Department of Neuroscience, University of Pittsburgh, Pittsburgh, PA 15260, USA.
Adolescents often respond differently than adults to the same salient motivating contexts, such as peer interactions and pleasurable stimuli. Delineating the neural processing differences of adolescents is critical to understanding this phenomenon, as well as the bases of serious behavioral and psychiatric vulnerabilities, such as drug abuse, mood disorders, and schizophrenia. We believe that age-related changes in the ways salient stimuli are processed in key brain regions could underlie the unique predilections and vulnerabilities of adolescence. Because motivated behavior is the central issue, it is critical that age-related comparisons of brain activity be undertaken during motivational contexts. We compared single-unit activity and local field potentials in the nucleus accumbens (NAc) and dorsal striatum (DS) of adolescent and adult rats during a reward-motivated instrumental task. These regions are involved in motivated learning, reward processing, and action selection. We report adolescent neural processing differences in the DS, a region generally associated more with learning than reward processing in adults. Specifically, adolescents, but not adults, had a large proportion of neurons in the DS that activated in anticipation of reward. More similar response patterns were observed in NAc of the two age groups. DS single-unit activity differences were found despite similar local field potential oscillations. This study demonstrates that in adolescents, a region critically involved in learning and habit formation is highly responsive to reward. It thus suggests a mechanism for how rewards might shape adolescent behavior differently, and for their increased vulnerabilities to affective disorders.
During adolescence a myriad of neurodevelopmental changes occur (1) that may affect how salient events, such as rewarding stimuli, are processed. Such neural processing changes could underlie some of the common behavioral predilections seen in adolescents across mammalian species, such as increased risk-taking (1–5), as well as the increased tendencies to develop disorders like addiction, depression, and schizophrenia (6–8). Before we can understand the neural substrate of these vulnerabilities, we must first learn more about the typical neural processing patterns of the adolescent brain, compared and contrasted with those of the adult.
Essentially every behavioral and psychiatric vulnerability of adolescence is apparent during motivational contexts. It is therefore important to compare the neural activity of adolescents with that of adults during motivated behavior. Motivated behavior is action that facilitates an adjustment in the physical relationship between an organism and stimuli (e.g., the probability of or proximity to a particular reward) (9). Such behavioral contexts, however, will naturally complicate the analysis of neural activity: How do we know that neural differences don’t merely reflect a behavioral performance difference between the two age groups? Is a difference in neural processing simply due to a behavioral confound, or are there more basic differences in the ways that adolescents encode and process salient events in a motivational context? We performed in vivo single-unit electrophysiological recording to compare the neural activity of adolescents with that of adults during salient events when behavioral performance was indistinguishable between the two groups (e.g., reward retrieval latencies in late sessions when the task was well learned). In doing this we effectively used a “behavioral clamp” that allowed us to identify fundamental age-related processing differences that were not confounded by performance.
Though much of the adolescent brain has yet to be examined in this way, we focused on the dorsal striatum (DS) and nucleus accumbens (NAc) because of their central role in motivated behavior. Together, these brain regions are involved in association learning, habit formation, reward processing, and the adaptive control of behavioral patterns (10–13). The striatum receives projections from cortical regions involved in sensory, motor, and cognitive processes (14), as well as dopaminergic input (15). The NAc, part of the ventral striatum, receives afferents from the amygdala (16) and prefrontal cortex (17), and dopaminergic afferents from the ventral tegmental area (18). The NAc is considered key to the translation of motivation to action (19) and is central to some current hypotheses regarding the neurobiological underpinnings of adolescent risk taking and sensation seeking (5, 20, 21).
