Divergent representation and processing of task cues in sensory and prefrontal cortices of preterm-born mice

Domain: neuroscience
Published: 2026-02-05
Editorial outcome: minor_revision

Abstract
Preterm birth is a leading risk factor for atypicalities in cognitive and sensory processing, but it is unclear how prematurity impacts circuits that support these functions. To address this, we trained adult male and female mice born a day early (preterm mice) on a visual discrimination task and found that they fail to achieve high levels of performance due to increased responding to the non-rewarded cue. The representation of the non-rewarded cue in the prefrontal cortex (PFC), a brain area that mediates response inhibition, is significantly weaker in regular spiking, putative pyramidal neurons in preterm mice, while their fast-spiking interneurons show blunted responses to both task cues. Similar cue representation is present in the PFC of adolescent term-born mice, suggesting that preterm birth disrupts prefrontal maturation. Altogether, our study describes the long-term impact of preterm birth on prefrontal circuits and highlights their sensitivity to traumatic experiences during the perinatal period.

Introduction
Children born prematurely (<37 weeks of gestation) are at 4-5x higher risk for developing intellectual disability (ID) and attention-deficit/hyperactivity disorder (ADHD), at 10x higher risk for developing autism spectrum disorder (ASD), and display persistent deficits in executive function and academic performance. As preterm labor occurs in >10% of all births in the US and worldwide, preterm birth is the leading risk factor for cognitive and neurodevelopmental conditions. Yet, the neural circuits whose function may be disrupted by preterm birth are still unknown.

Impairments in the structure and function of the frontal cortex are frequently associated with executive dysfunction and neurodevelopmental conditions with high incidence in the preterm population. Unsurprisingly, neuroimaging and neurophysiological studies in preterm infants and children revealed reduced volume of and connectivity between frontal, thalamic, and sensory areas. On a cellular level, studies of postmortem prefrontal cortex samples obtained from preterm fetuses demonstrated reduced density of inhibitory interneurons and altered expression of genes related to inhibitory neurotransmission. Some of the changes in brain structure can be recapitulated in animal models of prematurity, which also display disrupted layer-specific distribution and proportion of cortical interneurons, and alterations in the expression of multiple neurotransmission markers. Behaviorally, animal models of prematurity often display persistent hyperactivity, and some cognitive deficits, but the association between preterm birth-related changes in the frontal cortex and high incidence of cognitive dysfunction in the preterm population remains unclear.

To address this, we performed a detailed neurophysiological study of prefrontal function in adult mice born prematurely that were trained on a prefrontal cortex (PFC)-dependent visual discrimination task. While adult mice born 1 day early do not display any changes in baseline visual processing and locomotor activity, they show a significantly impaired learning trajectory. Preterm mice respond more than term mice to the non-rewarded cue, have weaker representation of the non-rewarded cue in the PFC, and show disinhibition of neuronal firing during task performance. Cue representation in regular-spiking (RS) and evoked activity of fast-spiking (FS) neurons in the adult preterm PFC resemble those of adolescent term-born mice, suggesting that preterm birth disrupts prefrontal maturation. Together, our study describes circuit- and cell-type-specific impairments in prefrontal function and top-down sensory processing after preterm birth, paving a path towards uncovering the circuit mechanisms of cognitive dysfunction in the preterm-born population.

Results
Adult preterm mice commit more errors during visual discrimination

Children born preterm have an increased rate of cognitive deficits and neurodevelopmental conditions with unclear circuit origins. As we previously found that preterm birth similarly impacts the resting neural activity in the primary visual cortex (V1) of juvenile mice and infants, we hypothesized that mice born preterm would also display cognitive deficits. To test this, we chose to train the mice in a visual discrimination task, a classical learning task that facilitates electrophysiological probing of neural circuits in awake, behaving mice.

Preterm mice were generated through subcutaneous injections of mifepristone (MFP) to timed-pregnant C57BL/6 dams on gestational day (GD) 17 (Fig. 1a), resulting in the birth of viable preterm pups on GD18. Term pups in our colony are born on GD19 with very little variability, as previously published. Before training, adult (2-7 months old) male and female term and preterm mice were implanted with headposts for head fixation and their baseline visual function in form of orientation selectivity was tested using in vivo electrophysiology. We collected neuronal responses to sinusoidal oriented gratings from all cortical layers (6 orientations 30° apart), at 100% contrast and 0.15 cycles per degree spatial frequency; Fig. 1b), and determined the orientation selectivity index (OSI; Fig. 1c), the number of orientation selective neurons (Fig. 1d), orientation tuning width (Fig. 1e), and the distribution of preferred frequencies. While neurons in the V1 of naive preterm mice had elevated baseline firing rates (during the presentation of the blank screen; median ± MAD: Term = 2.41 ± 1.01 spikes/s; Preterm = 7.49 ± 3.65 spikes/s, p=0.061, Mann-Whitney t-test U=82, 414 neurons from n=11 term mice and 306 neurons from n=10 preterm mice), we found no significant differences in any of the orientation selectivity measures between term and preterm mice (OSI median ± MAD: Term = 0.78 ± 0.05, Preterm = 0.81 ± 0.08, Mann Whitney t-test p=0.86, U=52; % selective neurons mean ± SEM: Term = 77.59 ± 3.81, Preterm = 72.04 ± 5.22, t-test p=0.4, t=0.86, df=16.84; tuning width (°) median ± MAD: Term = 14.02 ± 1.08, Preterm = 15.19 ± 1.73, Mann Whitney t-test p=0.29, U=70.5; distribution of orientation preferences p=0.92, X2 test). Increase in neuronal firing at the preferred orientation (calculated as the ratio between firing at the preferred orientation and baseline firing) was not different between term and preterm mice (median ± MAD Term = 6.4 ± 1.75, Preterm = 6.59 ± 2.3, Mann-Whitney t-test p=1, U=55). Our results hence demonstrated that preterm birth in mice does not negatively impact baseline orientation selectivity and that they can be trained in an orientation discrimination task.

To prepare the mice for task training, adult term and preterm mice were implanted with headposts, and then gradually water-restricted and habituated to the experimental setup and handlers over 7-10 days, after which the training began. To account for the effects of prenatal mifepristone exposure on behavior, we included an additional group of mice that were born term to dams injected with mifepristone on GD18 (MFP Term, Fig. 2a). Mice were trained on the task in 2 daily sessions of 100 trials, where each session consisted of 50 randomized presentations of 120° and 60° oriented gratings each. 120° was paired with the delivery of 10µl of water, while 60° had no consequence (Fig. 2b, c). Licks of the spout were recorded and quantified as Hits (licks to 120°), Misses (no licks to 120°), False Alarms (licks to 60°) and Correct Rejections (no licks to 60°), which were then used to quantify the discriminability [d’=z(Hits)-z(False Alarms)]. Mice were considered trained after reaching a d’ of 2 or more for 3 sessions in a row, and they were trained for a maximum of 4000 trials (20 days of training) if they did not cross the threshold within that timeframe. In our task, Hits remained high throughout the training, while False Alarms gradually decreased (Fig. 2d and Supplementary Fig. 1), driving an increase in d’. In term mice from both groups, d’ increased in a linear fashion and most mice crossed the threshold within 2 weeks of training (Fig. 2e and f). While preterm mice also displayed an increase in d’, the learning trajectory was significantly different from that of term-born mice (LMM, effect of Animal Group p=0.009; Term vs Preterm p=0.007, MFP Term vs Preterm p=0.02, Term vs MFP Term p=0.78; estimated marginal means (EMM) ± SE Term = 2.06 ± 0.16, MFP Term = 2.01 ± 0.18, Preterm = 1.6 ± 0.13; n=22 Term, 29 Preterm and 17 MFP Term mice; Supplementary Table 1), and a fraction of them (17.24%) failed to reach the criterion within the 4000 trial limit (Fig. 2e and f; Kolmogorov-Smirnov test of d’ values from the last training session Term vs Preterm: D=0.43, p=0.009, Term vs MFP Term: D=0.34, p=0.14, MFP Term vs Preterm: D=0.2893, p=0.23), confirming that preterm birth negatively impacts learning. Naive preterm mice had significantly lower Hit Rates at the onset of training, which recovered as the training progressed (Fig. 2g and Supplementary Fig. 1; ANOVA birth × training p=0.016, F(2,65)=4.37; Bonferroni post-hoc naive Term vs Preterm p=0.005, Term vs MFP Term p>0.9, MFP Term vs Preterm p=0.0013; all p>0.9 among Trained groups). However, their False Alarm rates remained high throughout the training (Supplementary Fig. 1) and were significantly higher at the end of the training compared to both groups of term mice (Fig. 2h; ANOVA birth × training p=9.93 × 10-5, F(2,65)=10.66; Bonferroni post-hoc naive Term vs Preterm p>0.9, Term vs MFP Term p=0.13, MFP Term vs Preterm p=0.04; Trained Term vs Preterm p=0.036, Term vs MFP Term p=0.53, MFP Term vs Preterm p=0.0007). To test if the effect on the Hit Rates and False Alarms was driven by preterm mice that failed to reach the criterion, we repeated the analyses while excluding those animals. We found that the interaction between birth timing and training remained for both Hit Rates and False Alarms (ANOVA birth × training HR p=0.04, F(2,60)=3.2; FA p=0.001, F(2,60)=7.7), demonstrating that preterm birth impacts task performance regardless of whether the animal learned the task.

Our experiments included both male and female mice of sufficient numbers to segregate the data by sex and test if birth has a sex-specific effect (Supplementary Fig. 2). LMM with Animal Group and Sex as fixed effects was fitted to the learning trajectories, which revealed that sex has a significant impact on the learning trajectory (p=0.021), with female mice in all groups reaching lower d’ values (Supplementary Table 1). Analyses of Hits and False Alarm rates in trained mice (Supplementary Fig. 2B, C) revealed no effect of sex, while the effect of birth was still present (ANOVA Hits: birth × sex p=0.48, F(2,62)=0.74; sex p=0.42, birth p=0.02; False Alarms: birth × sex p=0.12, F(2,62)=2.2; sex p=0.38, birth p=0.03). Our results hence demonstrate that preterm birth negatively impacts the learning trajectory of both sexes, as well as that female mice from all groups reach lower d’ values during training.

To test if the poor performance of preterm mice in the task is due to a deficit in motivation, we compared the lick rates to the rewarded cue (binned in 20 trials) during the last training session (Fig. 2I). Lick frequencies in response to task cues gradually dropped during the training session, which has been proposed as a measure of motivation. An LMM was fitted to the data using Animal Group and Trial Block as fixed effects, which confirmed that the frequency of licks to the rewarded cue is reduced at the end of the session (Trial Block p<0.0001; Supplementary Table 2), and that lick frequencies do not differ significantly between animal groups (p=0.15). Lick frequencies were different in naive mice, with preterm mice showing fewer licks in response to the rewarded cue (Supplementary Table 3; Animal Group p=0.01). However, as in trained mice, all groups showed a reduction in lick frequencies towards the end of the first session (Trial Block p=0.04). Together, these results indicate that poor performance of preterm mice (at least at the end of the training) is not due to deficits in motivation.

Our task is performed in head-fixed mice (on a treadmill), which may mask locomotion deficits in preterm mice. To test this, we tested the locomotor activity of term and preterm mice in an open field using an automated approach (Fig. 2j). ANOVA (Welch’s) revealed a significant and small reduction in locomotor activity of MFP term mice when compared to term mice, while the level of locomotor activity in preterm mice was comparable to term mice (F(2,56.98)=3.9, p=0.03; Dunnett’s T3 multiple comparisons test Term vs. MFP Term p=0.03, Term vs. Preterm p=0.83, MFP Term vs. Preterm p=0.41). While ANOVA found a significant difference between groups of term mice in their preference for moving close to the walls of the open field arena (Fig. 2k, thigmotaxis; F(2,51.62)=4.08, p=0.03), post hoc tests revealed only marginal differences (Dunnett’s T3 multiple comparisons test Term vs. MFP Term p=0.07, Term vs. Preterm p=0.06, MFP Term vs. Preterm p=0.87), indicating largely preserved exploratory activity in preterm mice.

Altogether, our results demonstrate that preterm mice display impaired learning in a visual discrimination task, despite visual function, locomotor activity and motivation comparable to term-born mice. As learning impairments in mouse models of neurodevelopmental conditions are often associated with impaired visual processing during task performance, we next characterized the processing of task cues in the V1 of trained term and preterm mice while the mice were engaged in the task.

Selectivity for task cues is weaker in putative pyramidal neurons in preterm V1

Visual discrimination training increases the representation of and responsiveness to trained cues in the V1, both thought to support task acquisition. As preterm mice display impairments in task acquisition (Fig. 2), we asked if the processing and representation of task cues in the V1 of preterm mice are altered compared to term mice. To address this, we used in vivo electrophysiology to isolate neuronal responses to task cues in trained mice while they were engaged in the task (Fig. 3a, b). Firing of neurons in the V1 of trained term and preterm mice displayed the typical transient (early, 100 ms after the onset of lick) and sustained (late) components, where the peak of the sustained component typically preceded the onset of licks (Fig. 3c). To test the relationship between V1 spiking and licks, we computed event correlations in 1 ms time bins for all isolated units while using licks as the reference (Fig. 3d). As trained mice lick to the rewarded cue in bouts (Fig. 2d), with 100–200 ms on average between licks, we restricted the correlation window to 200 ms. We found that the bulk of V1 unit firing occurs prior to lick events, with a peak at 55 ms and a significantly higher number of spiking events preceding the licks (Fig. 2d; Wilcoxon matched-pairs signed rank test, p=0.018, W=-1354).

To compare the representation of task cues between term and preterm V1, we calculated the percentiles of neurons (per mouse) whose activity was significantly modulated by task cues (as determined by the Wilcoxon signed-rank test of pre- and post cue firing rates). We calculated the direction of the modulation as the selectivity index (SI): (Rcue - Rbaseline)/(Rcue + Rbaseline), with positive values indicating cue-evoked increase in firing and negative values indicating cue-evoked suppression. We further split the units into broad spiking (putative pyramidal neurons) and narrow spiking (putative fast-spiking inhibitory interneurons) based on the width of their action potential waveform (Supplementary Fig. 3; 406 neurons from 12 term mice and 253 neurons from 9 preterm mice).

