Neural Correlations Underlying Self-Generated Decision in the Frontal Pole Cortex during a Cued Strategy Task

NEUROSCIENCE Valentina Mione et al. / Neuroscience 404 (2019) 519–528

Neural Correlations Underlying Self-Generated Decision in the Frontal Pole Cortex during a Cued Strategy Task Valentina Mione,a Satoshi Tsujimoto b,c,* and Aldo Genovesio a,** a

Department of Physiology and Pharmacology, Sapienza, University of Rome, Rome, Italy

b

Department of Intelligence Science and Technology, Graduate School of Informatics, Kyoto University, Kyoto, Japan

c

The Nielsen Company Singapore Pte Ltd, Singapore

Abstract—We have previously shown how the Frontal Pole cortex (FPC) neurons play a unique role in both the monitoring and evaluating of self-generated decisions during feedback in a visually cued strategy task. For each trial of this task, a cue instructed one of two strategies: to either stay with the previous goal or shift to the alternative goal. Each cue was followed by a delay period, then each choice was followed by a feedback. FPC neurons show goal-selective activity exclusively during the feedback period. Here, we studied how neural correlation dynamically changes, along with a trial in FPC. We classified the cells as goal-selective and not goal-selective (NS) and analyzed the time-course of the crosscorrelations in 76 pairs of neurons from each group. We compared a control epoch with the feedback epoch and we found higher correlations in the latter one between goal-selective neurons than between NS neurons, in which the correlated activity dropped during feedback. This supports the involvement of goal-selective cells in the evaluation of selfgenerated decisions at the feedback time. We also observed a dynamic change of the correlations in time, indicating that the connections among cell-assemblies were transient, changing between internal states at the feedback time. These results indicate that the changing of the pattern of neural correlations can underlie the flexibility of the prefrontal computations. © 2019 IBRO. Published by Elsevier Ltd. All rights reserved.

INTRODUCTION

1999), showing that the relationships between neurons in these areas are strictly bounded both to stimulus attributes and to attentional processes. In research on high-level cognition, the study of neural synchrony has focused on the structure, functional organization, and nature of the possible connections among neurons within the prefrontal and parietal cortex (Constantinidis et al., 2002; Sakurai and Takahashi, 2006; Katsuki and Constantinidis, 2013; Katsuki et al., 2014; Nougaret and Genovesio, 2018) to have a better understanding of the temporal integration of events and how different representations are linked to the planning of future actions. The ability to process such complex information in order to make future decisions relies on a widely developed granular prefrontal cortex, a unique characteristic of a primate's brain (Goldman-Rakic, 1988; Fuster, 1991), in which the representation of increasingly abstract information takes place across hierarchically-organized structures (Badre, 2008). In this study, we explore for the first time, the interaction between pairs of neurons simultaneously recorded in Frontal Pole cortex (FPC), a prefrontal area that has been suggested as the apex of this functional hierarchy (Christoff and Gabrieli, 2000). FPC is located at the extreme anterior part of the frontal lobe, and its contribution to high-level cognition remains unclear and little explored, principally due to its arduous accessible position (Mitz et al., 2009).

While the study of the properties of single-neurons, especially through their firing rates, can provide information about the mechanisms underlying the function performed by single neurons, understanding how their interactions might lead to the encoding and processing of information requires consideration of the activity of neurons recorded simultaneously from different electrodes. One method to examine the spike trains of simultaneously recorded neurons is the crosscorrelation analysis between pairs of cells. Fast interactions among different neurons have been reported repeatedly in several brain regions, especially in the sensory and motor cortices. Changes in correlation strength between neurons have been extensively reported in the visual system (e.g. Kreiter and Singer, 1996; Dan et al., 1998; Fries et al., 2001), in the somatosensory (Steinmetz et al., 2000; Reed et al., 2008, 2012), and in the auditory cortex (DeCharms and Merzenich, 1995; Brosch and Schreiner,