Neural unit activity was recorded from the DS and NAc (Fig. S1) of adolescent (n = 16) and adult (n = 12) rats as they learned to associate an instrumental action (poke) with a reward outcome (food pellet; Fig. 1A). Behavioral data are shown combined (Fig. 1 B–D), because no statistical differences were observed between regions. There were no significant age-related differences across training in the number of trials per session [F(1, 1) = 1.74, P = 0.20]; the latency from the cue to the instrumental poke [F(1, 1) = 0.875, P = 0.36]; or latency from the instrumental poke to the entry into the food trough [F(1, 1) = 0.82, P = 0.36]. The latency from cue onset to instrumental poke appeared to be different in the early sessions, although this was not statistically significant and was driven by three outlier animals that had not yet learned the association (Fig. 1C, Inset). From session 4 onward, all measures reached a stable maximum in both age groups. During these sessions the average adult and adolescent latency from the instrumental response to entry into the food trough were (mean ± SEM) 2.47 ± 0.12 s and 2.54 ± 0.17 s, respectively.
Consistent DS neural population responses around the instrumental poke and food trough entry were observed as rats learned the action–outcome association and performed numerous trials in each session (i.e., sessions 4–6; Fig. S2A). A closer examination of this activity during sessions 4–6 reveals similarities in the activity of some neuronal groups, but considerable differences in others (Fig. 2). About 10% of recorded neurons became activated at the trial-onset cue, with few cells becoming inhibited (Fig. 2 A and C, Left). The distributions of adolescent and adult firing-rate Z-scores was not different at this time (Z = 1.066, P = 0.29; Fig. 2B, Left). There were also no age-related differences in the proportions of activated, inhibited, and nonsignificant neurons to the cue [χ2(2, n = 570) = 2.35, P = 0.31; Table 1]. The proportion of activated cells and their magnitude of activity increased in both groups before the instrumental response, although such magnitude increases were greater in adolescents (Z = −2.41, P = 0.02; Fig. 2B, Center). Age-related differences in the response-type proportions during the 0.5 s before the instrumental poke were significant [χ2(2, n = 570) = 10.01, P < 0.01], an effect driven by a larger proportion of adult-inhibited units (Z = 3.05, P < 0.01; Table 1). Immediately after the instrumental response, cells that were previously activated became inhibited, as did many units that were not previously engaged (Fig. 2A, Center). This led to a transient downward deflection in population activity, which increased again at age-specific rates, with continued statistical differences between adolescent and adult activity during the 0.5 s after the instrumental response (Z = 2.19, P = 0.03; Fig. 2B, Center). During this period the proportions of response types again differed between the two [χ2(2, n = 570) = 10.57, P < 0.01], due to a larger proportion of adult-activated units (Z = 2.87, P < 0.01; Fig. 2C, Center and Table 1). Many of the same neurons that increased their activity before the instrumental poke became transiently inhibited and then activated again before entry into the food trough (heat plot rows showing a red-blue-red pattern in Fig. 2A, Center). The timing of this pattern differed between adolescents and adults. A sizeable proportion of adolescent neurons remained activated until reward. Such “reward-anticipation neurons” were sparse in adults (Fig. 2A, Right). In addition to differences in time course, adolescent neurons that activated in the 0.5 s before entry into the food trough also peaked with a higher magnitude (Z = −7.63, P < 0.01; Fig. 2B, Right). This overall pattern of activity was relatively stable throughout sessions 4–6 (Movie S1), although a random sampling of units demonstrates within-unit variability for some units (Fig. S3). The proportions of activated and inhibited units differed [χ2(2, n = 570) = 41.18, P < 0.01], with adolescents and adults, respectively, having significantly larger proportions of activated (Z = −6.21, P < 0.01) and inhibited units (Z = 4.59, P < 0.01; Fig. 2C, Right and Table 1). In the 0.5 s after reaching the food trough, adolescents continued to exhibit stronger activity (Z = –6.43, P < 0.01). The proportions of activated, inhibited, and nonsignificant remained different as it had immediately before entry into the food trough [χ2(2, n = 570] = 31.18, P < 0.01; Fig. 2C, Right and Table 1). Again, adolescents had a larger proportion of activated units (Z = –4.89, P < 0.01) and a smaller proportion of inhibited units at this time (Z = 4.36, P < 0.01).