Both regular-spiking (RS) and fast-spiking (FS) neurons in term and preterm mice had comparable representation of task cues in the V1 with a slight over representation of the rewarded cue (RM ANOVA with birth and the direction of modulation as factors; Fig. 3e: RS Reward: birth × modulation p=0.93, F(2,38)=0.07; RS No Reward: birth × modulation p=0.7, F=0.36; Fig. 3f: 2-tailed t-test, p=0.9, t=0.0046, df=19; Fig. 3g: FS Reward: birth × modulation p=0.95, F=0.09; FS No Reward: birth × modulation p=0.24, F=1.46; Fig. 3h: p=0.66, t=0.45). However, when we compared the peristimulus time histograms (PSTHs) of neurons whose firing was significantly modulated by task cues (% of all RS neurons: Term R = 54.2, Preterm R = 59.59, Term NR = 42.05, Preterm NR = 44.89; % of all FS neurons: Term R = 50.67, Preterm R = 78.14, Term NR = 36.7, Preterm NR = 54.96), we found starkly elevated firing in preterm mice [Fig. 3i–l; R and NR: p=0.000, permutation test of PSTH area under the curve (AUC)].

To test the magnitude and the direction of neuronal responses to task cues, we measured selectivity index (SI) as (Rcue - Rbaseline)/(Rcue + Rbaseline), where Rcue is the firing rate during cue presentation. SI ranges from -1 to 1, where the indices of suppressed neurons fall below 0, and those of excited neurons above it. As the distribution of SI values in a population of neurons can provide more information about cue-evoked activity than population averages, we compared the cumulative distributions of SI values from neurons significantly modulated by cues in term and preterm mice. Despite increased firing, RS neurons in preterm V1 had reduced selectivity for task cues, evident in the distribution of selectivity indices (SI) shifted towards lower values (Fig. 3m; SI median ± MAD Term R = 0.34 ± 0.12, Preterm R = 0.19 ± 0.09, K-S test: D=0.35, p=0.0007; Term NR = 0.21 ± 0.1, Preterm NR = 0.08 ± 0.04, K-S test, D=0.46, p=0.00005). On the other hand, a large fraction of FS neurons in preterm mice had high SI values, with no changes in the selectivity for the non-rewarded cue (Fig. 3n; Term R = 0.36 ± 0.13, Preterm R = 0.43 ± 0.23, K-S test: D=0.29, p=0.000006; Term NR = 0.17 ± 0.1, Preterm NR = 0.13 ± 0.09, D=0.11, p=0.36).

As locomotion can increase V1 firing rates, we measured the amount of movement on the treadmill during the task (Supplementary Fig. 4A, n=8 term and 5 preterm mice). Locomotion during the task was very variable in both groups of mice and overall not statistically different (p=0.37, permutation test of AUC). Elevated firing in the V1 was task-specific, as PTSHs revealed similar levels of activity in both groups of mice outside of the task context (cues presented in the absence of the reward delivery spout, Supplementary Fig. 4B, compare with Fig. 3g). Further, licking activity in preterm mice was slightly elevated (Supplementary Fig. 4B; # of licks per bout Term R = 23.65 ± 2.23, Preterm R = 28.48 ± 1.49, Term NR = 8.37 ± 1.28, Preterm NR = 13.31 ± 3.056; birth × cue p=0.97, F(1,22)=0.00065; birth p=0.024, F=5.85; cue p<0.0001, F=46.26), but the level of cue-evoked sustained activity in the V1 [mean sustained response (0.1-1.5s)/mean transient response (0-0.1s)] was not significantly correlated with lick frequency in either group of mice (Supplementary Fig. 4E; Pearson’s correlation, Term RS p=0.97, Term FS p=0.22, Preterm RS p=0.28, Preterm FS p=0.08).

Neurons in the V1 display activity related to behavioral outcomes. While the behavioral relevance of this activity is unclear, we checked if this activity can be detected in the V1 of preterm mice. Trained mice typically had a 100% hit rate during the last session (Supplementary Fig. 1), so we compared the responses of RS and FS neurons during the Correct Rejections (CR) and False Alarms (FA) between term and preterm mice. RS neurons represented outcomes to a similar degree in term and preterm mice (Supplementary Fig. 5A; ANOVA cue × modulation: Correct Rejection p=0.24, F(2,38)=1.47; False Alarms p=0.7, F=0.36), despite elevated neuronal activity in preterm mice and weakened SIs for the non-rewarded cue during both outcomes (Supplementary Fig. 5B and C; p=0.000, permutation test of PSTH AUC; SI Term CR = 0.2 ± 0.09, Preterm CR = 0.09 ± 0.06; K-S test, D=0.48, p=0.0002; Term FA = 0.29 ± 0.09, Preterm FA = 0.09 ± 0.25; D=0.37, p=0.02). While FS neurons of term and preterm mice represented the behavioral outcomes to a comparable degree (Supplementary Fig. 5D; ANOVA cue × modulation: Correct Rejection p=0.36, F=1.03; False Alarms p=0.75, F=0.28), neuronal activity was elevated in preterm mice and the cue-evoked peak in activity was barely detectable during the False Alarms (Supplementary Fig. 5E and F; p=0.000, permutation test of PSTH AUC). However, selectivity for the non-rewarded cue in FS neurons displayed a significant shift towards lower values once it was segregated into behavioral outcomes; SI Term CR = 0.26 ± 0.12, Preterm CR = 0.17 ± 0.11; K-S test, D=0.23, p=0.009; Term FA = 0.36 ± 0.18, Preterm FA = 0.23 ± 0.32; D=0.31, p=0.001).

Altogether, our data demonstrates that preterm mice have an intact representation of task cues and behavioral outcomes in the V1, despite significantly elevated neuronal activity and overall weakened selectivity for the task cues. The increase in neuronal activity in preterm mice appeared to be specific to task context, suggesting the involvement of top-down modulation. Therefore, we next examined the neuronal responses to task cues in the term and preterm prefrontal cortex (PFC), a top-down area that sends major glutamatergic monosynaptic input to the V1 and modulates its activity during discrimination tasks.

Representation of task cues is altered in the PFC of preterm mice

Within the PFC, the anterior cingulate cortex (ACC) sends and receives dense glutamatergic projections to and from the V1. Neurons in the ACC are not responsive to sensory cues in naive mice, but develop robust context-specific responses to cues during training in sensory discrimination tasks. Further, optogenetic activation of ACC neurons that project to the V1 (ACC→V1) evokes a robust increase in V1 firing rates and promotes behavioral performance in visual tasks, while inactivating ACC impairs visual discrimination (Supplementary Fig. 6), suggesting that ACC exerts top-down modulation of cue processing in the V1 of trained mice. We therefore hypothesized that elevated activity and altered cue selectivity in preterm V1 during task performance reflects the activity and selectivity in their PFC. To test this, we recorded the activity of the PFC while the mice were engaged in the task (Fig. 4a, b) and sorted the isolated neurons by their waveform width as in the V1. While most of the units recorded were from deep cortical layers (5 and 6), our recordings collected units from layer 2/3 as well (Fig. 4b). As expected, putative FS interneurons had higher pre-cue firing rates overall in both groups of mice (median ± MAD FS: Term = 8.61 ± 6.97, Preterm = 7.57 ± 5.75 spikes/s 143 and 183 units respectively from 10 and 9 preterm mice; RS: Term = 2.7 ± 1.08, Preterm = 2.46 ± 1.2 spikes/s, 70 and 170 units respectively), but pre-cue firing rates between term and preterm mice were comparable (Mann-Whitney t-test, FS: p=0.6, U=43; RS: p=0.96, U=25). Similar to V1, the firing of PFC neurons preceded the licks, with the event correlogram revealing peaks at 14 and 65 ms prior to lick events, as well as a significantly higher number of spiking events prior to lick events (Fig. 4c; Wilcoxon matched-pairs signed rank test, p<0.0001, W=-2770).

We next evaluated cue representation and found that the non-rewarded cue had a significantly weaker representation in RS neurons of preterm mice, shifting the overall representation in favor of the rewarded cue (Fig. 4d; ANOVA Reward: birth × modulation, p=0.6, F(2,34)=0.52; No Reward: p=0.014, F=4.88; Fig. 4e: Mann-Whitney test p=0.045, U=20.5). Significantly modulated neurons in both groups of mice had an overall higher firing rate than the entire recorded population (median ± MAD; precue firing Term = 4.3 ± 2.84, Preterm = 3.27 ± 1.73 spikes/s; % of all neurons Term R = 78.7, Term NR = 52.9, Preterm R = 88.1, Preterm NR = 23.01), and their PSTHs revealed a significantly increased pre- and postcue firing rate in preterm mice (Fig. 4f, g; p=0.000, permutation test of PSTH AUC). Additionally, the PSTH to the non-rewarded cue in preterm mice had a very irregular shape with no clear peak, suggesting weakened selectivity of prefrontal RS neurons for the non-rewarded cue in preterm mice (Fig. 4g). Indeed, while the SI values for the rewarded cue were not different between term and preterm mice (Fig. 4h: median ± MAD, Term R = 0.72 ± 0.13, Preterm R = 0.71 ± 0.1; K-S test, D=0.13, p=0.36), the SI values for the non-rewarded cue were significantly lower in preterm mice (Fig. 4h: Term NR = 0.33 ± 0.09, Preterm NR = 0.16 ± 0.07; K-S test, D=0.4, p=0.000014).

Previous work demonstrated a strong correlation between prefrontal responsivity to task cues during training and the progressive increase in performance. As the SI reflects the responsiveness of neurons to task cues, we tested the correlation between the SI values (averaged per animal) and d’ values during the recording session, as well as the strength of the SI values in predicting behavioral performance using linear regression when a significant correlation was detected. In term mice, SI values for the rewarded and non-rewarded cues were not significantly correlated with d’ values (Pearson’s correlation, Fig. 4i). In contrast, SI values for the rewarded cue were significantly negatively correlated with d’ in preterm mice (r=-0.75, p=0.02) and were a predictor of performance (Fig. 4j and Supplementary Table 4). The correlation between SI values for the non-rewarded cue and d’ in preterm mice was not significant (Fig. 4J).

Our bulk recordings did not isolate the activity of PFC neurons that project to the V1, making it difficult to draw connections between the activity patterns in the V1 and PFC. To do that, we used an intersectional viral approach to optogenetically label the V1-projecting PFC neurons, where we injected the retrograde AAV2 variant encoding the Cre-recombinase into the V1 and DIO-ChR2-mCherry into the PFC (Supplementary Fig. 7A and B). We used 10 ms pulses at 473 nm after the behavioral sessions ended to elicit the ChR2-mediated responses and isolate the activity of PFC→V1 neurons (short latency responses with >50% reliability, Supplementary Fig. 7C). We found a significantly increased representation of the rewarded cue in PFC→V1 neurons of preterm mice (Supplementary Fig. 7D; 28 neurons from n=9 term mice, 35 neurons from n=6 preterm mice; ANOVA; Reward: birth × modulation, p=0.0017, F(2,26)=8.27, No Reward: birth × modulation, p=0.38, F=0.99). PSTHs of neurons significantly modulated by the cue (% of all neurons: Term R = 66.67, Preterm R = 98.33, Term NR = 55.21, Preterm NR = 58.45) revealed elevated activity of PFC→V1 neurons in preterm mice after the presentation of the rewarded cue, especially in the late stages of their response (Supplementary Fig. 7F; p=0.000, permutation test of AUCs). SI values for the rewarded cue were also substantially higher in preterm than in term mice (median ± MAD, Term R = 0.31 ± 0.31, Preterm R = 0.57 ± 0.09; K-S test, D=0.79, p=3.15 × 10-6). PSTHs of PFC→V1 neuron responses to the non-rewarded cue revealed irregular firing in preterm mice, in line with weakened activity evoked by the non-rewarded cue in V1 and PFC (Supplementary Fig. 7G; p=0.000, permutation test of AUCs). The SI values for the non-rewarded cue were also elevated in preterm mice (Term NR = 0.11 ± 0.19, Preterm NR = 0.2 ± 0.07), but the differences in their distribution between term and preterm mice were marginally significant (K-S test, D=0.41, p=0.06), likely due to the low number of significantly modulated neurons (Term=16 and Preterm=23).

The representation of cues in FS neurons was similar to that of RS neurons, although the difference in the representation of the non-rewarded cue was not statistically significant (Fig. 4k; ANOVA Reward: birth × modulation, p=0.71, F(2,34)=0.34; No Reward: p=0.2, F=1.67). As in RS neurons, the representation of cues was slightly shifted in favor of the rewarded cue (Fig. 4l), but it was not statistically different between term and preterm mice (Mann-Whitney test p=0.17, U=28). However, when we compared the PSTHs of neurons significantly modulated by task cues between term and preterm mice (% of all neurons Term R = 86.42, Term NR = 56.37, Preterm R = 90.8, Preterm NR = 37.6), we found that the responses to both cues in preterm mice were significantly blunted (Fig. 4m, n; p=0.000, permutation test of PSTH AUC). Similar to V1 FS neurons, the median SI of prefrontal FS neurons for the rewarded cue in preterm mice was marginally larger than in term mice, while the SI for the non-rewarded cue was lower (Fig. 4o; median ± MAD SI: Term R = 0.52 ± 0.17, Preterm R = 0.58 ± 0.11, K-S test: D=0.19, p=0.02; Term NR = 0.21 ± 0.06, Preterm NR = 0.092 ± 0.07, K-S test, D=0.36, p=0.00097). None of the SI values in either group of mice correlated with d’ (Fig. 4p, q; Pearson’s correlation).

