. Neurosci. 31, 1471–1478 (2011).
20. Sturman, D. A. & Moghaddam, B. Striatum processes reward dierently in adolescents versus adults. Proc. Natl. Acad. Sci. 109,
1719–1724 (2012).
21. Simon, N. W., Gregory, T. A., Wood, J. & Moghaddam, B. Dierences in response initiation and behavioral exibility between
adolescent and adult rats. Behav. Neurosci. 127, 23 (2013).
22. Donoghue, T. et al. Parameterizing neural power spectra into periodic and aperiodic components. Nat. Neurosci. 23, 1655–1665
(2020).
23. Cavanagh, J. F. & Frank, M. J. Frontal theta as a mechanism for cognitive control. Trends Cogn. Sci. 18, 414–421 (2014).
24. Cooper, P. S. et al. Frontal theta predicts specic cognitive control-induced behavioural changes beyond general reaction time
slowing. NeuroImage 189, 130–140 (2019).
25. Pscherer, C., Mückschel, M., Summerer, L., Bluschke, A. & Beste, C. On the relevance of EEG resting theta activity for the neuro-
physiological dynamics underlying motor inhibitory control. Hum. Brain Mapp. 40, 4253–4265 (2019).
26. Vahid, A., Mückschel, M., Neuhaus, A., Stock, A.-K. & Beste, C. Machine learning provides novel neurophysiological features that
predict performance to inhibit automated responses. Sci. Rep. 8, 16235 (2018).
27. Zavala, B. et al. Cognitive control involves theta power within trials and beta power across trials in the prefrontal-subthalamic
network. Brain 141, 3361–3376 (2018).
28. Gruber, M. J., Watrous, A. J., Ekstrom, A. D., Ranganath, C. & Otten, L. J. Expected reward modulates encoding-related theta
activity before an event. NeuroImage 64, 68–74 (2013).
29. van Wingerden, M., Vinck, M., Lankelma, J. & Pennartz, C. M. eta-band phase locking of orbitofrontal neurons during reward
expectancy. J. Neurosci. 30, 7078–7087 (2010).
30. Gao, R., Peterson, E. J. & Voytek, B. Inferring synaptic excitation/inhibition balance from eld potentials. NeuroImage 158, 70–78
(2017).
31. McCane, A. M. et al. COMT inhibition alters cue-evoked oscillatory dynamics during alcohol drinking in the rat. eNeuro https://
doi. org/ 10. 1523/ ENEURO. 0326- 18. 2018 (2018).
32. Morein-Zamir, S. & Robbins, T. W. Fronto-striatal circuits in response-inhibition: Relevance to addiction. Brain Res. 1628, 117–129
(2015).
33. Terra, H. et al. Prefrontal cortical projection neurons targeting dorsomedial striatum control behavioral inhibition. Curr. Biol.
https:// doi. org/ 10. 1016/j. cub. 2020. 08. 031 (2020).
34. Mar, A. C., Walker, A. L., eobald, D. E., Eagle, D. M. & Robbins, T. W. Dissociable eects of lesions to orbitofrontal cortex
subregions on impulsive choice in the rat. J. Neurosci. 31, 6398–6404 (2011).
35. B oulougouris, V., Dalley, J. W. & Robbins, T. W. Eects of orbitofrontal, infralimbic and prelimbic cortical lesions on serial spatial
reversal learning in the rat. Behav. Brain Res. 179, 219–228 (2007).
36. Schoenbaum, G., Nugent, S. L., Saddoris, M. P. & Setlow, B. Orbitofrontal lesions in rats impair reversal but not acquisition of go,
no-go odor discriminations. Neuroreport 13, 885–890 (2002).
37. Renteria, R., Baltz, E. T. & Gremel, C. M. Chronic alcohol exposure disrupts top–down control over basal ganglia action selection
to produce habits. Nat. Commun. 9, 211 (2018).
38. Bryden, D., Burton, A., Kashtelyan, V., Barnett, B. & Roesch, M. Response inhibition signals and miscoding of direction in dor-
somedial striatum. Front. Integr. Neurosci. https:// doi. org/ 10. 3389/ fnint. 2012. 00069 (2012).
39. Eagle, D. M. & Robbins, T. W. Inhibitory control in rats performing a stop-signal reaction-time task: Eects of lesions of the medial
striatum and d-amphetamine. Behav. Neurosci. 117, 1302 (2003).