In the NAc, average adolescent and adult spiking activity went from little or variable task-related responses to more consistent patterns (Fig. S2B). By session 4, both groups had a similar increase and then decrease in phasic activity at the instrumental poke. This pattern was more pronounced leading up to and following reward (food trough entry). A closer examination of the NAc phasic neural activity reveals several close similarities in the pattern and extent of neuronal activation and inhibition, along with some notable differences (Fig. 3). Specifically, the onset of the cue light led to the activation of about 10% of NAc neurons in both adolescents and adults, with few neurons becoming inhibited, and no significant age-related difference in the proportion of activated or inhibited neurons at this time [χ2(2, n = 349) = 1.51, P = 0.47], and no differences in overall population activity (Z = 1.82, P = 0.07; Fig. 3, Left). Once neurons activated for a trial, they tended to remain activated until the animal’s entry into the food trough. The temporal dynamics were such that some proportion of neurons became more strongly activated around both the instrumental poke and food trough entry. No age-related differences in population activity (Z = –0.16, P = 0.87) or unit category proportions [χ2(2, n = 349) = 0.22, P = 0.90] were found in the 0.5 s preceding the instrumental poke. After the instrumental poke, adults showed higher average activity (Z = 4.09, P < 0.01) and differences in unit category proportions [χ2(2, n = 349) = 7.23, P = 0.03] due to a greater proportion of adult activated neurons (Z = 2.53, P = 0.01; Fig. 3C, Center and Table 1). Similarly, higher average adult activity was observed in the 0.5 s before food trough entry (Z = 2.67, P < 0.01), and again, different unit category proportions were observed [χ2(2, n = 349) = 6.64, P = 0.04] due to significantly larger proportions of adult activated units (Z = 2.32, P = 0.02; Fig. 3C, Right and Table 1). During this period, trial-by-trial neural activity still exhibited some measure of stability, however less so than in the DS (Movie S2). There was no significant age-related difference in population activity in the 0.5 s after entry into the food trough (Z = −0.61, P = 0.54), although unit proportion differences were present [χ2(2, n = 349) = 7.81, P = 0.02]. This reflected a significantly greater proportion of inhibited adolescent units at this time (Z = −2.81, P < 0.01; Fig. 3C, Right and Table 1). Thus, though there were some differences between the groups, the general pattern of neural responses (and activity across units) was more similar in the NAc than in the DS.
Average normalized LFP spectrograms were similar for adolescents and adults in both the NAc and DS (Fig. 4). Before food trough entry, in the NAc, both adolescents and adults exhibited decreased power in β (13–30 Hz) and γ (>30 Hz) bands, with more-extensive γ-power reductions in adults. After entry into the food trough, both groups exhibited transient β-power increases centered around 20 Hz. There was a tendency for greater adolescent LFP power in lower frequencies such as θ (3–7 Hz) and α (8–12 Hz), with significant age-related differences being found ∼500 ms after food trough entry (Fig. 4 A and B). Similar patterns were seen in the DS, with slightly stronger adult increases in β-power immediately after entry into the food trough (Fig. 4 C and D). Overall, the statistical contrast maps (Fig. 4 B and D) demonstrate the similarity in the reward-related LFP activity of adolescents and adults across many frequencies, with several noted exceptions.
We found a strong reward-related activation in the adolescent but not adult DS, a structure associated with the formation of habits and the adaptive control of behavioral patterns (11–13, 22). The NAc responded similarly in both age groups; although some unit activity differences were seen in the NAc, these differences were smaller and more transient, and the time course of neural activity was highly similar between groups in this region. These findings demonstrate regional heterogeneity related to reward processing in the functional maturity of basal ganglia structures during adolescence and, with the DS, suggest a heretofore overlooked locus of adolescent neural processing differences that may be directly relevant to age-related vulnerabilities. We also found that although significant age-related differences were seen at the unit level, such differences were not readily observable in the power of LFP oscillations, which are more akin to the larger-scale regional signals of fMRI and EEG (23).