FS interneurons can remain undetected during spike sorting of bulk electrophysiological recordings due to their low spontaneous firing rates. To ensure that our spike sorting approach reliably captured the weaker responsivity to task cues in preterm mice, we used a transcriptional enhancer S5E2 to transduce FS interneurons with ChR2 for optogenetic identification (“optotagging”, Supplementary Fig. 8). S5E2-mCherry only partially colocalized with Parvalbumin (PV) signal in the ACC, which was significantly weaker compared to other cortical areas (Supplementary Fig. 8B), as previously reported. As expected, 10 ms pulses of blue light evoked strong rhythmic firing in transduced neurons (Supplementary Fig. 8C), with all isolated neurons having a narrow waveform (average spike half-width: 0.33 ± 3.2 × 10-6 ms, n=50 units from 5 term mice and 52 units from 4 preterm mice). Indeed, the pre-cue firing rate of optotagged FS units was significantly lower than that of FS units isolated through spike sorting of bulk electrophysiological data (Fig. 4b and c; median ± MAD, N=units; Term/Preterm narrow-spiking units = 5.9 ± 4.79 spikes/s vs Term/Preterm optotagged S5E2+ units = 1.45 ± 1.45 spikes/s; Mann-Whitney t-test, p<0.0001, U=11297), with no significant differences between term and preterm optotagged FS units (Term = 0.63 ± 0.63 spikes/s, Preterm = 1.69 ± 1.52; Mann-Whitney t-test, p=0.18, U=1104). While the fractions of neurons significantly modulated by task cues were overall lower in the optotagged population (Supplementary Fig. 8D; compare with 4K), they were not significantly different between term and preterm mice (ANOVA Reward: birth × modulation: p=0.91, F(2,14)=0.094; No Reward: p=0.99, F=0.014). PSTHs of significantly modulated optotagged neurons (% of all neurons: Term R = 61.56, Preterm R = 71.18, Term NR = 52.66, Preterm NR = 53.26) revealed their weak responsivity to task cues in preterm mice (p=0.000, permutation test of PSTH AUC), similar to what we found in FS neurons isolated through bulk recordings (Supplementary Fig. 8E and F; compare with Fig. 4m and n). However, the selectivity for the rewarded cue was significantly weakened in preterm mice, while the selectivity for the non-rewarded cue appeared comparable between term and preterm mice (median ± MAD SI: Term R = 0.78 ± 0.22, Preterm R = 0.38 ± 0.15, K-S test: D=0.79, p=0.000003; Term NR = 0.31 ± 0.06, Preterm NR = 0.35 ± 0.13, K-S test: D=0.41, p=0.057).

Our results hence demonstrate a significant shift in the representation of task cues towards the rewarded one in putative principal neurons of preterm PFC, along with pronounced disinhibition of their responses to task cues. This shift comes at the expense of the non-rewarded cue, which elicits blunted responses in both putative pyramidal neurons and interneurons. The selectivity of the population responses to the rewarded cue was predictive of performance in preterm, but not in term mice. As the preterm mice that underwent electrophysiological recordings had comparable d’ values to term mice (Fig. 4i and j; mean ± SEM Term = 2.56 ± 0.16, Preterm = 2.83 ± 0.13, n=9 mice/group), our results indicate divergent processing of task cues in preterm mice and further suggest that preterm mice use different strategies to encode behavioral outcomes. To test this further, we next compared the representation of behavioral outcomes to the non-rewarded cue in prefrontal RS and FS neurons of term and preterm mice and modeled their ability to classify trial-by-trial behavioral outcomes.

Cue-evoked activity of prefrontal neurons is a weaker predictor of behavioral outcomes in preterm mice

The prefrontal cortex is critical for many forms of goal-directed behavior, and its activity is predictive of behavioral outcomes across different tasks. As mentioned previously, the population selectivity for the rewarded cue was predictive of performance in preterm but not in term mice (Fig. 4), suggesting that preterm mice use a strategy different from term mice to encode task-relevant information. Indeed, both the increase in licks to the rewarded cue and suppression of licks to the non-rewarded cue drive the increase in performance in preterm mice, while the suppression of behavioral responses to the non-rewarded cue drives the performance increase in term mice (Fig. 2 and Supplementary Fig. 1). We therefore hypothesized that the PFC of term and preterm mice uses divergent strategies to encode behavioral responses to the non-rewarded cue, as preterm mice show increased False Alarm rates (Fig. 2h). As almost 100% of behavioral responses to the rewarded cue in trained mice from both groups are Hits (Fig. 2 and Supplementary Fig. 1), we focused our analysis on neuronal responses to the non-rewarded cue, i.e. Correct Rejections (CRs) and False Alarms (FAs; Fig. 2C).

We first tested the representation of behavioral outcomes in prefrontal neurons and found no differences between term and preterm mice in either regular spiking (Fig. 5a; ANOVA: birth × modulation CR: p=0.81, F(2,34)=0.21, FA: p=0.6, F=0.51) or fast-spiking neurons (Fig. 5b; CR: p=0.65, F=0.43, FA: p=0.45, F=0.82). PSTHs of significantly modulated RS neurons (% of all neurons Term CR = 33.5, Term FA = 41.42, Preterm CR = 35, Preterm FA = 49.03) revealed elevated firing and irregular cue-evoked activity in preterm mice during both outcomes (Fig. 5c; p=0.000, permutation test of AUCs). While cue-evoked activity in preterm FS neurons also appeared irregular, analysis revealed that term and preterm FS responses to the non-rewarded cue are comparable during CR but not during FA trials (Fig. 5D, CR p=0.125, FA p=0.022, permutation test of AUCs; % of all neurons Term CR = 48.53, Term FA = 53.34, Preterm CR = 46.34, Preterm FA = 56.94). Selectivity indices were shifted towards lower values in both cell populations of preterm mice during both behavioral outcomes (Fig. 5e; median ± MAD, K-S test for all; RS CR Term = 0.49 ± 0.21, Preterm = 0.13 ± 0.09, D=0.44, p=0.000042; RS FA Term = 0.4 ± 0.16, Preterm = 0.29 ± 0.18, D=0.26, p=0.014; FS CR Term = 0.14 ± 0.07, Preterm = 0.09 ± 0.06, D=0.24, p=0.044; FA Term = 0.37 ± 0.11, Preterm = 0.08 ± 0.19, D=0.57, p=1.2 × 10-10). Further, FS neurons in term mice showed higher SI values during FA trials, while the same was true for RS neurons in preterm mice (Mann-Whitney test, Term FA vs CR: RS p=0.82, U=150, FS p<0.0001, U=526.5; Preterm FA vs CR: RS p<0.0001, U=1593, FS p=0.7, U=2517).

To test if neuronal responses can predict behavioral outcomes on a single-trial basis, we used logistic regression with CR and FA as binary outcomes and cue-evoked (baseline-subtracted) spike rates (Fig. 5f, g) as classifiers. We included all isolated neurons in the analysis as the number of neurons significantly modulated by the non-rewarded cue was low for some mice. Cue evoked spike rate distributions of all isolated neurons mirrored the trends seen in SI values of neurons significantly modulated by task cues, with FS and RS neurons in term and preterm mice, respectively, preferentially responding to the non-rewarded cue during the FA trials and weaker cue-evoked firing in preterm FS neurons during both trial outcomes (median ± MAD spikes/s, RS: Term CR = 1.5 ± 1.43, Preterm CR = 0.65 ± 1, K-S test p=0.011, D=0.23; Term FA = 2 ± 1.15, Preterm FA = 2.3 ± 3.31, p=0.0008, D=0.28; FS: Term CR = 1.28 ± 1.27, Preterm CR = 0.34 ± 0.89, p=0.0001, D=0.26; Term FA = 4.41 ± 4.29, Preterm FA = 1.25 ± 1.86, p=0.93 × 10-6, D=0.31). We constructed Receiver Operating Characteristic (ROC) curves for each group of mice and compared their performance first to shuffled outcomes from each group, and then to each other (Fig. 5h). We found that neuronal responses to the non-rewarded cue in both groups of mice classified trial outcomes significantly better than shuffled data (au(ROC) ± SE: Term = 0.703 ± 0.0058, 7928 trials; Preterm = 0.65 ± 0.005, 11164 trials; Shuffled term = 0.5 ± 0.006, Shuffled preterm = 0.5 ± 0.005; Term vs Shuffled term, z=23.26, p<0.0001, Preterm vs shuffled preterm, z=19.93, p<0.0001). However, firing rates of term mice performed significantly better as classifiers than those of preterm mice (z=6.79, p<0.0001). ROC values for each mouse did not correlate with the fraction of significantly modulated neurons nor with their SI values (Pearson’s r=-0.24, p=0.42 and r=0.25, p=0.4, respectively), indicating that including all recorded neurons in the model did not impair the classification.

Our results hence provide further evidence for weak selectivity of prefrontal neurons for the non-rewarded cue in preterm mice, indicating divergent processing of task cues and encoding of behavioral outcomes in preterm PFC. Preterm birth is a significant risk for delays in cognitive development, especially in the development of response inhibition, suggesting that the impairments in the processing and representation of task cues in preterm PFC reflect a maturational deficit. As prior research found weak representation of the non-rewarded cue in the brains of adolescent mice trained on an auditory task, we next tested if the representation and processing of cues in our task is similar between adult preterm mice and adolescent term mice.

Similar encoding of behavioral outcomes to the non-rewarded cue in adolescent term and adult preterm mice

To test for impaired maturation of preterm PFC, we examined the representation and processing of task cues in adolescent, term-born male and female mice and compared it to adult preterm mice (Supplementary Fig. 9). To prepare the adolescent mice for the task, they were implanted with a headpost between postnatal days 21 and 24 and begun habituation and water restriction 4 days after the implantation (Fig. 6a). The training started between postnatal days 32 and 35 and lasted until mice crossed the criterion (all mice learned the task, n=18). Adolescent mice performed comparable to term mice (6B, LMM with Age as Fixed Factor and Animal and Training Day as Random Factors, p=0.42, Supplementary Table 5), and showed a significant increase reduction in FA rate between naive and trained stages (Fig. 6c; RM ANOVA: response × training p<0.0001, F(1,17)=40.5; HR p=0.07, FA p<0.0001), indicating that a decrease in response inhibition drives behavioral performance in our task regardless of age tested.

We next recorded the activity of prefrontal neurons in adolescent mice during task performance (n=324 neurons from 9 mice), which we classified into regular and fast-spiking neurons as in Figs. 3 and 4 (Fig. 6d and e). As previously reported, the representation of the non-rewarded cue was significantly weaker than that of the rewarded cue (RM ANOVA cue × modulation Fig. 6e: p<0.0001, F(2.2,17.79)=37.11; Fig. 6f: p<0.0001, F(2.5,20.04)=18.37). Similar to preterm mice, the representation of the non-rewarded cue was significantly weaker only in RS neurons when compared to adult term mice (Supplementary Fig. 9). While the rewarded cue elicited substantially higher activity in both RS and FS neurons (Fig. 6g; p=0.000, permutation test of AUCs), the PSTHs of neurons whose activity was significantly modulated by task cues revealed that cue-evoked activity was lower in adolescents than in adult term mice (% of all neurons: RS R = 72.98, NR = 29.42, FS R = 93.11, NR = 35; Supplementary Fig. 9). Preference for the rewarded cue was also evident in SI values, with higher selectivity for the rewarded cue in both RS and FS of adolescent mice (Fig. 6h; median ± MAD RS R = 0.4 ± 0.16, NR = 0.25 ± 0.16; K-S test, D=0.31, p=0.005; FS R = 0.41 ± 0.12, NR = 0.26 ± 0.18; D=0.27, p=0.01).

In terms of behavioral outcomes to the non-rewarded cue, both CR and FA were equally represented in the adolescent PFC (RM ANOVA outcome × modulation Fig. 6e: p=0.17, F(2.16)=1.96; Fig. 6f: p=0.95, F=0.04). PSTHs of neurons significantly modulated by the non-rewarded cue revealed slightly higher neuronal responses during FA trials in both neuronal populations (Fig. 6i; p=0.000, permutation test of AUCs; % of all neurons: RS CR = 15.71, FA = 34.03, FS CR = 41.73 FA = 44.75), but their SI values were comparable (Fig. 6j; median ± MAD RS CR = 0.28 ± 0.16, FA = 0.3 ± 0.22; K-S test, D=0.21, p=0.47; FS CR = 0.25 ± 0.16, FA = 0.29 ± 0.21; D=0.12, p=0.89). We next isolated single trial firing for adolescent neurons and tested if their evoked firing can accurately classify the two behavioral outcomes to the non-rewarded cue (Fig. 6k; term and preterm data from Fig. 5m added for comparison). While the logistic model built on adolescent evoked firing rates performed significantly better than the shuffled data (p<0.0001, z=22.92), there was no significant difference between the auROC of preterm mice and that of adolescent mice (adolescent vs preterm p=0.17, z=1.38; adolescent vs term adult p<0.0001, z=-5.76).

Life-long environmental enrichment fails to improve the learning trajectory of preterm mice

Adult preterm mice and adolescent term mice display similarly weak representation of the non-rewarded cue in the PFC (Supplementary Fig. 10), as well as similar encoding of its behavioral outcomes (Fig. 6k), suggesting a maturational delay in the PFC of preterm mice. We therefore tested if environmental enrichment (ENR), a classical paradigm that promotes sensory maturation and rescues cognitive deficits in animal models of prematurity-related brain injury, improves learning in preterm mice. To do this, we housed timed-pregnant dams with their term and preterm litters from postnatal day 5 (P5) to weaning (P28) in standard rat cages supplemented with a running wheel, multiple play and chew objects (such as gnawing sticks and tunnels), as well as a non-related dam with an age-matched litter for social enrichment. The object location was shuffled in the cage every 2-3 days. Prior to P5, pups were housed in a standard cage with enrichment for the dams in the form of igloos, extra nesting material and sunflower seeds. After weaning, term and preterm mice were housed in groups of 2-3 in standard cages supplemented with an igloo, small running platform, and gnawing sticks. When mice reached adulthood (>2 months of age), they were prepared for the visual discrimination task, water-restricted, and trained to criterion or to 4000 trials if they did not pass the criterion (Fig. 7a). While term mice had a steep learning curve, the enrichment had a negative impact of their performance, lowering the overall d’ values when compared to term mice that received no enrichment [LMM with Enrichment (ENR) and Birth as Fixed Effects, and Animal and Training Day as Random Effects; ENR p<0.0001, Estimated Marginal Means (EMM) Term ENR = 1.6 ± 0.2, Term = 2.22 ± 0.19; Supplementary Table 6]. However, just like the term mice that received no enrichment, all ENR mice learned the task within 15 days (Fig. 7b). In contrast, preterm mice that received ENR had a substantially impaired learning curve (LMM: Birth p<0.0001) and performed worse than preterm mice that received no enrichment (EMM Preterm ENR = 1.003 ± 0.68, Preterm = 1.6 ± 0.14). While a significant fraction of ENR preterm mice failed to learn the task within 4000 trials (27.27%), the distribution of final d’ values was not significantly different between ENR term and preterm mice (Fig. 7c; K-S test, D=0.46, p=0.16).