*Correspondence to: S. Tsujimoto, Consumer Neuroscience Division, The Nielsen Company Singapore Pte Ltd, 47 Scotts Road, #13-00 Goldbell Towers, Singapore, 228233. **Correspondence to: A. Genovesio, Department of Physiology and Pharmacology, Sapienza, University of Rome, Piazzale Aldo Moro 5, 00185 Rome, Italy. E-mail address: [email protected] (Aldo Genovesio). https://doi.org/10.1016/j.neuroscience.2019.02.023 0306-4522/© 2019 IBRO. Published by Elsevier Ltd. All rights reserved. 519

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A few lesion studies on FPC have suggested its role in the exploration of novel task alternatives and in redistributing cognitive resources to catch novel opportunities of reward (Boschin et al., 2015: Mansouri et al., 2015). Neuroimaging (for a review Christoff and Gabrieli, 2000) and one monkey neurophysiology study (Tsujimoto et al., 2010) have pointed to a hypothesis that FPC is involved mainly when evaluation and manipulation of internally generated (or self-generated) decisions are needed. A recent work by Miyamoto et al. (2018) combined neuroimaging with lesions and indicated that FPC is recruited during nonexperienced events and, in particular, when the subjects are asked to judge their own confidence, in support of a role in higher-order concepts, such as introspection and awareness of ‘ignorance.’ We reanalyzed the dataset of Tsujimoto et al. (2010) extending the analysis from single neurons to the correlation between neurons. In this dataset, the firing activity of FPC neurons is, in fact, quite simple, as they do not encode any information except for the goal chosen in the feedback period. This apparent simplicity has an advantage to facilitate the comparison of the correlations of goal-selective cells with the correlations of the not goal-selective cells, which can be viewed as a control for the lack of any task coding. To study the dynamic structure of the correlations among a cell assembly, we adopted the Joint Peristimuls Time Histogram analysis (as descripted by Aertsen et al., 1989; Vaadia et al., 1995), which highlights the time-course of spike interdependence through different epochs of a task and confers robustness to the statistical analysis with regard to firing rate (Ushiba et al., 2002). We found that goal-selective cells maintained stable correlations during the FB period in contrast to the drop of correlations between the remaining cells.

EXPERIMENTAL PROCEDURE Subjects We trained two male rhesus monkeys (Macaca mulatta), each weighing ~ 10 kg, to perform a visually cued strategy task. During the experiment, the monkeys were restrained comfortably in a primate chair with their heads fixed and positioned 32 cm from the monitor. All procedures conformed to the Guide for the Care and Use of Laboratory Animals and were approved by the National Institute of Mental Health Animal Care and Use Committee.

Task

As described previously (Tsujimoto et al., 2010, 2011, 2012, Fascianelli et al., 2017, Tsujimoto and Genovesio, 2017) and illustrated in Fig. 1A, the monkeys had to choose between a shift or stay strategy according to a presented cue stimulus and make a saccade toward the peripheral target. The trial started with the onset of a fixation point (a 0.6° filled white circle) at the center of the screen with two peripheral targets (2.0° unfilled white squares) located 11.6° to the right and left side of it. After a correctly executed 1.5 s fixation on the fixation point, a cue stimulus, indicating the strategy, appeared at the center of the screen for 0.5 s. For the visually cued task, a set of 4 cue stimuli was used: a vertical/horizontal gray rectangle (1.0° × 4.9°) and a yellow/ purple square (2.0° × 2.0°). After a correct cue-period fixation on the stimulus, the cue disappeared, and a delay period started (pseudorandomly 1.0, 1.25, or 1.5 s), where only the fixation point and the peripheral targets were present. When the fixation point disappeared, the monkeys were allowed to make a saccade toward one of the targets. The vertical rectangle and yellow square were associated with a “stay” strategy, requiring the selection of the same target that was chosen in the previous trial. Conversely, the horizontal rectangle and purple square were associated with a “shift” strategy, requiring the selection of a different target than the previous trial (Fig. 1B). A choice was always followed by the Fixation Cue Delay ‘’Go’’ Pre-Fb Post-Fb ‘filling’ of the two targets, where they became (1.5 s) (0.5 s) (1.0-1.5 s) (0.5 s) (0.5 s) solid white squares, and by a pre-feedback period of 0.5 s in which the monkeys had to remain fixated on the chosen target. Saccade Fixation Fb After the pre-feedback, a correct choice was rewarded with a 0.2 ml drop of fluid, rostral Visual whereas an incorrect choice was flagged lateral by red squares over both targets. Stay The number of alternative visual cues PS and responses (left or right) was approximately equal in each experimental sesShift sion. An inter-trial interval of 1 s followed AS the end of each trial. Any break in fixation during the pre-cue, cue, delay, and preFig. 1. (A) Sequence of task events. The dashed red lines show the monkey's gaze angle and fixation feedback periods caused the trial to be target (dashed red circle). Following a Go signal, the monkeys choose the right target with a saccade movement (red arrow). (B) Set of visual cues associated with the two strategies. (C) Recording area. aborted.