Phasic neural activity data suggested that the precise role of the DS during reward anticipation, or the influence of rewarding stimuli on its neural representations, is different in adolescents vs. adults. Both groups had units that became activated at the beginning of trials, briefly inhibited at the instrumental response, and then activated again. Among these, consistent with other studies, adult units were reactivated earlier and returned to baseline before reward (24, 25). The activation of their adolescent counterparts, in contrast, persisted all of the way to the time of reward retrieval. Thus, only adolescents had a sizable group of what could be described as reward-anticipation neurons in the DS. Although others have previously observed prereward activity in the DS (24–26), the critical point here is that adolescents and adults have a different balance and time course in their patterns of such activity. The striatum is thought to play a direct role in situation–action associations (25) and may serve as the actor in an “actor-critic” model for biasing behavior toward more advantageous actions (27). The striatum receives dopamine input from the substantia nigra and glutamate projections from cortical regions; it sends GABA projections to globus pallidus, which further projects to thalamus, ultimately looping back to the cortex. Afferent signals from an immature prefrontal cortex or basal ganglia regions could in part account for the age-specific patterns presently observed in the DS. Indeed, we have previously observed reduced inhibition and increased activation in portion in adolescent orbitofrontal cortex (OFC) during this task (28), which directly projects to this region of DS (29).
Consistent with previous reports of increased LFP θ- and β-oscillations in the DS during voluntary behavior (30, 31), both adolescents and adults exhibited these before and after food trough entry. Despite the substantial single-unit activity differences in the DS, LFP oscillations were highly similar between the two age groups in both the DS and NAc. This finding is critical because human adolescent studies have focused on larger-scale functional measures such as fMRI and EEG. We show that robust age-related unit activity differences can be found even when larger-scale regional oscillations, which better correlate with fMRI signals, are similar (23). Though the functions of basal ganglia LFP oscillations are unknown, they are modulated by behavioral context (30, 31), which was the same for the two age groups.
In the NAc, aside from some transient differences, the proportions of recruited activated and inhibited units, and the time course of their responses, were generally similar, as reflected in the average normalized population activity. Manipulations of the NAc affect motivation, baseline behavioral activity, and the learning and execution of instrumental behavior (32–35). In the present study, adolescent neural activity differences in NAc were modest and transient compared with those in DS. fMRI studies in humans have been inconsistent in comparisons of reward-related NAc activity in adolescents vs. adults. Some studies have shown stronger NAc adolescent signals to reward (36, 37) and others have found weaker ones (38) or more complex context-dependent patterns (39). This study, which records subcortical single-unit and LFP activity in awake-behaving adolescents, sheds light on this issue: we demonstrate that such age-related differences can depend on the type of signal measured. Our findings are also consistent with previous evidence that functional maturity is reached in the NAc earlier than other regions like the OFC (37, 28). However, in finding that adolescent DS unit activity differs from that of the adult, we conclude that this is not simply a cortical vs. subcortical distinction as has been proposed (40).
It is important to underscore that the neural activity differences in the present study were observed despite a lack of measured behavioral differences. Because of the role of the DS in the execution of behavioral patterns, neural differences may be due in part to unmeasured behavioral difference. Though such differences are always possible, in the present study they seem highly unlikely for a few reasons. Neural comparisons were made only when rats were highly proficient with the task and were observed to be highly task focused. The period of greatest neural differences was the time between the instrumental response and entry into the food trough, whereas the average latency of this behavior was essentially identical for the two age groups. Furthermore, neural differences were consistently observed in certain places (e.g., during reward anticipation) but not others (e.g., response to the trial-onset cue), and though the time course of neuronal activation often differed substantially, the time course of neuronal inhibition was generally similar in both brain regions of each age group. Together, these findings are consistent with the interpretation that fundamental age-related neural processing differences exist, especially in the DS, even during similar behavior/contexts, which speaks to differences in neural architecture, processing efficiency, and/or the physiological impact of salient events.
In conclusion, we found that reward-related salient events strongly tap into the DS of adolescents but not adults, which could indicate a new locus within networks responsible for age-related behavioral and psychiatric vulnerabilities. This basal ganglia structure plays a central role in normal learning and memory, habit formation, and other aspects of motivated behavior, and its dysfunction is associated with psychiatric problems (41–43). Therefore, learning more about how the activity of this region changes through development, along with its interaction with other key brain regions, will be critical to our understanding of the mechanisms of adolescent vulnerabilities and the future design of clinical interventions. The complexity of adolescent behavioral and psychiatric vulnerability is likely multifactorial, involving many brain regions. Thus, the DS is only one of many interacting regions that together (and not in isolation) are likely critical to the behavioral and psychiatric vulnerabilities of adolescence. It is our hope that with techniques like adolescent electrophysiological recording and the behavioral clamp approach to studying age-related neural processing differences in behavioral contexts, we can begin to appreciate the substrates of adolescent vulnerability at the network level.