When we analyzed Hit Rates and False Alarms in ENR mice and compared them to standardly reared term and preterm mice (STD; Fig. 7d and e, compare with Fig. 2g and h), we found a significant interaction between training, enrichment, and birth timing (RM ANOVA, p=0.002, F(1,67)=10.75, Supplementary Table 7). Post-hoc tests revealed no significant differences between naive ENR term and preterm mice in Hit Rates and a significant reduction in trained preterm mice (Holm post hoc naive ENR Term vs Preterm p=0.19, trained p=0.006; Fig. 7d). Both groups of ENR mice had significantly reduced HR compared to STD mice at the onset of training, and this trend persisted in preterm mice only (Supplementary Table 7). There were no significant differences in FA rates between term and preterm mice raised in ENR (Fig. 7e; naive ENR Term vs Preterm p=0.99, trained p=0.99; Supplementary Table 7), and trained ENR preterm mice had comparable FA rates to STD trained term mice (p=0.99).

Methods
Mice
Mice were maintained on C57BL/6 background (The Jackson Laboratory, Bar Harbor, ME) on reverse 12:12 light:dark cycle (lights off 11 AM-11 PM), with food and water ad libitum, except during visual discrimination training (see below). Animals of both sexes were used between postnatal day 21 and 7 months of age. Preterm mice were generated through timed breedings, where the day after the pairing was considered as gestational day (GD) 0. Once the pregnancy was confirmed (>1.5g increase in weight at GD10), pregnant dams were habituated to handlers by daily handling sessions. Mifepristone (MFP, MiliporeSigma, Burlington, MA or Hello Bio, Bristol, UK) was dissolved in DMSO (MiliporeSigma) and 150µg was injected subcutaneously on GD17. Preterm mice were delivered on GD18. The cage with preterm mice was occasionally supplemented with external heat and oxygen to prevent hypothermia and hypoxia, commonly observed in preterm mice. Control term mice were obtained from timed pregnant dams injected with DMSO on GD17, and with MFP on GD18. All dams received continuous enrichment in form of plastic igloos and nesting material, as well as sunflower seeds (Bio-Serv, Flemington, NJ) beginning 3-4 days before parturition. Animals were treated in accordance with the University of Virginia Institutional Animal Care and Use Committee guidelines.

Environmental enrichment
Nursing dams with their litter were transferred to a standard rat cage when the pups reached postnatal day (P) 5. Dams were co-housed with a nursing dam and its litter that was age matched and on BALB/c strain (purchased from Jackson Laboratory) to facilitate social enrichment. Cages contained multiple enrichment objects: InnoDome and Inno-Wheels from BioServ, gnawing sticks, multiple nesting pads, shredded paper and hay for building nests, as well as a variety of chew toys (pumice chews, willow twigs, willow balls, veggie chews, and hay tunnels, hideaways and sticks). Large enrichment objects were rearranged in the cage every 2 days, while chew toys were replaced with new ones every 2-3 days. Once the litters reached P25, they were weaned and group-housed in standard cages at 2-3 mice/cage to allow for placement of an InnoDome and InnoWheel, as well as 2-3 small chew toys. When the mice reached P60, they were prepared for visual discrimination task as described below.

Viruses
Following viruses were purchased from Addgene: AAV-S5E2-ChR2-mCherry (135634-AAV1; 2×1013 vg/ml), AAV-EF1a-double floxed-hChR2(H134R)-mCherry-WPRE-HGHpA (20297-AAV9; 3.2x1013 vg/ml) and AAV-hSyn-HI-eGFP-Cre-WPRE-SV40 (105540-AAVrg; 1.7x1013 vg/ml). All viruses were diluted 1:10 in sterile PBS before the injection.

Surgeries
Animals were anesthetized with isoflurane in oxygen (2-2.5% induction, 1–1.5% maintenance), warmed with a heating pad at 38°C and given subcutaneous injections of Buprenorphine SR (1mg/kg) or Rimadyl (5mg/kg) and 0.25% Bupivacaine (beneath their scalp). Eyes were covered with Puralube (Decra, Northwich, UK). Scalp and fascia from Bregma to behind lambda were removed, and the skull was cleaned, dried and covered with a thin layer of Scotchbond adhesive (3M, Maplewood, MN). Skin edges were sealed with VetBond (3M).

For virus injections, the head was immobilized in a custom-made stereotactic apparatus and small craniotomies were made on the left hemisphere with a dental drill at the following coordinates: primary visual cortex (V1): 0.5 mm anterior to lambda, 2-3 mm lateral to midline; anterior cingulate cortex (ACC): 2-2.5 mm anterior to bregma, 0.2-0.5 mm lateral to midline. NanoFil syringe (35G beveled needle; WPI, Sarasota, FL) was then used to inject the virus at 1 nl/s using a Syringe Pump (KD Scientific, Holliston, MA). Volume of injected virus was 300 nl, injected in 2 150 nl increments at 2 different depths: 500 and 250 µm beneath the brain surface for V1 (targeting layers 2/3 and 5), and 700 and 800 µm for the PFC. The syringe was left in place for 5-10 minutes after the injection to allow the virus solution to diffuse and then very slowly raised to the surface to prevent the backflow. Muscimol (TMR-X conjugate; HelloBio, Princeton, NJ) was injected in the same manner, volume and at the same PFC coordinates of trained term mice at 1.6 mM concentration.

Headplates were attached after the Scotchbond adhesive application for animals that received no virus injection, or immediately after the virus injection for those that did. The headplate (stainless steel, SendCutSend, Reno, NV) was attached with dental cement (RelyX Ultimate, 3M). After the cement was cured, the well of the headplate was filled with silicone elastomer (Reynold Advanced Materials, Brighton, MA) to protect the skull.

If the mice received Rimadyl for analgesia, they were given Rimadyl dissolved in hydrogel (1% food-grade agar in distilled water) during the recovery. Animals were group-housed after the implantation and monitored daily for signs of shock or infection. On the day of electrophysiological recording, the animals were anesthetized as above and craniotomies (~0.5 mm in diameter) were made above V1, PFC and cerebellum with 18G needles. If mice were injected with viruses, the existing craniotomies were reopened. The brain surface was covered in 2-3% low melting point agarose (Promega, Madison, WI) in sterile saline and then capped with silicone elastomer. Animals were allowed to recover for 1.5-2 h before the recording.

Electrophysiology and optogenetics
For the recording sessions, mice were placed in the head-plate holder above an aluminum mesh treadmill and allowed to habituate for 5-10 minutes. The silicone plug was removed, the reference (insulated silver wire electrode; A-M Systems, Carlsborg, WA) was placed in cerebellum and the well was covered with warm sterile saline. A multisite electrode spanning all cortical layers (A1x16-5mm-50-177-A16; Neuronexus Technologies, Ann Arbor, MI) was coated with DiI (Invitrogen) to allow post hoc insertion site verification and then inserted in the V1 through the craniotomy. For PFC, a 4x4 shank probe (A4x4-3mm-50-125-177-A16) was used for the recordings, coated with DiI, DiO or DiD based on the type of fluorophore used in the experiment (mCherry or EGFP; listed in Viruses). The electrodes were slowly (5-10 µm/s) lowered to the appropriate depth: 800 µm for the PFC and until the uppermost recording site had entered the brain for the V1 and allowed to settle for 15-30 minutes. For optogenetics, a 473 nm laser diode with 0.4 µm fiber tip (0.22 NA, Doric Lenses, Quebec, Canada) was lowered to the craniotomy. 10 ms square pulses were delivered at 3-5 mW/mm2 (200 pulses at 0.5 Hz) using Spike2 (CED, Cambridge, UK) after the last recording session for optogenetic identification of neuronal subtypes (optotagging). The well was filled with 3% agarose prior to the recordings to stabilize the electrode and the whole region was kept moist with surgical gelfoam soaked in sterile saline (Pfizer, MA). The signals from the recording probes were fed into a 16-channel amplifier (Model 3500; A-M Systems, Sequim, WA) and amplified 200x, before being sampled at 25 kHz using Spike2 and Power 1401-3 data acquisition unit (CED). After the recording ended, the electrodes were slowly retracted, and the well was cleaned and protected with silicone elastomer.

Visual stimuli
For visual stimuli, blank screen was generated with MATLAB (MathWorks, Natick, MA) using the PsychToolBox extension (Brainard, 1997) and presented on a gamma corrected 27” LCD. The screen was centered 20-25 cm from the mouse’s right eye, covering ~80° of visual space. For calculating baseline orientation selectivity, sinusoidal gratings at 6 orientations 30° apart were presented at 100% contrast at 0.15 cpd, with 1 s long stimuli and 1 s interstimulus interval (blank screen). For visual discrimination training, visual stimuli (120° and 60° gratings) were presented at 0.15 cpd and 100% contrast in a random sequence for 3 seconds, followed by a 15 second long interstimulus interval (blank screen). Presentation of 120° triggered the delivery of 10µl of water through a syringe pump (New Era Pump Systems, Farmingdale, NY), while the presentation of 60° had no consequence.

Behavior
4-7 days after the headpost implantation, mice were gradually water-restricted (from 3 ml of water/day to 1 ml of water/day) for 5-7 days. Food was available ad libitum. Mice were weighed daily and their weight was maintained at 85% of initial weight to prevent dehydration. During water restriction, mice were gradually habituated to handling by the experimenters, the treadmill, and the water delivery spout during daily habituation sessions. After water restriction, the visual discrimination training began, where mice were head fixed above the treadmill and a stainless-steel gavage needle (18G) was positioned near the mouse’s mouth for water delivery. The pump was controlled by the Power 1401-3 data acquisition interface (CED) for lick detection. Mice were trained in 2 daily sessions consisting of 50 120° and 60° presentations each, for a total of 200 trials per day. Licks of the spout during the 10 s after the onset of stimulus presentation were quantified according to the signal detection theory as Hits (the fraction of trials in which the mouse licked to the 120° orientation), Misses (the fraction of trials in which the mouse failed to lick to the 120° orientation), False Alarms (the fraction of trials in which the mouse licked to the 60° orientation) and Correct Rejections (the fraction of trials in which the mouse failed to lick to the 60° orientation), where the performance was measured as discriminability (d’) = z(H)-z(FA). Mice were trained until they achieved a d’ of 2 or above for 3 consecutive sessions or up to 4000 trials if they failed to reach the criterion.

For open field, mice were placed in a 24x24 inch plexiglass arena after a brief habituation to the room and the experimenters. Their activity was recorded for 10 minutes and analyzed using an open-source toolbox.

For measuring locomotor behavior during task performance, a small piezoelectric disc was placed in contact with the treadmill and the signal was digitized using Power 1401-3 and Spike2. Waveform averages in response to both cues were constructed using Spike2 and used to compare the amount of treadmill movement during the training.

Animals were monitored for signs of distress for the duration of the experiment. Animals that developed corneal damage, cataracts, and symptoms that had the potential to impact their behavior or physiology (such as immobility and other signs of pain, sudden weight loss or impeded weight gain) were excluded from the study. All mice except those in the MFP Term and Environmental Enrichment groups underwent electrophysiology either at the beginning or at the end of the training, or both. If the animals were recorded at the onset of training, that was counted as the first training session.

Histology and imaging
After the last training session or electrophysiological recording, mice were anesthetized with a mixture of ketamine and xylazine and transcardially perfused with warm 0.1M phosphate buffer, followed by warm 4% paraformaldehyde (Electron Microscopy Sciences, Hatfield, PA). Brains were postfixed 1 hr at room temperature, followed by overnight fixation at 4°C. Brains were sectioned into 40-80 µm sections using a vibratome and stored in 1x phosphate buffered saline (PBS) and 0.01% sodium azide. For immunohistochemistry, sections were rinsed in PBS, non-specific binding was blocked with 3% normal horse serum (heat inactivated, ThermoFisher, Waltham, MA) and 0.3% Triton-X100 (Sigma-Aldrich) in PBS (sterile filtered). Antibodies were incubated overnight at 4°C. Parvalbumin (anti-goat) was used at 1/200 (Swant, Belinzona, Switzerland) and detected with donkey anti-goat Alexa647 (ThermoFisher). After staining, sections were rinsed in distilled water, mounted on glass slides, briefly dried, and coverslipped with Aquamount (Polysciences, Warrington, PA). Images were acquired using Leica Stellaris 8 at 2048x2048 resolution using 10x HC PLAN FLUOTAR air (NA=0.3 for tiling) or 40x HC PL APO CS2 (NA=1.3) oil immersion objective.

Electrophysiological and behavioral data analysis
Locomotion measured with piezoelectric sensors was analyzed in Spike2 (CED) using waveform averaging and cues as triggers. Spikes were isolated from the recordings using template matching in Spike2 (CED). Briefly, the recordings were band-pass filtered (0.7-7 kHz) and smoothed (0.1 ms). Threshold for detection was set at 3 SDs of the mean baseline (blank screen) and a window of 0.9-1 ms. Isolated units were clustered using waveform properties (amplitude, spike half-width and slope of repolarization), and the clusters were checked manually for quality in addition to confirming there were no spikes during the refractory period (2 ms) using interval histograms. Units with refractory period violations or units with large variations in amplitude (>10%) were excluded from analysis. For orientation selectivity, custom-written scripts in Spike2 were used to plot the baseline-subtracted responses of isolated units to the presented orientations (10 ms bin) and construct a tuning curve, which was then fitted with a Gaussian to determine the preferred (Opref) and orthogonal (Oorth) orientations, and calculate the orientation selectivity index (OSI) as (Rpref - Rorth)/(Rpref + Rorth), where R is the firing rate at preferred and orthogonal orientations. We considered the neurons with at least 3x elevated firing rate to the preferred orientation (averaged over 0.8 s and compared to averaged 0.2 s of baseline) as visually responsive. Tuning width was calculated as half-width at half-maximum of the Opref.

For optotagging, spikes were isolated from optogenetic recording sessions and PSTHs (0.1-1 ms bin) were constructed in response to the laser pulses. Units with reliable responses (>50%) of trials and short latencies (within 10 ms of the laser pulse) were considered optotagged and their waveforms were used to identify their spiking during the task performance. Unit waveforms were further compared between optotagging and task recordings for quality assurance. Units whose waveforms differed by more than 10% in amplitude and width between the two sessions were discarded from analysis.