40. Rogers, S., Rozman, P. A., Valero, M., Doyle, W. K. & Buzsáki, G. Mechanisms and plasticity of chemogenically induced interneu-
ronal suppression of principal cells. Proc. Natl. Acad. Sci. 118, e2014157118 (2021).
41. Corbit, L. H., Nie, H. & Janak, P. H. Habitual alcohol seeking: Time course and the contribution of subregions of the dorsal striatum.
Biol. Psychiatry 72, 389–395 (2012).
42. Gremel, C. M. & Costa, R. M. Orbitofrontal and striatal circuits dynamically encode the shi between goal-directed and habitual
actions. Nat. Commun. 4, 2264 (2013).
43. Yin, H. H., Ostlund, S. B., Knowlton, B. J. & Balleine, B. W. e role of the dorsomedial striatum in instrumental conditioning.
Eur. J. Neurosci. 22, 513–523 (2005).
44. Stalnaker, T. A., Berg, B., Aujla, N. & Schoenbaum, G. Cholinergic interneurons use orbitofrontal input to track beliefs about
current state. J. Neurosci. 36, 6242–6257 (2016).
45. Cazares, C., Schreiner, D. C., Valencia, M. L. & Gremel, C. M. Orbitofrontal cortex populations are dierentially recruited to sup-
port actions. Curr. Biol. 32, 4675-4687.e5 (2022).
46. Schuck, N. W., Wilson, R. & Niv, Y. A state representation for reinforcement learning and decision-making in the orbitofrontal
cortex. In Goal-Directed Decision Making (eds Morris, R. et al.) 259–278 (Elsevier, Amsterdam, 2018).
47. Sharpe, M. J. et al. An integrated model of action selection: Distinct modes of cortical control of striatal decision making. Annu.
Rev. Psychol. 70, 53–76 (2019).
48. Costa, K. M. et al. e role of the lateral orbitofrontal cortex in creating cognitive maps. Nat. Neurosci. 26, 107–115 (2023).
49. Panayi, M. C. & Killcross, S. e role of the rodent lateral orbitofrontal cortex in simple pavlovian cue-outcome learning depends
on training experience. Cereb. Cortex Commun. 2, tgab010 (2021).
50. Gore, F. et al. Orbitofrontal cortex control of striatum leads economic decision-making. Nat. Neurosci. 26, 1566–1574 (2023).
51. Kim, H., Sul, J. H., Huh, N., Lee, D. & Jung, M. W. Role of striatum in updating values of chosen actions. J. Neurosci. 29, 14701–
14712 (2009).
52. Cox, J. & Witten, I. B. Striatal circuits for reward learning and decision-making. Nat. Rev. Neurosci. 20, 482–494 (2019).
Content courtesy of Springer Nature, terms of use apply. Rights reserved
11
Vol.:(0123456789)
Scientic Reports | (2024) 14:8605 | https://doi.org/10.1038/s41598-024-58648-w
www.nature.com/scientificreports/
53. Buzsáki, G., Anastassiou, C. A. & Koch, C. e origin of extracellular elds and currents—EEG, ECoG, LFP and spikes. Nat. Rev.
Neurosci. 13, 407–420 (2012).
54. Varela, F., Lachaux, J.-P., Rodriguez, E. & Martinerie, J. e brainweb: Phase synchronization and large-scale integration. Nat. Rev.
Neurosci. 2, 229–239 (2001).
55. Waschke, L. et al. Modality-specic tracking of attention and sensory statistics in the human electrophysiological spectral exponent.
eLife 10, e70068 (2021).
56. Karalunas, S. L. et al. Electroencephalogram aperiodic power spectral slope can be reliably measured and predicts ADHD risk in
early development. Dev. Psychobiol. 64, e22228 (2022).
57. Ostlund, B. D., Alperin, B. R., Drew, T. & Karalunas, S. L. Behavioral and cognitive correlates of the aperiodic (1/f-like) exponent
of the EEG power spectrum in adolescents with and without ADHD. Dev. Cogn. Neurosci. 48, 100931 (2021).
58. Pertermann, M., Bluschke, A., Roessner, V. & Beste, C. e modulation of neural noise underlies the eectiveness of methylphe-
nidate treatment in attention-decit/hyperactivity disorder. Biol. Psychiatry Cogn. Neurosci. Neuroimaging 4, 743–750 (2019).