A

B

C

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Surgery and Anatomical Locations Both monkeys underwent surgery to implant a recording chamber (10.65 mm inner diameter) on the right Frontal Pole (Fig. 1C); detailed surgical procedures were described elsewhere (see Mitz et al., 2009). After the collection of data, both monkeys were anesthetized and then perfused through the heart with 10% (v/v) formol saline. We used the histological analysis and the structural MRI information to reconstruct the recording sites. Immediately before and during the perfusion, a pin was inserted through the center of the chamber. The penetration sites and tracks were reconstructed in Nissl-strained sections by reference to recovered electrolytic lesions and to the marking pins; all of this information has been described previously (Tsujimoto et al., 2010).

Experimental Design and Statistical Analysis During the task, eye movements were collected by an infrared oculometer (Arrington Research, Inc., Scottsdale, AZ) and single cell activity by 16 independent platinum–iridium electrodes (0.5–1.5 MΩ at 1 KHz; Thomas Recording, Giessen, Germany) guided through a multielectrode drive (Thomas Recording). We acquired neural signals from each electrode with a multichannel processor (Plexon, Dallas, TX), and then isolated spike activity online and sorted it offline relying on several criteria, such as principal component analysis (PCA), minimum inter-spike intervals, and close visual inspection of the entire waveform (Offline Sorter, Plexon).

Data Analysis Neuron Selection All of the neurons used in this work were recorded from the FPC area during the visually cued strategy task described before (see Tsujimoto et al., 2010 and Task in Materials and Methods section), that is the task in which we recorded enough neurons for the analysis. Error trials and correction trials after errors were excluded from the analysis. As first pre-selection, we performed a two-way ANOVA (factors: Goal left/right and Strategy shift/stay) and defined a subpopulation of goal-selective cells that showed the main effect of Goal during the feedback period (0.3 s before and 0.2 s after the feedback). Then we excluded all the neurons that were not suitable for the subsequent cross-correlation analysis from this pool, eliminating both neurons that were recorded alone in a session, and the few neurons were only co-recorded with other neurons isolated from the same electrode. As a control population, we selected neurons that were not goal-selective (NS) by the two-way ANOVA and recorded, when possible, in the same sessions of the goal-selective included in the analysis or (at the most) the same day in a different session.

Pair Construction All of the pairs were constructed from single units recorded simultaneously from different electrodes to prevent artifacts (Bar-Gad et al., 2001).

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To form pairs from the NS group, we counted how many goal-selective pairs were included in each of the sessions considered, and sampled the same number of NS pairs for each session. When this sampling was not possible (e.g. in session 1 there were 4 goal-selective pairs but only 2 NS pair), the NS pairs were sampled from a different session, but recorded in the same day (to complete the example before: to reach the same number of 4 goal-selective pairs, we added another 2 NS pairs that were recorded in a different session on the same day as the goal selective). As further control to our analysis, we also used these two pools to form mixed goal-NS pairs of neurons.