Selectivity indices (SIs) were calculated from 10 ms peristimulus time histogram (PSTHs) as (Rcue - Rbaseline)/(Rcue + Rbaseline), where Rcue is the firing rate during the initial 1-1.5 s presentation of 120° and 60° stimuli. Behavioral responses during the task were analyzed using custom scripts in Spike2 by converting the detected licks into events and constructing PSTHs in response to the non-rewarded cue during the 2 possible outcomes (Correct Rejection and False Alarm).

Stationary and moving stages were not analyzed separately as animals were typically walking in short bouts during the recordings. Recordings where large motion artifacts were present when the animal was simply balancing on the treadmill or grooming were excluded from analysis, along with recordings from locations outside of the PFC or V1 (as determined through post-hoc mapping of insertion sites).

Quantification and statistical analysis
All analyses were performed with the researchers blind to the condition. Statistical analyses were performed in GraphPad Prism 9.0 (GraphPad Inc., La Jolla, USA), R and JASP, as indicated in text and figure legends. N refers to the number of animals or single units, as indicated in figure legends or in text. For orientation selectivity analysis, OSIs, proportions of selective cells (OSI>0.3) and tuning width (OSI>0.3) means (normal data distribution, Shapiro-Wilk test) or medians (non-normal distribution) were calculated per mouse before comparison. For comparison of preferred orientations, selective neurons (OSI>0.3) were only segregated by group (Term/Preterm). For all other analyses, units significantly modulated by cues were determined through comparison of pre-cue and post-cue firing using Wilcoxon signed rank test. The proportions of significantly modulated units (positively and negatively) were determined and reported per animal, and PSTHs and SIs represent significantly modulated units. PSTHs were compared using permutation test of area under the curve (AUC) calculated from 0.5 s precue and 1 s (V1) or 3 s (PFC) postcue activity (10 ms bins). Cue representation was determined as (%R-%NR)/(%R+%NR), where %R and %NR are the proportion of significantly positively modulated units by the rewarded and non-rewarded cue, respectively. All unit data was tested for normality using Shapiro-Wilk test and data violating the assumption of normality was tested using non-parametric tests (Mann-Whitney t-test, Kolmogorov-Smirnoff for comparison of distributions or non-parametric ANOVA). Normally distributed data was tested using t-tests and ANOVA with post-hoc tests for comparing groups as indicated in text and figure legends. Levene’s test was used to test homoscedasticity with appropriate corrections used for heteroscedasticity. For linear mixed model (LMM) in Fig. 2e, an LMM with Animal Group as a fixed effect and Subject and Training Day as random effects was fitted to the data using the following formula and maximum likelihood method: d’ ~ Group + (1 + Group || Subject)+(1 + Group || Training Day). To test the sex effects, LMM with Sex and Animal Group as fixed effects and Subject and Training Day as random effects was fitted using the following formula and maximum likelihood method: d’ ~ Sex + Animal Group+ (1+ Sex + Animal Group | Subject)+(1+Sex+AnimalGroup|TrainingDay). For Fig. 2i, lick counts were averaged per 20 trials and an LMM with Animal Group and Trial Block as fixed effects and Subject as the random effect was fitted to the data using the following formula and maximum likelihood method: Lick count ~ Group + Trial block + (1 + Group + Trial Block | Animal). For Fig. 6b, an LMM with Age as a fixed effect and Subject and Training Day was fitted to the data using the following formula and maximum likelihood method: d’ ~ Age + (1 + Age | Training Day)+(1 + Age | Animal). For Fig. 7b, an LMM with Enrichment and Birth as fixed effects and Subject and Training Day as the random effects was fitted to the data using the following formula and maximum likelihood method: d’ ~ Enrichment + Birth + (1 + Enrichment + Birth | Subject)+(1 + Enrichment + Birth | Training Day). For linear regression, SI values from all neurons per animal were averaged and used as predictors of d’ as described in text and figure legends. For logistic regression, trial-by-trial spike timestamps were used to calculate the evoked rate per trial (number of spikes in a 3 s window post cue - number of spikes in a 3 s window pre cue). As behavioral outcomes were biased towards Correct Rejection, random oversampling was used to balance the datasets. Data are reported as mean ± SEM (standard error of mean) or median ± MAD (median absolute deviation), as indicated in text or figure legends, where N represents the number of animals or the number of single units. Target power for all sample sizes was 0.8 and alpha was set to 0.05.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "In this study, McCoy et al present a significant set of findings documenting the electrophysiological and behavioral consequences of preterm birth in a mouse model. Specifically they find that mice induced to be born 1 day preterm have behavioral inhibition to a go/no-go visual task as adults. This is accompanied by increased firing rates in V1 and ACC and increased firing to go-cue but reduced modulation in ACC to the no-go cue. These results are compared to other data showing similar results in adolescent mice to suggest pre-term mice maintain a persistent ‘adolescent’ mode of processing that is impairing their task performance. This is an important set of physiological findings that leads to an interesting conceptual conclusion. This work is an important advance in our understanding of the circuit and cognitive basis of neural issues linked to preterm birth. Overall the manuscript is very well written, the studies are properly designed, appropriately powered (within the limitations of mouse ephys), and appropriately analyzed.\nI do believe there is one major sticking point that needs to be addressed before this work can be published, as well as a number of major improvements that should be considered to increase the impact of the work.\nCritical point: Firing rates for ostensible single units throughout the study are nearly an order of magnitude higher than other published studies. The resting rates are similar to visually evoked rates observed by multiple other labs. This undermines confidence in the results, which are likely multi-units or severely biased to very high firing rate sub-populations. This discrepancy needs to resolved and integrated into the interpretation of the results.\nMajor points:\n(1) Unless I am mistaken, the correlation of preterm behavior and ephys with ‘adolescent’ coding is based on a single pre-print, which used calcium imaging to an auditory cue. While the similarities are indeed pretty striking, the absence of relevant adolescent data in this paper and the fact that the reference remains a preprint at this point suggests that the title should be changed to better reflect actual behavioral and physiological differences, rather than the larger implication. The authors are strongly encouraged to pursue this correspondence in their discussion of course.\n(2) There is no discussion of the potential depth of the units recorded. It is not required they break the recordings down by depth and layer, but reporting where the majority of units were located is important.\n(3) Percentage of visually responsive units in V1 and firing rate increases to preferred orientation should be presented.\n(4) Why no separation of FS and RS neurons in V1 as for AAC? If there is no compelling reason these should be separated and analyzed as for AAC.\n(5) Why does the analysis switch to z-score normalized firing rates in figure 5? For comparison with previous data absolute firing rates should be used, unless there is a compelling reason.\n(6) lines 241-242 Firing rates are rarely normally distributed, but more likely to be log-normal. Normal descriptive statistics (mean/SEM) are likely inappropriate so should use log transformed or non-normal appropriate (median, percentiles).\nMinor\nWere all animals recorded included in the analysis, or were some excluded for technical reasons?\n236 and Fig 1: 6 Orientations or 12?\n272 “‘errors of omission” is this the same as “Miss”?\nFig 3 Define R and NR, preferably on graph or at least clearly in Legend.", "reviewer_2": "The manuscript by McCoy and colleagues investigates the representation and processing of behaviorally salient sensory stimuli in the V1 and PFC of adult preterm-born mice. Combining behavioral assessment with in vivo electrophysiology, it shows that preterm birth causes major deficits, especially in the function of prefrontal circuits for error monitoring. The topic is timely and the main conclusions, as they are currently listed, of high relevance. I found particularly interesting the limited role of environmental enrichment on the prematurity-induced cognitive dysfunction. The complex experimental design and technically-demanding methodologies are well chosen to address the key questions. However, my overall enthusiasm for the manuscript is dampened by the wealth of imperfections, errors and especially, by the poor analyses and statistics. The last is critical, since the conclusions of the study rely on correct analytical tools. The manuscript requires a comprehensive revision of the analyses and, if necessary, correction of the conclusions, before pursuing with the review.\nLines 192-193: what do the authors mean with “Unit waveforms were further compared between optotagging and task recordings for quality assurance.”?\nLines 194-200: this paragraph is quite unclear. The authors refer to a Rvar, but this variable does not appear in the formula that is given. Also: paired t-test seems like an inappropriate test for a log-distributed variable like firing rate. Lastly, what is the rationale of double-dipping with a t-test first and then then a comparison of z-scored AUCs?\nLines 232-233: could the authors speculate as to what a day of prematurity in mice corresponds in human pregnancies?\nLines 241-243: I do not think that it is appropriate to test differences in firing rates with a t-test. I suggest to log-transform the data if the authors want to use a test that assumes normality. Further, what is a nested t-test? By looking at GraphPad guides, it seems that the test that is used is a linear mixed-effect model, but I am not sure that this is the case. If it is indeed the case, I suggest that the authors use this terminology, which is much more common than “nested t-test”. This part should also be better explained in the Methods section.\nLines 244-248: to what do the degrees of freedom refer to? Did the authors use a nested statistical test also in this analysis?\nLines 262-3: do any of the provided references show that there is a monosynaptic connection from V1 to the ACC?\nLines 264-281: I have many doubts about the statistical analysis:\n- Are the authors running two separate models to compare Term vs MFP Term and Term vs MFP Term vs Preterm? If so, I do not understand the rational for this. I would suggest to only run one model with the three groups, which should also simplify the manner in which statistical results are reported.\n- Why is there only one pvalue when comparing Term, MPF Term and Preterm? Are the authors aggregating the first two groups?\n- Why are the authors reporting R2 when the variable of interest is the slope?\n- Are the numbers used to run the Chi2 test 0/22, 0/19 and 5/29? If so, how can these values lead to the reported p-value? If not, could the authors give the exact values that were used?\nLines 308-311: in previous instances in which data was nested (see lines 241-243), the authors seemingly took this into account when comparing means, but this does not seem to be the case for this analysis. I suggest that the authors re-run the analysis while keeping in consideration the nestedness of their data.\nLines 308-316: why does the number of units change between the two analyses?\nLines 312-316: if the increase in firing rate is context-specific, why is it elevated also before the stimulus is administered?\nFigures 3D-3H: why are the authors binning the MI and then modeling their data with a Chi2 test? I don’t understand the rational for binning. This data would be much more straightforwardly analyzed without binning and with a simple linear-mixed effect model. If the authors even want to consider that the variable can only vary between -1 and 1, they could use a generalized linear mixed-effect model, but this might be an overkill judging by the distribution of the Mis (rather towards 0 than the extrema). If you use a Chi2, you are not testing for differences in means anymore.\nFigure 3E and lines 338-339: as in lines 264-281, I do not understand how such a small % of inhibited neurons can lead to the reported p-values. What data went into this test? Since the effect has to be tested on a mouse level, I also suggest to improve the visualization of the data to understand the consistency of the effect on an individual mouse level (e.g. plot the % of neurons that are activated/inhibited/not affected in individual mice).\nLines 374-375: still linear mixed-effect model? Please provide details\nLines 376-378: why ignore nestedness when this was not the case when comparing firing rates in prior parts (see comments above) of the manuscript?\nLines 379-383: what are the values that went into this Chi2?\nFigures 4F-G-H-I, see previous comments on Chi2 on continuous data and Figure 3E.\nFigures 5C-D-F-G, see previous comments on Chi2 on continuous data and Figure 3E.\nFigures 6E-G-H-I, see previous comments on Chi2 on continuous data and Figure 3E.\nLine 986: there is a typo. It should be “white”, not “while”.", "reviewer_3": "In this study, the authors investigate how preterm birth affects cognitive function and visual information processing by recording neural activity in the V1 and ACC of mice during a Go/No-go task using electrophysiological techniques. They demonstrate that preterm mice exhibit a higher FA rate in the Go/No-go task, despite showing comparable orientation selectivity in V1 neurons to term controls. Furthermore, they show that both V1 and ACC neurons in preterm mice respond differently to visual stimuli during the task. Using optogenetic tagging, they also identify abnormalities in the visual processing of V1-projecting ACC neurons and ACC-FS neurons. The authors conducted experiments using both male and female mice, employed appropriate control groups, and used suitable methods and analyses to address the important question of how preterm birth impacts cognitive function. However, as described more in detail below, there are significant limitations to the interpretation of the data. Notably, the study lacks an investigation of causality, which diminishes its impact.\nMajor comments:\n1) The study does not provide any causal link between neural activity changes of studied circuit (V1 projecting ACC neurons or FS-ACC neurons) and behavioral changes (increased FA) in pre-term mice. A key hypothesis in this manuscript is that overactivation of ACC-FS neurons leads to decreased activity of V1-projecting ACC neurons, resulting in reduced V1 activity in No-go trial and an increased FA rate. However, none of these causal relationships have been demonstrated, leaving the study less mechanistic. Without a demonstration of direct manipulations of neural circuits during behavior impacting behavior, observed neural changes may not be related to behavioral changes, leaving the study descriptive.\n2) It is unclear if the observed neural and behavioral deficits in preterm mice reflect deficits in learning or/and discrimination. Throughout the manuscript, these two concepts are not clearly considered separately. Due to the trajectory difference (especially FA) during training phase, it is highly likely that there is a deficit in learning. Demonstration of neural correlate of learning trajectory would be informative to enhance the mechanistic significance of this study (especially if the study does not provide causal link between circuit activity and behavior).\n3) Given that V1 activity is known to be enhanced by locomotion, extent and timing of locomotion on the treadmill during the task should be analyzed to rule out that neural changes are not confounded by locomotion.\n4) Preterm mice show a lower Hit rate even in the naïve state (Fig 2G). This raises questions about potential motivational deficits, which can be assessed by different tasks such as progressive ratio test.\n5) While the activity of FS and RS neurons is analyzed separately in Fig 4, the activity of V1 neurons in Fig 3 is not divided into these categories. It is unclear why FS and RS neurons were not differentiated for V1 activity, particularly as the distinction between excitatory and inhibitory neurons in V1 could provide critical insights into the observed changes.