Materials and Methods
Subjects and Surgery.
Animal procedures were approved by the University of Pittsburgh Animal Care and Use Committee. Adult male (postnatal day 70–90, n = 12) and pregnant dam (embryonic day 16; n = 4) Sprague–Dawley rats (Harlan) were housed in climate-controlled vivaria with 12-h light/dark cycle (lights on at 7:00 PM), and ad libitum access to chow and water. Litters were culled to no more than six male pups, which were then weaned on postnatal day 21 (n = 16). Adult surgeries were performed after a minimum of 1 wk of habituation to housing. Adolescent surgeries were performed at postnatal day 28–30. Eight-wire microelectrode arrays were implanted in NAc or DS (SI Materials and Methods). Recordings were made as described previously (28) while rats performed a behavioral task. Single units were isolated using Offline Sorter (Plexon) through a combination of manual and semiautomatic sorting techniques (44).
Behavioral testing procedures were conducted as described previously (28, 45). Rats learned to perform an instrumental poke for food pellet rewards (Fig. 1A and SI Materials and Methods). At each session, the total number of trials, the average latency from trial-onset cue to the instrumental response, and the latency from the instrumental response to pellet retrieval were assessed. Age × session repeated-measures ANOVAs were performed using SPSS software on all of these measures (α = 0.05), with lower-bound df corrections where the assumption of sphericity was violated.
Electrophysiological data were analyzed using custom-written Matlab (MathWorks) scripts along with functions from the Chronux toolbox (http://chronux.org/). Single-unit analyses were based on peri-event time firing-rate histograms in windows around task events. Single-unit activity was Z-score normalized based on the mean and SD firing rates of each unit during the baseline period (a 2-s window beginning 3 s before cue onset). Average population unit activity was plotted around task events. Statistical comparisons of adolescent and adult unit activity were made on a priori time windows of interest (0.5-s windows after the cue, before and after the instrumental poke, and before and after entry into the food trough) using Wilcoxon rank-sum tests (presented as Z-values), Bonferroni corrected for multiple comparisons. The null hypothesis was rejected in this analysis when P < 0.01. Movies S1 and S2 represent locally estimated scatterplot smoothed (LOESS) average normalized firing-rate activity across five trials moving in single-trial steps through video frames during sessions 4–6. Video time represents the evolution of activity through the trials of each session. Units were also categorized as activated or inhibited in particular time windows if they contained three consecutive 50-ms bins with Z ≥ 2 or Z ≤ −2, respectively. These criteria were validated as yielding low false-categorization rates via nonparametric bootstrap analyses as described previously (39) (SI Materials and Methods). Once units were categorized, χ2 analyses were performed on a priori windows of interest for all activated, inhibited, and nonsignificant units. Only significant χ2 tests were followed by post hoc Z-tests for two proportions to determine the underlying significant category differences. The null hypothesis was rejected when P < 0.05, indicated in Table 1 with bold type. To visualize the time course of unit recruitment (i.e., as activated or inhibited), category analyses were performed in 500-ms moving windows (in 250-ms steps) in larger windows time-locked to task events.
After removing trials in which the raw LFP voltage trace contained clipping artifacts or outliers (±3 SD from the mean voltage), trial-averaged power spectra were computed for each subject using fast Fourier transform (SI Materials and Methods). Power spectra were averaged for each age group. T-score contrast maps comparing the normalized LFP power of adolescent and adult spectrograms for each time × frequency bin were plotted to highlight age-related similarities and differences.
Support for this work was provided by the National Institute of Mental Health, the Pittsburgh Life Sciences Greenhouse, and an Andrew Mellon Foundation Predoctoral Fellowship (to D.A.S.).
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
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