The Rat Medial Prefrontal Cortex Exhibits Flexible Neural Activity States during the Performance of an Odor Span Task

Task-normalized firing rates for pyramidal cells and interneurons. A, D, Coronal and sagittal rat brain sections depicting the location of the recording sites and photograph of a representative electrode placement. Probes were located in the prelimbic region of the mPFC. Box indicate the medial-lateral (left) and anterior–posterior (right) locations of the electrode arrays. B1, Grand-average (±SEM) of task-normalized firing rate for 382 neurons recorded across 77 recording sessions. Firing rates were z-scored before averaging across neurons. B2, Timeline of a single trial, where the three main epochs of the task (Delay, Foraging, and Reward) were identified through the four behavioral timestamps: Delay starts; Delay ends; Correct dig; and End of trial. Specific percentages of completion were assigned to each task epoch to calculate the task-normalized firing rates (see Data analysis – Task normalized firing rates). C1, Distribution of first PCA components (integrating two waveform features) for pIn, pPy, and unclassified neurons. The Gaussian fits used for the classification are shown as continuous lines on top of the distribution. C2, Mean waveforms (±SEM) for the three classes of neurons detailed in C1. Unclassified neurons had a mean waveform closer to the pPy class and where subsequently labeled as pPys. C3, Distribution of mean firing rates for 61 pIns and 321 pPy. Firing rates were higher in the pIn population than in the pPy one (Kolmogorov–Smirnov test, D_(321,61) = 0.30, p = 1.1 × 10⁻⁴). Vertical dotted lines mark the mean value of each distribution. D, Grand-average (±SEM) of task-normalized firing rate for pIns and pPys. The firing rates in the two classes were significantly different (two-way ANOVA, interaction cell class × time, F_(99,38000) = 3.02, p = 8.4 × 10⁻²²). Black horizontal lines mark groups of time bins with significant differences between pIns and pPy (FDR-corrected rank-sum, p < 0.05). Top Left, Distribution of Fano factors for pPys versus pIns (dotted lines mark mean values). Pins exhibit higher trial-to- trial variability (Kolmogorov–Smirnov test, D_(321,61) = 0.30, p = 9.8 × 10⁻⁵). ***p < 0.001.

Identification of neural populations via PCA. A, First three PCs. Projection of firing rates for the 382 neurons along the first three principal eigenvectors identified through PCA (left) and variance explained by each PC (right; blue line marks the broken stick model fit on the data). The first three PCs together explained 56% of the original variance of the dataset. B, Task-normalized firing rates for the 382 neurons identified sorted according to their loadings on first, second, and third PC (left, center, and right, respectively). Red arrows on the right side of each color-plot indicates the transition point between positive and negative loaders. C, Distributions of loadings on each PC separated for pIns and pPys. On the first PC pIns’ loadings were significantly higher than pPys’ ones (left; Kolmogorov–Smirnov test: D_(321,61) = 0.24, p = 4.9 × 10⁻³), whereas no significant effect was found on the other two PCs (Kolmogorov–Smirnov test: D_(321,61) = 0.10, p = 0.63 for PC2; D_(321,61) = 0.12, p = 0.37). **p < 0.01.

Activity of pyramidal neurons is predictive of span. A, Grand-average (±SEM) of task-normalized firing rate for 321 pPys, separated according to the session’s span (low and high span were defined according to the threshold identified in Fig. 1C). Firing rates in the two groups were significantly different (two-way ANOVA, interaction between span class and time bin, F_(99,31900) = 1.72, p = 1.1 × 10⁻⁵). Black horizontal lines mark groups of time bins showing significant differences between low and high span groups (FDR-corrected rank sum, p < 0.05). B, Grand-average (±SEM) of task-normalized firing rate for 61 pIns, separated according to the session’s span. Firing rates in the two groups were not significantly different (two-way ANOVA, interaction between span class and time bin F_(99,5900) = 0.87, p = 0.81). ***p < 0.001.

Identification of subpopulations of pyramidal neurons. A1, AIC for the PCA-features k-means clustering, calculated for different number of clusters (k). The selected number of clusters (k = 4) was identified through a broken stick fit (cyan line). A2, Loadings on the first 3 PCs for the population of 321 pPys clustered. Different colors indicate the different classes assigned. A3, Average (±SEM) task-normalized firing rate for each of the classes identified. B, Grand-average (±SEM) of task-normalized firing rate for each class of pPys, separated according to the session’s span (low or high). Only firing rates in Class 2 were significantly different (two-way ANOVA, interaction span class × time, F_(99,7000) = 3.59, p = 1.4 × 10⁻²⁹). Black horizontal lines mark groups of time bins showing significant differences between low and high span groups (FDR-corrected rank sum, p < 0.05). No significant differences between firing rates in the low and high span sessions were observed in the remaining classes (two-way ANOVA, interaction span class × time: F_(99,15300) = 1.23, p = 0.06 for Class 1; F_(99,4700) = 0.93, p = 0.67 for Class 3; F_(99,4300) = 1.18, p = 0.11 for Class 4. ***p < 0.001.

Distinct neural trajectories for familiar and novel odor approaches. A, Neural activity trajectories in the PC space for 188 pyramidal neurons around familiar and novel approaches (time interval −2 to 2 s around each event, first 3 PCs explaining 56% of variance). Arrows indicate module and direction of trajectories’ speed. B1–B3, Average normalized firing rates (±SEM) for positive and negative PC loaders for familiar approaches (left) and novel approaches (right). Loadings were obtained considering a time interval from −2 s to 0.3 s around each event. C, Empirical cumulative distribution function (CDF) of absolute loadings on the first three PCs for familiar and novel approaches (time interval −2 s to 0.3 s around each event, first 3 PCs explaining 74% of variance). Absolute loading distributions in the two classes were different (Kolmogorov–Smirnov test: D_(188,188) = 0.22, p = 2.0 × 10⁻⁴ for PC1; D_(188,188) = 0.19, p = 2.5 × 10⁻³ for PC2; D_(188,188) = 0.15, p = 2.7 × 10⁻² for PC3). *p < 0.05; **p < 0.01; ***p < 0.001.