\n6) Inclusion of “Adolescent-like” in the title misleading. While author cited relevant literatures, they did not examine the neural activity nor behavior using the same task used in this study. Authors should consider conducting experiments in adolescent mice to support the claim, or “Adolescent-like” should be removed from the title, result section, and limited to the discussion section.\nOther comments:\n-In Fig 2C, the Miss marker (water droplet) is present, but the cross marker is missing.\n-How were trained mice defined in Fig 2G? Were only animals that passed the criteria included, or were all animals analyzed at their final session? If the latter, it would be important to compare only those that passed the criteria to see whether FA rate differences remain.\n-In Fig 3G, V1 activity during FA trials does not appear to increase significantly in the Term group. The sustained activity in V1 shown in Fig 3C should be further discussed regarding whether it genuinely reflects premotor activity.\n-The authors claim that elevated firing rates in preterm mice are unlikely due to licking during the task (L312), but Supplementary Fig S3 shows that Preterm mice have significantly more licks per trial than Term mice. Without investigating the correlation between lick count and sustained activity, this conclusion is premature. Correlating lick count with sustained activity and demonstrating a lack of correlation would strengthen this argument.\n-Fig 3D shows a distribution of MI values where some V1 neurons exhibit higher MI and others lower MI in Preterm mice. It is unclear whether the average MI for V1 neurons in Preterm mice is genuinely higher than for neurons in Term mice. Showing vertical lines indicating group means would clarify this point.\n-Are the data in Fig 4E limited to RS neurons? Clearly separating RS and FS neurons and presenting the data would improve clarity.\n-Figure 4K is missing.\n-The statement on L427 (\"stronger positive modulation by the rewarded cue despite no change in the firing rates\") is confusing. Since MI is calculated using firing rates, the claim of \"no change in firing rates\" is misleading. While statistical significance may not have been detected, differences are evident in the traces.\n-The claim in L443 that deficits are \"not as severe as in the V1\" (Fig 3) is unclear. Based on the MI values, changes in V1-projecting ACC neurons appear larger. Moreover, throughout the manuscript, the overlap between the bars of MI histograms for each group makes it difficult to interpret the data. Adjusting the colors or the presentation style of the graphs could improve readability.\n-The degree of overlap between opto-tagged FS neuron data in Fig 6 and FS neuron data classified by spike width in Fig 4 should be presented in the supplementary figure. If discrepancies exist, these should be discussed.\n-In Fig 7B, the authors claim that environmental enrichment (ENR) accelerates learning in the Term group compared to the standardly housed (STD) condition. However, the initial performance on Day 0 appears worse, and there seems to be no difference in final performance or days to reach criterion.\n-Since both male and female data were collected, the authors should explicitly show whether there were any sex differences in the supplementary figures.", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "POINT-BY-POINT RESPONSE TO REVIEWS\nIntroduction and response to general concerns\nWe greatly appreciate the Reviewers’ support, critiques and suggestions, and we think they\nsignificantly improved the manuscript and the quality of our analyses. Before we address the\nconcerns point by point below, we would like to highlight major revisions to our study. Major\ntextual revisions are highlighted in light grey throughout the revised manuscript.\n1. We consulted Dr. Xin Cynthia Tong, a mathematician in our Department, who is an\nexpert in quantitative approaches. She helped reanalyze all our data, which we hope\naddressed concerns about data analysis. New analysis approaches made some of the\ndifferences between experimental groups even more pronounced, especially in the cue-\nevoked firing and the distribution of selectivity indices. While our findings remain the\nsame, i.e. the representation and processing of task cues in preterm mice is altered\ncompared to term mice, some of our conclusions are revised considering the new\nanalyses.\n2. Revised analysis revealed that the N of units significantly responsive to the non-\nrewarded cue when split by behavioral outcomes for optotagging experiments was too\nlow to comfortably draw conclusions. We therefore focused the results derived from\noptotagged neurons solely on general cue representation, and we moved that data to\nthe supplementary material. Of note is that the data for fast-spiking neurons isolated\nthrough bulk electrophysiology and the data for optogenetically identified fast-spiking\nneurons overlaps to a large degree (new Figure 4 and Supplementary Figures 9 and 10).\n3. We separated the data for the visual cortex neurons into regular and fast-spiking\nneurons and to our surprise found that both neuronal populations exhibit similar task-\nrelated hyperactivity. We revised our discussion to incorporate these new findings,\nespecially since the prefrontal cortex shows a different pattern of activity in RS and FS\nneurons during the task.\n4. To give more credence to our previous suggestion that preterm PFC resembles\nadolescent PFC, we trained adolescent term mice in our task, recorded their prefrontal\nactivity and compared the cue representation to that of preterm mice (new Figure 6 and\nSupplementary Figure 11). While our results haven’t fully recapitulated the Nieves and\nListon preprint we cite in the original submission (likely due to methodology differences),\nwe did find remarkable similarities between adolescents and adult term mice in the\nrepresentation and processing of the non-rewarded cue. We discuss these in the revised\nmanuscript, and we think that the adolescent data will be a valuable resource to the field.\n5. In the original manuscript, we use the term “Modulation Index” to determine the degree\nof responsivity of neurons to task cues. We revised this term to “Selectivity Index” as our\nliterature search found that Modulation Index is typically used to denote firing rate\nalterations by behavioral states, such as attention. To emphasize that we measure\nresponsiveness to the sensory cues, we chose to use Selectivity Index instead. While\nthis was not an issue brought up by the reviewers, it was brought up by the\nneuroscience community as the submission was accessible as a preprint.\nPoint-by-point responses are below with the Reviewer comments italicized.\nReviewer #1\nIn this study, McCoy et al present a significant set of findings documenting the\nelectrophysiological and behavioral consequences of preterm birth in a mouse model.\nSpecifically they find that mice induced to be born 1 day preterm have behavioral inhibition to a\ngo/no-go visual task as adults. This is accompanied by increased firing rates in V1 and ACC and\nincreased firing to go-cue but reduced modulation in ACC to the no-go cue. These results are\ncompared to other data showing similar results in adolescent mice to suggest pre-term mice\nmaintain a persistent ‘adolescent’ mode of processing that is impairing their task performance.\nThis is an important set of physiological findings that leads to an interesting conceptual\nconclusion. This work is an important advance in our understanding of the circuit and cognitive\nbasis of neural issues linked to preterm birth. Overall the manuscript is very well written, the\nstudies are properly designed, appropriately powered (within the limitations of mouse ephys),\nand appropriately analyzed.\nI do believe there is one major sticking point that needs to be addressed before this work can be\npublished, as well as a number of major improvements that should be considered to increase\nthe impact of the work.\nCritical point: Firing rates for ostensible single units throughout the study are nearly an order of\nmagnitude higher than other published studies. The resting rates are similar to visually evoked\nrates observed by multiple other labs. This undermines confidence in the results, which are\nlikely multi-units or severely biased to very high firing rate sub-populations. This discrepancy\nneeds to resolved and integrated into the interpretation of the results.\nWe thank the reviewer for bringing this up, as well as for their later comments which\nhelped us resolve this discrepancy. In the original submission, we present our firing rate data\nthroughout the manuscript as mean±SEM. However, and as the reviewer pointed out, the firing\nrate distribution is log-normal and largely reported as median±IQ range/MAD. For example in\nthe V1, Durand et al. report these values: L2/3 0.58 ± 0.11, L4 3.00 ± 1.14, and L5 2.86 ± 0.65\nsp/s, with an average of 1.88 ± 0.25 for spontaneous, outside of any task context, activity in the\nawake V1 of adult mice (Durand et al., 2016). These values are for units unsorted by width into\nfast and regular spiking (FS and RS). Please note that they did not report whether the spread of\ndata reported is IQR or another measure. We separated our data into Upper (putative 2/3) and\nLower (4-6) layers through analysis of visually evoked potentials (VEPs), where layer 4 has the\nshortest latency VEP. and found somewhat higher values in adult term mice: Upper layers:\n0.88±0.74 spikes/s, Lower layers: 4.1±3.2 spikes/s, and an average for all units at 2.41±1.01\nspikes/s. We report only the last value in the manuscript and have changed our reporting\nthroughout to median±MAD for all lognormal data (except peristimulus time histograms). Of note\nis that task training increases the overall firing rate (Maor et al., 2019) and, in our hands, placing\nthe animals in the task context also increases firing. We show this for the V1 in the manuscript,\nbut we found the same to be true for the PFC.\nIt is certainly possible that we captured multiunit responses in our data as spike sorting\nis never perfect, and we discuss this in the revised manuscript. However, Durand et al. also\nreported that mice rarely ran or walked during the recordings, which is why they did not\nseparate the data into stationary and moving. In our hands, habituated adult mice typically\nspend a significant amount of time on the treadmill walking at different speeds, which is why we\nhave not separated the data into stationary and moving (see figure below). It is likely that this\nsomewhat elevated the firing rates in our dataset as locomotion is known to enhance them (Niell\nFigure 1. Example of motion recorded in the\nsession, with grey boxes highlighting longer\nstationary stages. The low amplitude\ndeflections from baseline reflect slow walks\n(partial treadmill rotation), while high\namplitude reflects an occasional run.\nApproximately 13 minutes of recording is\npresented.\n& Stryker, 2010). We also compared our values to other studies from adult V1 that present\nsingle unit data as individual points, for example Figure 4 in (Glickfeld et al., 2013), and found\nsimilar distributions. We also need to point out that the bias towards high spiking neurons is well\nestablished in extracellular electrophysiology (Harris et al., 2016; Siegle et al., 2021; Wei et al.,\n2020), and our data is unfortunately no exception. After quality control (ISI and amplitude\nviolations), we discard significantly more RS neurons than FS neurons. We discuss this in lines\n807-816 of the revised manuscript.\nMajor points:\n(1) Unless I am mistaken, the correlation of preterm behavior and ephys with ‘adolescent’\ncoding is based on a single pre-print, which used calcium imaging to an auditory cue. While the\nsimilarities are indeed pretty striking, the absence of relevant adolescent data in this paper and\nthe fact that the reference remains a preprint at this point suggests that the title should be\nchanged to better reflect actual behavioral and physiological differences, rather than the larger\nimplication. The authors are strongly encouraged to pursue this correspondence in their\ndiscussion of course.\nThe reviewer is correct-the study we cite remains a preprint, and it used a different\nmethodology. We, however, decided to pursue this issue experimentally as we were\nunsuccessful in our attempts to identify the circuit(s) that drive the impairments in preterm mice\n(please see our response to Reviewer 3). We were able to confirm some similarities between\nadolescent and adult preterm PFC: they both have a weak representation of the non-rewarded\ncue in RS neurons, the peristimulus time histograms (PSTHs) of their prefrontal FS neurons\nduring the task are very similar, and their evoked responses to the non-rewarded cue classify\nthe behavioral outcomes to that cue at a similar accuracy (Figures 6 and Supplementary Figure\n9). Given the differences in the processing of the rewarded cue, having “adolescent-like” in the\ntitle was not justified and we have replaced it with what we think is the main finding of our study,\nas the reviewer suggested.\nSome of our findings are different from the Nieves and Liston, 2023, but we think that is due to\nmethodology used. As the reviewer mentioned, Nieves and Liston used 2-photon imaging after\nan auditory task, while we used electrophysiology in a visual task. These two approaches, while\ncomplementary, detect different cell populations. Despite this, we were able to confirm their\nmain finding, i.e. that the overall activity in adolescent PFC is shifted towards the rewarded cue\nat the expense of the non-rewarded one.\n(2) There is no discussion of the potential depth of the units recorded. It is not required they\nbreak the recordings down by depth and layer, but reporting where the majority of units were\nlocated is important.\nThis is an important point-we now state that the neurons in the V1 are recorded from all layers.\nFor the PFC, most of our recordings were from deeper portion of layer 2/3 going to layer 6 as\nwe tried to avoid recording close to the midline (see minor points response). We revised Figure\n4 to include an image of the electrode tracks.\n(3) Percentage of visually responsive units in V1 and firing rate increases to preferred\norientation should be presented.\nThe percentage of units that are considered selective for oriented gratings is presented in\nFigure 1d and this is what we define as visually-responsive for this study. We clarify this now in\nthe methods section in the revised manuscript (lines 218-220), and we also report the firing rate\nincrease to the preferred orientation (over baseline, line 309-312). We find that our values are\nsimilar to what other studies report [such as (Wang et al., 2010), which also reports the fraction\nof neurons with OSI>0.3].\n(4) Why no separation of FS and RS neurons in V1 as for AAC? If there is no compelling reason\nthese should be separated and analyzed as for AAC.\nWe include the separation of V1 neurons into FS and RS in the revised manuscript (new Figure\n3) and we would like to thank the reviewers for bringing this up-indeed, there was no reason to\nomit it in the first place, and we apologize for this lapse. We found that both RS and FS neurons\nin preterm V1 have elevated firing during the task. While surprising, as we expected weakened\ninhibition like in the PFC, this is in line with the powerful top-down modulation of the V1 during\ntask performance.\n(5) Why does the analysis switch to z-score normalized firing rates in figure 5? For comparison\nwith previous data absolute firing rates should be used, unless there is a compelling reason.\nWe present all data now as raw firing rates. While PSTHs are the only instance where the firing\nrates are presented as mean±SEM, we changed the scale to logarithmic to indicate that the\ndata is not normally distributed.\n(6) lines 241-242 Firing rates are rarely normally distributed, but more likely to be log-normal.\nNormal descriptive statistics (mean/SEM) are likely inappropriate so should use log transformed\nor non-normal appropriate (median, percentiles).\nAs suggested, we changed all our reporting of log-normally distributed data to median±MAD\nand indicate in the Methods that all data was first tested for normality before conducting other\nanalyses.\nMinor\nWere all animals recorded included in the analysis, or were some excluded for technical\nreasons?\nWe now clarify which animals were excluded from the study and from analysis in the methods\nsection (line 184-190). Animals were excluded from study for health reasons, while animals that\nwere excluded from analysis either did not have the electrode in the correct location or the\nlocation was unconfirmed, or they had large motion or blood vessel pulsation artifacts. Artifacts\nwere present more often in animals that took longer to reach the criterion or in animals where\nthe craniotomy over the PFC was very close to the midline because of the sinus.\n236 and Fig 1: 6 Orientations or 12?\nThis was a copy/paste error and we apologize-6 orientations, which we corrected in the revised\nversion.\n272 “‘errors of omission” is this the same as “Miss”?\nYes, but we removed that term from the revised version of the manuscript.\nThey are head fixed and it is corrected in the revised version of the manuscript.