59. Pertermann, M., Mückschel, M., Adelhöfer, N., Z iemssen, T. & Beste, C. On the interrelation of 1/f neural noise and norepinephrine
system activity during motor response inhibition. J. Neurophysiol. 121, 1633–1643 (2019).
60. Steriade, M. Impact of network activities on neuronal properties in corticothalamic systems. J. Neurophysiol. 86, 1–39 (2001).
61. Siapas, A. G., Lubenov, E. V. & Wilson, M. A. Prefrontal phase locking to hippocampal theta oscillations. Neuron 46, 141–151
(2005).
62. B egus, K. & Bonawitz, E. e rhythm of learning: eta oscillations as an index of active learning in infancy. Dev. Cogn. Neurosci.
45, 100810 (2020).
63. Cohen, M. X. et al. Top–down-directed synchrony from medial frontal cortex to nucleus accumbens during reward anticipation.
Hum. Brain Mapp. 33, 246–252 (2012).
64. Xu, T. et al. Impaired cortico-striatal functional connectivity is related to trait impulsivity in unmedicated patients with obsessive-
compulsive disorder. J. Aect. Disord. 281, 899–907 (2021).
65. Sanefuji, M. et al. Double-dissociation between the mechanism leading to impulsivity and inattention in Attention decit hyper-
activity disorder: A resting-state functional connectivity study. Cortex 86, 290–302 (2017).
66. Hammerslag, L. R. & Gulley, J. M. Age and sex dierences in reward behavior in adolescent and adult rats. Dev. Psychobiol. 56,
611–621 (2014).
67. Simon, N. W. & Moghaddam, B. Methylphenidate has nonlinear dose eects on cued response inhibition in adults but not ado-
lescents. Brain Res. 1654, 171–176 (2017).
68. McCane, A. M. et al. Adolescent dopamine neurons represent reward dierently during action and state guided learning. J. Neurosci.
https:// doi. org/ 10. 1523/ JNEUR OSCI. 1321- 21. 2021 (2021).
69. du Sert, N. P. et al. Reporting animal research: Explanation and elaboration for the ARRIVE guidelines 2.0. PLOS Biol. 18, e3000411
(2020).
70. Allen, M., Chowdhury, T., Wegener, M. & Moghaddam, B. Ecient sorting of single-unit activity from midbrain cells using KiloSort
is as accurate as manual sorting. BioRxiv https:// doi. org/ 10. 1101/ 303479 (2018).
71. Ardid, S. et al. Mapping of functionally characterized cell classes onto canonical circuit operations in primate prefrontal cortex. J.
Neurosci. 35, 2975–2991 (2015).
72. Cayzac, S., Delcasso, S., Paz, V., Jeantet, Y. & Cho, Y. H. Changes in striatal procedural memory coding correlate with learning
decits in a mouse model of Huntington disease. Proc. Natl. Acad. Sci. 108, 9280–9285 (2011).
73. Jean-Richard-dit-Bressel, P., Cliord, C. W. & McNally, G. P. Analyzing event-related transients: Condence intervals, permutation
tests, and consecutive thresholds. Front. Mol. Neurosci. 13, 14 (2020).
74. Hurtado, J. M., Rubchinsky, L. L. & Sigvardt, K. A. Statistical method for detection of phase-locking episodes in neural oscillations.
J. Neurophysiol. 91, 1883–1898 (2004).
75. Lachaux, J.-P., Rodriguez, E., Martinerie, J. & Varela, F. J. Measuring phase synchrony in brain signals. Hum. Brain Mapp. 8, 194–208
(1999).
76. Pikovsky, A., Rosenblum, M. & Kurths, J. A universal concept in nonlinear sciences. Self 2, 3 (2001).
Acknowledgements
is work was supported by Grants R01MH048404 (B.M.), NIAAA F32AA027935 (A.M.), and Brain & Behavior
Research Foundation Young Investigator Grant (A.M.).
Author contributions
Conceptualization, A.M., B.M.; investigation, A.M., L.K., A.T.P.; formal analysis, A.M.; writing-original dra,
A.M., B.M., writing-review and editing, A.M., L.K., A.T.P., B.M.; visualization, A.M.; funding acquisition, A.M.,
B.M.
Competing interests
e authors declare no competing interests.
Additional information
Supplementary Information e online version contains supplementary material available at https:// doi. org/
10. 1038/ s41598- 024- 58648-w.
Correspondence and requests for materials should be addressed to B.M.
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