\nFig 3 Define R and NR, preferably on graph or at least clearly in Legend.\nWe defined R and NR on the graphs in the revised version.\nReviewer #2\nThe manuscript by McCoy and colleagues investigates the representation and processing of\nbehaviorally salient sensory stimuli in the V1 and PFC of adult preterm-born mice. Combining\nbehavioral assessment with in vivo electrophysiology, it shows that preterm birth causes major\ndeficits, especially in the function of prefrontal circuits for error monitoring. The topic is timely\nand the main conclusions, as they are currently listed, of high relevance. I found particularly\ninteresting the limited role of environmental enrichment on the prematurity-induced cognitive\ndysfunction. The complex experimental design and technically-demanding methodologies are\nwell chosen to address the key questions. However, my overall enthusiasm for the manuscript is\ndampened by the wealth of imperfections, errors and especially, by the poor analyses and\nstatistics. The last is critical, since the conclusions of the study rely on correct analytical tools.\nThe manuscript requires a comprehensive revision of the analyses and, if necessary, correction\nof the conclusions, before pursuing with the review.\nWe appreciate the concerns brought up and we have completely revised our analysis for the\nresubmission. We consulted with a colleague who is a mathematician and an expert statistician\n(Dr. Xin Cynthia Tong), who helped us reanalyze all the data. We would like to add that she\nbrought up the same concerns after reading the first version of the manuscript. While our main\nfindings after reanalysis did not change, some of the conclusions did, as the reviewer (correctly)\nimplied.\nLines 192-193: what do the authors mean with “Unit waveforms were further compared between\noptotagging and task recordings for quality assurance.”?\nWe added a line immediately after that sentence (line 226-228) to clarify that a change in unit\namplitude of 10% or more between the two recording sessions warranted the exclusion of that\nunit from analyses as such high amplitude variability indicates either unit/probe drift or multiunit\ncontamination.\nLines 194-200: this paragraph is quite unclear. The authors refer to a Rvar, but this variable\ndoes not appear in the formula that is given. Also: paired t-test seems like an inappropriate test\nfor a log-distributed variable like firing rate. Lastly, what is the rationale of double-dipping with a\nt-test first and then then a comparison of z-scored AUCs?\nRvar was a copy/paste error and we apologize. This is corrected in the revised version of the\nmanuscript. In the revised version, we reanalyzed our unit data with tests appropriate for log-\nnormally distributed data. For determining significant cue modulation, Wilcoxon paired signed\nrank test was used, for comparison of firing rates Mann Whitney test was used, and we used K-\nS test to compare distributions (all of these and others are indicated in the methods and\nappropriate sections throughout the manuscript). Instead of t-tests of AUCs, we use the\npermutation test of PSTH AUCs in the revised version of the manuscript. While our results\nremained largely the same, the significance is different with the new analysis.\nLines 232-233: could the authors speculate as to what a day of prematurity in mice corresponds\nin human pregnancies?\nWe briefly discuss this in the revised manuscript (line 865-872), but we must admit that this\ncomparison remains a challenge and a contentious issue in the field. Most studies consider\nmice during the first week to 10 days of life an equivalent of a third trimester fetus, some of\nwhich is based on electrophysiological comparison of rodent and human developing cortices\n(Colonnese et al., 2010). Our unpublished data indicates that in terms of electrophysiological\nactivity, mice and humans share a similar developmental trajectory from birth through the first 2\nweeks and one year of life, respectively, and that birth itself plays a role in patterning the\nperinatal intrinsic activity in mice (which is the line of research that led us to test the long term\nconsequences of advancing the birth timing). Current models of prematurity-related brain injury\n(term mice subjected to hypoxia and/or ischemia) do not capture the actual prematurity that, at\nleast according to our findings, itself has an impact on brain development. It will be important to\ncompare the prefrontal function in term mice subjected to hypoxia/ischemia to those in the\ncurrent study and relate both to findings in humans.\nLines 241-243: I do not think that it is appropriate to test differences in firing rates with a t-test. I\nsuggest to log-transform the data if the authors want to use a test that assumes normality.\nFurther, what is a nested t-test? By looking at GraphPad guides, it seems that the test that is\nused is a linear mixed-effect model, but I am not sure that this is the case. If it is indeed the\ncase, I suggest that the authors use this terminology, which is much more common than “nested\nt-test”. This part should also be better explained in the Methods section.\nThe reviewer is right, and we corrected this-we now use Wilcoxon signed rank test for pre-post\ncomparisons and Mann-Whitney or K-S test for comparison between groups. We reanalyzed all\nthe data for the revised version of the manuscript and removed the nested tests, which should\nmake the terminology clearer, along with the analysis itself.\nLines 244-248: to what do the degrees of freedom refer to? Did the authors use a nested\nstatistical test also in this analysis?\nWe revised this analysis completely, and we compare values obtained per mouse-we explained\nthis with more detail in the revised version.\nLines 262-3: do any of the provided references show that there is a monosynaptic connection\nfrom V1 to the ACC?\nThank you for pointing this out-we added (Zhang et al., 2016) which shows exactly that.\nLines 264-281: I have many doubts about the statistical analysis:\n- Are the authors running two separate models to compare Term vs MFP Term and Term vs\nMFP Term vs Preterm? If so, I do not understand the rational for this. I would suggest to only\nrun one model with the three groups, which should also simplify the manner in which statistical\nresults are reported.\nWe have done this for the revised manuscript.\n- Why is there only one pvalue when comparing Term, MPF Term and Preterm? Are the authors\naggregating the first two groups?\nYes, as originally both term groups were aggregated into a single one as there were no\nstatistical differences between them. In the revised manuscript we separated them and report all\ngroup comparisons.\n- Why are the authors reporting R2 when the variable of interest is the slope?\nWe removed the linear regression from the trajectory analyses and rely solely on linear mixed-\nmodels in the revised manuscript, which described our data better.\n- Are the numbers used to run the Chi2 test 0/22, 0/19 and 5/29? If so, how can these values\nlead to the reported p-value? If not, could the authors give the exact values that were used?\nWe replaced Chi-square for most analyses with K-S test in the revised version as it can handle\nzero values. All data is provided in source data file.\nLines 308-311: in previous instances in which data was nested (see lines 241-243), the authors\nseemingly took this into account when comparing means, but this does not seem to be the case\nfor this analysis. I suggest that the authors re-run the analysis while keeping in consideration the\nnestedness of their data.\nPSTHs of neurons are typically compared with Ns of units in literature, and we did not want to\ndeviate from that. However, we replaced the means comparison with permutation test of the\nAUCs.\nLines 308-316: why does the number of units change between the two analyses?\nThe number of neurons significantly modulated by the cue is different between the two cues, as\nwell as between the behavioral outcomes to the cues. For the comparison of PSTHs and\nselectivity indices (SIs), we considered only significantly modulated neurons. We clarify this in\nthe methods section of the revised manuscript and report it as % of all neurons in the results to\nmake this point clear.\nLines 312-316: if the increase in firing rate is context-specific, why is it elevated also before the\nstimulus is administered?\nWe corrected this to “task-specific”. The firing rates in preterm mice are elevated as soon as\nthey are placed in the context of the task, which is why they are elevated before the cues as\nwell. We clarified this point in the revised version of the manuscript.\nFigures 3D-3H: why are the authors binning the MI and then modeling their data with a Chi2\ntest? I don’t understand the rational for binning. This data would be much more straightforwardly\nanalyzed without binning and with a simple linear-mixed effect model. If the authors even want\nto consider that the variable can only vary between -1 and 1, they could use a generalized linear\nmixed-effect model, but this might be an overkill judging by the distribution of the Mis (rather\ntowards 0 than the extrema). If you use a Chi2, you are not testing for differences in means\nanymore.\nThis is an important point which we clarified in the revised version-we are comparing the\ndistributions of the indices and not their means/medians, as the populations can have very\nsimilar means through different distributions. We think that distribution is more important for\nselectivity index. We also renamed the modulation index to selectivity index as our literature\nsearch found that modulation is used more for state-dependent processes, and selectivity for\nthe sensory cues.\nFigure 3E and lines 338-339: as in lines 264-281, I do not understand how such a small % of\ninhibited neurons can lead to the reported p-values. What data went into this test? Since the\neffect has to be tested on a mouse level, I also suggest to improve the visualization of the data\nto understand the consistency of the effect on an individual mouse level (e.g. plot the %\nof neurons that are activated/inhibited/not affected in individual mice).\nWe tested this on a mouse level in the revised version of the manuscript and came to different\nconclusions (new Figure 3 and Supplementary Figure 5). While we tried to include all points in\nthese graphs, it was a challenge to make them readable, which is why we added the Reward/No\nReward preference graphs that represent the distribution of the mouse data.\nLines 374-375: still linear mixed-effect model? Please provide details\nThis analysis was revised in the new version of the manuscript, where we analyzed the data per\nanimal (lines 495-497).\nLines 376-378: why ignore nestedness when this was not the case when comparing firing rates\nin prior parts (see comments above) of the manuscript?\nThis was revised in the new version of the manuscript, which now represents per mouse data\n(line 498).\nLines 379-383: what are the values that went into this Chi2?\nFigures 4F-G-H-I, see previous comments on Chi2 on continuous data and Figure 3E.\nFigures 5C-D-F-G, see previous comments on Chi2 on continuous data and Figure 3E.\nFigures 6E-G-H-I, see previous comments on Chi2 on continuous data and Figure 3E.\nAll these analyses were revised in the new version of the manuscript (Chi-square is no longer\nused for this type of data).\nLine 986: there is a typo. It should be “white”, not “while”.\nWe corrected this.\nReviewer #3\nIn this study, the authors investigate how preterm birth affects cognitive function and visual\ninformation processing by recording neural activity in the V1 and ACC of mice during a Go/No-\ngo task using electrophysiological techniques. They demonstrate that preterm mice exhibit a\nhigher FA rate in the Go/No-go task, despite showing comparable orientation selectivity in V1\nneurons to term controls. Furthermore, they show that both V1 and ACC neurons in preterm\nmice respond differently to visual stimuli during the task. Using optogenetic tagging, they also\nidentify abnormalities in the visual processing of V1-projecting ACC neurons and ACC-FS\nneurons. The authors conducted experiments using both male and female mice, employed\nappropriate control groups, and used suitable methods and analyses to address the important\nquestion of how preterm birth impacts cognitive function. However, as described more in detail\nbelow, there are significant limitations to the interpretation of the data. Notably, the study lacks\nan investigation of causality, which diminishes its impact.\nMajor comments:\n1) The study does not provide any causal link between neural activity changes of studied circuit\n(V1 projecting ACC neurons or FS-ACC neurons) and behavioral changes (increased FA) in\npre-term mice. A key hypothesis in this manuscript is that overactivation of ACC-FS neurons\nleads to decreased activity of V1-projecting ACC neurons, resulting in reduced V1 activity in No-\ngo trial and an increased FA rate. However, none of these causal relationships have been\ndemonstrated, leaving the study less mechanistic. Without a demonstration of direct\nmanipulations of neural circuits during behavior impacting behavior, observed neural changes\nmay not be related to behavioral changes, leaving the study descriptive.\nWe agree with the reviewer and emphasize in the revised version that our study is descriptive,\nand we discuss this limitation. We have attempted to test for causality in both ACC→V1 circuit\nand in FS neurons with no success, some of which was due to methodological limitations, as we\noutline below.\nFirstly, we tried to manipulate both ACC→V1 and prefrontal FS neurons optogenetically but\nfound that prolonged light stimulation (2x day for 50 x 3 s duration) at levels that had a\nphysiological impact on neuronal firing led to neurotoxicity. Reducing the cue duration to 2\ninstead of 3 seconds did not mitigate this. We next attempted to suppress the activity of\nACC→V1 circuit with chemogenetics, which unexpectedly elevated V1 firing in controls (term\nmice, see Figure 2a). Increasing the activity of FS neurons through chemogenetics in adult\npreterm mice failed to improve their behavior in a pilot experiment (Figure 2b). We did not\ninclude this data in the manuscript as the data from ACC→V1 activation will be a part on\nanother manuscript, and the pilot experiment with chemogenetic activation of interneurons did\nnot warrant collecting a full dataset. Further, after collecting the adolescent data (new Figure 6),\nwe now think that there is a developmental window during which these manipulations might\nhave more of an effect. We also mention this in our response to Reviewer 2, but our preliminary\ndata indicates that the alterations in the firing of prefrontal neurons in preterm mice begin at\nbirth, further indicating that there is a defined window for interventions after preterm birth. We\nare actively addressing this issue in another study as it is beyond the scope of this manuscript.\nTo increase the significance of our findings and to try to shed more light on the developmental\nimpact of preterm birth on the PFC, we added data describing cue processing in adolescent\nmice and compared it to preterm mice. We found several similarities in the representation of the\nnon-rewarded cue and the activity of FS neurons, which we discuss in details in the revised\nversion (Figure 6, Supplementary Figure 11 and associated Results and Discussion sections).\nFigure 2. a) PSTH of V1 firing after administration of CNO (2.5 mg/kg) and vehicle (DMSO) to mice 30 minutes\nbefore the onset of training. Mice were transduced with inhibitory DREADD G1 in V1-projecting PFC neurons.\nSignificant firing rate increase is present with CNO administration. b) Comparison of behavioral performance\nof preterm mice from Figure 2 of the manuscript and preterm mice injected with S5E2-Gq in the PFC (Gq in\nfast-spiking interneurons) and treated with CNO (2.5 mg/kg) 30 minutes before each training session. While\nthis preliminary cohort includes only 5 mice, the overlap with the standard preterm group indicates no effect of\nfast-spiking interneuron activation on behavior in preterm mice.\n2) It is unclear if the observed neural and behavioral deficits in preterm mice reflect deficits in\nlearning or/and discrimination. Throughout the manuscript, these two concepts are not clearly\nconsidered separately. Due to the trajectory difference (especially FA) during training phase, it is\nhighly likely that there is a deficit in learning. Demonstration of neural correlate of learning\ntrajectory would be informative to enhance the mechanistic significance of this study (especially\nif the study does not provide causal link between circuit activity and behavior).\nThis is an important point-the deficit in preterm mice is a learning deficit and we clarify this in the\nrevised version. In the original version, we used the terms interchangeably which is incorrect\nand as the reviewer pointed out, preterm mice have a learning deficit (and not discrimination).\nWe now added new data that indicates altered encoding of behavioral outcomes to the non-\nrewarded cue in preterm and adolescent mice, as well as a finding that re responses to the\nrewarded cue are predictive of performance in preterm but not term mice, further providing\nsupport for divergent mechanisms of learning in the two groups of mice (new Figures 4, 5 and\n6). We discuss these, especially in the context of behavioral inhibition.\n3) Given that V1 activity is known to be enhanced by locomotion, extent and timing of\nlocomotion on the treadmill during the task should be analyzed to rule out that neural changes\nare not confounded by locomotion.\nWe discuss this also in our response to Reviewer 1, where there is an example of motion during\na recording session. We added motion tracking data during behavioral sessions to the revised\nmanuscript (Supplementary Figure 4A of the revised manuscript). We found no significant\ndifferences in the extent or the timing of motion between term and preterm mice during task\nperformance, indicating that the elevated firing rates in preterm mice have a different origin.\n4) Preterm mice show a lower Hit rate even in the naïve state (Fig 2G). This raises questions\nabout potential motivational deficits, which can be assessed by different tasks such as\nprogressive ratio test.\nWhile we were unable to add the progressive ratio test, we analyzed the lick frequencies during\nthe training session as a proxy for motivation (Berditchevskaia et al., 2016). We indeed found\nthat lick rates significantly drop during the session, but all animal groups had a similar reduction\nin lick frequencies, indicating that the motivation of preterm mice during the task is comparable\nto term mice. The reviewer correctly points out low lick rates in naïve preterm mice, but they\nincrease throughout the training and surpass those of term mice (Supplementary Figure 4C of\nthe revised manuscript). We, however, plan to pursue tasks that can address motivation as well\nas attention better than the current one in our future studies.\n5) While the activity of FS and RS neurons is analyzed separately in Fig 4, the activity of V1\nneurons in Fig 3 is not divided into these categories. It is unclear why FS and RS neurons were\nnot differentiated for V1 activity, particularly as the distinction between excitatory and inhibitory\nneurons in V1 could provide critical insights into the observed changes.\nWe agree and we have added this to the revised manuscript (new Figure 3). Indeed, our data\nfurther indicates the involvement of top-down areas in observed V1 firing changes in preterm\nmice, especially since both RS and FS neurons are equally affected.\n6) Inclusion of “Adolescent-like” in the title misleading. While author cited relevant literatures,\nthey did not examine the neural activity nor behavior using the same task used in this study.\nAuthors should consider conducting experiments in adolescent mice to support the claim, or\n“Adolescent-like” should be removed from the title, result section, and limited to the discussion\nsection.\nThis is a very important point. As the reviewer pointed out, the connections we made with\nadolescent processing were based on a different study and purely speculative. For the revision,\nwe trained adolescent term mice in our task and recorded their prefrontal activity (new Figure 6).\nWe indeed found similarities in the representation and processing of the non-rewarded cue in\nadolescent and preterm mice. Still, and as we mention in our response to Reviewer 1, the\ndifferences in the processing of the rewarded cue warrant removal of “adolescent-like” from the\ntitle and the manuscript text. Instead, we focused on describing the differences and similarities\nbetween the two groups of mice, as well as on larger implications of our findings. We were able\nto confirm some of the findings from Nieves and Liston, 2023 and we think that the adolescent\ndata will be a valuable addition to the field. The differences we found in our task between term\nadolescent and adult mice in prefrontal activity warrant their own study, which we are currently\npursuing.\nOther comments:\n-In Fig 2C, the Miss marker (water droplet) is present, but the cross marker is missing.\nWe corrected this in the revised version (minus is added instead of a cross).\n-How were trained mice defined in Fig 2G? Were only animals that passed the criteria included,\nor were all animals analyzed at their final session? If the latter, it would be important to compare\nonly those that passed the criteria to see whether FA rate differences remain.\nThank you for bringing this up-we now clarify in detail that-all term born mice passed the\ncriterion within the 4000 trial limit (lines 175-176 and 325-327). For preterm mice, all mice were\nincluded and analyzed at the last session, which for mice that did not pass the criterion is the\nsession on the 20th day of training. As the reviewer suggested, we compared the mice that\npassed the criteria and the FA differences remained, albeit the p value was higher (line 353-\n362). We also must note that all our electrophysiological data is from mice with similar d’ values,\ndemonstrating divergent processing of task cues and in term and preterm mice. We included\nthis in the revised version of the manuscript (line 597-601).\n-In Fig 3G, V1 activity during FA trials does not appear to increase significantly in the Term\ngroup. The sustained activity in V1 shown in Fig 3C should be further discussed regarding\nwhether it genuinely reflects premotor activity.\nThis is an important point-we removed this wording from the manuscript for 2 reasons: 1) the\nspace limitations did not accommodate a full discussion in this manuscript, and 2) a detailed\nstudy of task-related sustained activity in the V1 is ongoing in our laboratory and a part of\nanother manuscript in which we are addressing the role of this activity in behavior. It is important\nto note that licks to the rewarded cue occur in bouts (Figure 3), whereas for False Alarm trials\nthey typically happen as a few licks with random timing, so any increases in activity related to\nlicking are lost by averaging. Still, the current data in this manuscript is not sufficient to imply\nthat sustained activity reflects premotor activity, which is why we removed it from the\nmanuscript.\nFigure 3. Example of licks and behavioral outcomes, with visible lick bouts of many licks to the rewarded cue\n(Hits/H) and few licks to the non-rewarded cue during False Alarms (FA). CR: Correct Rejection.\n-The authors claim that elevated firing rates in preterm mice are unlikely due to licking during\nthe task (L312), but Supplementary Fig S3 shows that Preterm mice have significantly more\nlicks per trial than Term mice. Without investigating the correlation between lick count and\nsustained activity, this conclusion is premature. Correlating lick count with sustained activity and\ndemonstrating a lack of correlation would strengthen this argument.\nWe performed this analysis for the revised manuscript and found no correlation between\nsustained activity and lick frequency (Supplementary Figure 4D). In preterm mice, the\nrelationship between the two appeared inverse even, which would not be the case if sustained\nactivity reflected lick related activity. We also computed correlograms for both V1 and PFC\nwhere we show peaks in their activity occur significantly prior to onset of licks (new Figures 3D\nand 4C).\n-Fig 3D shows a distribution of MI values where some V1 neurons exhibit higher MI and others\nlower MI in Preterm mice. It is unclear whether the average MI for V1 neurons in Preterm mice\nis genuinely higher than for neurons in Term mice. Showing vertical lines indicating group\nmeans would clarify this point.\nWe changed the plots to make the distributions more visible and now clearly indicate median\nvalues. Means are indeed similar for several SI groups from term and preterm mice, but we are\ncomparing the distribution of indices in our study which we think is more informative, especially\nas means can overlap in two different distributions (Reviewer 2 brought up a similar concern).\nFor example, a higher number of positively modulated neurons with high SI values will give a tail\nto the distribution, but the difference in means may be marginal if the rest of the population is\nequally distributed compared to the other group. However, high SI values indicate that these\nneurons receive strong sensory or cue-related inputs, and the opposite is true for low SI values.\nThis is evident in revised Figure 4H, where a negative tail is present in the SI values for the non-\nrewarded cue in preterm mice, indicating that a fraction of their prefrontal neurons is suppressed\nafter the non-rewarded cue, which is not the case in term mice.\n-Are the data in Fig 4E limited to RS neurons? Clearly separating RS and FS neurons and\npresenting the data would improve clarity.\nWe revised this figure and now both RS and FS are included.\n-Figure 4K is missing.\nThis was corrected.\n-The statement on L427 (\"stronger positive modulation by the rewarded cue despite no change\nin the firing rates\") is confusing. Since MI is calculated using firing rates, the claim of \"no change\nin firing rates\" is misleading. While statistical significance may not have been detected,\ndifferences are evident in the traces.\nThis statement was removed, and we revised this and similar descriptions to simpler and more\naccurate sentences.\n-The claim in L443 that deficits are \"not as severe as in the V1\" (Fig 3) is unclear. Based on the\nMI values, changes in V1-projecting ACC neurons appear larger. Moreover, throughout the\nmanuscript, the overlap between the bars of MI histograms for each group makes it difficult to\ninterpret the data. Adjusting the colors or the presentation style of the graphs could improve\nreadability.\nWe swapped the histograms for raincloud plots which made the distributions more visible and\nadded a line for medians. As mentioned, we moved the optotagging data to Supplementary\nMaterial. We appreciate this feedback these data are key for some of the conclusions and it is\nvital that the figures are readable.\n-The degree of overlap between opto-tagged FS neuron data in Fig 6 and FS neuron data\nclassified by spike width in Fig 4 should be presented in the supplementary figure. If\ndiscrepancies exist, these should be discussed.\nWe now included the FS data to Figure 4 and moved the optotagged data to Supplementary as\nour revised statistical approaches indicated an insufficient N for some analyses. The data is\nlargely overlapping, with discrepancies in the firing rates as originally reported, likely due to\nmore low-firing neurons being detected with optotagging.\n-In Fig 7B, the authors claim that environmental enrichment (ENR) accelerates learning in the\nTerm group compared to the standardly housed (STD) condition. However, the initial\nperformance on Day 0 appears worse, and there seems to be no difference in final performance\nor days to reach criterion.\nWe revised the statistical analysis for this dataset, as well as our conclusions. The reviewer\ncorrectly identified that the performance in term group was slightly worse than in standardly-\nreared term mice, which our previous statistical tests failed to pick up on. We used linear mixed\nmodels for comparison of all learning trajectories in the revised manuscript and identified other\npotential effects of birth, as well as of sex (see point below). We discuss the potential reasons\nfor the negative effect of ENR in the revised version (lines 830-835).\n-Since both male and female data were collected, the authors should explicitly show whether\nthere were any sex differences in the supplementary figures.\nSupplementary Figure 2 now shows sex-segregated data and we report the effects of sex in\nlines (lines 353-362). To our surprise, we found that females overall have lower d’ values in our\ntask, but we also found that birth impacts both sexes to a comparable degree. While it is\ncurrently unclear why females of all groups perform worse in our task, we plan to address this in\nour future studies.\nReferences\nBerditchevskaia, A., Cazé, R. D., & Schultz, S. R. (2016). Performance in a GO/NOGO\nperceptual task reflects a balance between impulsive and instrumental components of\nbehaviour. Scientific Reports, 6, 27389. https://doi.org/10.1038/srep27389\nColonnese, M. 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Reconciling\nfunctional differences in populations of neurons recorded with two-photon imaging and\nelectrophysiology. ELife, 10. https://doi.org/10.7554/eLife.69068\nWang, B.-S., Sarnaik, R., & Cang, J. (2010). Critical period plasticity matches binocular\norientation preference in the visual cortex. Neuron, 65(2), 246–256.\nhttps://doi.org/10.1016/j.neuron.2010.01.002\nWei, Z., Lin, B.-J., Chen, T.-W., Daie, K., Svoboda, K., & Druckmann, S. (2020). A comparison\nof neuronal population dynamics measured with calcium imaging and electrophysiology.\nPLoS Computational Biology, 16(9), e1008198. https://doi.org/10.1371/journal.pcbi.1008198\nZhang, S., Xu, M., Chang, W.-C., Ma, C., Hoang Do, J. P., Jeong, D., Lei, T., Fan, J. L., & Dan,\nY. (2016). Organization of long-range inputs and outputs of frontal cortex for top-down control.\nNature Neuroscience, 19(12), 1733–1742. https://doi.org/10.1038/nn.4417"}, {"round": 2, "reviewer_comments": {"reviewer_5": "The authors addressed the majority of reviewers’ concerns (statistical analysis methods, clarifying that the study is descriptive, adding new adolescent data, motion tracking, lick rate, etc, included the distinction between learning and discrimination and discussed their methodological limitations). However, the following is insufficient.\nIn response to Reviewer 3's Key Comment 1, the authors state:\n\"Furthermore, after collecting adolescent data (new Figure 6), we believe there may be a developmental window during which these manipulations may be more effective. We also mentioned this point in our response to Reviewer 2, but preliminary data indicate that changes in the firing of prefrontal cortex neurons in preterm mice begin at birth, suggesting a clear window for intervention after preterm birth. This issue is beyond the scope of this manuscript and is being actively addressed in a separate study.\"\nThis statement is unclear. They propose experiments based on data that has not been shown. Additional data or a detailed explanation is required. Furthermore, any related statements cannot be found in their responses to Reviewer 2 (and Reviewer 1).", "reviewer_6": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "POINT-BY-POINT RESPONSE TO REVIEWS\nReviewers #5 and #6 (Remarks to the Author):\nThe authors addressed the majority of reviewers’ concerns (statistical analysis methods,\nclarifying that the study is descriptive, adding new adolescent data, motion tracking, lick rate,\netc, included the distinction between learning and discrimination and discussed their\nmethodological limitations). However, the following is insufficient.\nIn response to Reviewer 3's Key Comment 1, the authors state:\n\"Furthermore, after collecting adolescent data (new Figure 6), we believe there may be a\ndevelopmental window during which these manipulations may be more effective. We also\nmentioned this point in our response to Reviewer 2, but preliminary data indicate that changes\nin the firing of prefrontal cortex neurons in preterm mice begin at birth, suggesting a clear\nwindow for intervention after preterm birth. This issue is beyond the scope of this manuscript\nand is being actively addressed in a separate study.\"\nThis statement is unclear. They propose experiments based on data that has not been shown.\nAdditional data or a detailed explanation is required. Furthermore, any related statements\ncannot be found in their responses to Reviewer 2 (and Reviewer 1).\nOur response\nFirstly, we would like to thank the Reviewers for taking the time to review the manuscript as well\nas our response to the previous round of reviews. In the revised version of the manuscript, we\nnow discuss the developmental mechanism that may be impaired in preterm mice from birth.\nWhile we do not provide additional data from our laboratory, we cite studies with strong\nevidence supporting our reasoning.\nBriefly, previous studies indicate that preterm mice have reduced extracellular (CSF) serotonin\nconcentrations during the first few days after birth (Toda et al., 2013). Given the vital roles of\nperinatal serotonin signaling the development of prefrontal neurons, and in the maturation of\nadult behaviors (new references 98-109 in the revised manuscript), we hypothesize that\nreduced serotonin concentrations in neonate preterm mice negatively impact the development\nand/or maturation of circuits that guide reward-driven behaviors in adult mice. We discuss this in\nthe revised version of the manuscript, lines 857-865. While experiments that will test this\nhypothesis are beyond the scope of this study, our laboratory is actively working on them\n(R01MH140184)."}]

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