Single-Pair Analysis By dividing a trial of simultaneously recorded spike activities of two cells; cell 1 and cell 2, into multiple bins of time interval Δ, we can compute the Joint Peri Stimulus Time Histogram (JPSTH) of each pair., The JPSTH is defined (Aertsen et al., 1989) as the number of coincidences from neurons 1 and 2 in bin (uΔ, vΔ), given the occurrence of stimulus Z onset in bin (0,0). Let n(k)1 (u) be the activity of cell 1 in the u-th bin of the k-th trial and let 〈〉 represent averaging over k trials: the averaged response or PETH of a neuron 1 will be defined as 〈n1(u) 〉. The raw-JPSTH of n1 and n2 is computed as the cross product of the single PETHs: hn12 ðu; v Þi at the kth trial : nðkÞ 12 ðu; v Þ ¼ nðkÞ 1 ðuÞ  nðkÞ 2 ðv Þ To measure the synchronization, the contribution of firing rates is excluded by subtracting a predictor-JPSTH that represents the probability of accidentally correlated events that appear even when the two spike trains are statistically independent (Ito and Tsuji, 2000); this represents the expected correlations arising from non-simultaneous trial pairings. hraw  JPSTHðu; v Þ  predicted–JPSTHðu; v Þi ¼ hn12 ðu; v Þi−hn1 ðuÞihn2 ðv Þ i To compute JPSTHs for each pair we used FieldTrip, an open source MATLAB toolbox (Oostenveld et al., 2011). This tool uses as predicted-JPSTH a shift predictor computed in consecutive trials in a symmetric way. For example, the JPSTH is computed for n1 in trial k versus n2 in trial l, and at the same time for n1 in trial l versus n2 in trial k. This gives (nTrials-1)*2 JPSTH matrices for individual trials. Unlike the shuffling method, shifting both spike trains symmetrically results in a robust shift predictor which is less affected by slow changes in their temporal structure. (Oostenveld et al., 2011). The resulted subtracted-JPSTH was then normalized by dividing it with the cross product of the time dependent standard deviations (σ) of the neurons (Aertsen et al., 1989) to obtain a matrix of correlation coefficients.

normalized–JPSTH ¼

subtracted–JPSTH σ1ðn1Þ σ2ðn2Þ

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Briefly, in each trial, after a fixation of 1.5 s, a visual cue instructed either a stay (saccade to the same target chosen on the preceding trial) or a shift decision (saccade to the non-chosen target). Then a fixation throughout the cue period (0.5 s) and the delay period (1.0, 1.25 or 1.5 s, randomly selected on each trial) occurred before the fixation point disappeared, which triggered a saccade to one of the two targets. After a pre-feedback fixation period (0.5 s), a fluid reward was delivered. A typical FPC cell with goal selectivity (left vs. right) showed increased activity just before and after feedback, but only after the monkey's choice of one of the two targets (for details on the task and on the features of FPC neurons see Tsujimoto et al., 2010). Based on these results, we focused on the analysis from 0.5 s before feedback onset until 0.4 s afterward (FB epoch), taking 3 s from the start of the initial fixation as the control epoch: 1.5 s fixation + 0.5 s cue + 1 s minimum delay (FIX&DLY epoch). We sliced the two epochs into multiple 150-ms bins obtaining 2 different square panels (aligned at the two epochs FIX&DLY and FB, respectively) subdivided into tiny compartments of size Δ (150 ms). The matrix element assigned at each compartment, quantifies the probability of the occurrence of a joint event, where cell 1 has a spike event at bin u and cell 2 has one at bin v. A color scale represents the magnitude of each matrix element. At the bottom of the square, the PSTH of cell 1 is plotted upside down and PSTH of cell 2 from left to right. Such a broad time scale was chosen as a compromise because of the low firing rate; it was necessary to have enough cells with a sufficient number of spikes in each bin to perform a cross-correlation. Focusing on the minimum lag of 150 ms, we extracted the main diagonals (from the lower left) of the JPSTHs for each pair and used the coarse-coincidence histograms as described below for subsequent analysis and statistical tests. We transformed each value of the diagonals into a z value using Fisher's transformation: z¼

1 ð1 þ r Þ ln 2 ð1−r Þ

We computed the 95% confidence interval (C.I.) assuming that z is approximately normally distributed with Þ 1 pffiffiffiffiffiffi mean 12 ln ð1þr ð1−r Þ and standard deviation σ z ¼ n−3:     tan z−zα=2 σ z < r < tan z þ zα=2 σ z where n is trial number and zα/2 is 1.96 factor for a 95% C.I.. The total period of analysis (3.9 s) included 26 bins: we calculated a new zα/2 factor corrected for Bonferroni, and adjusted the significance level of the C.I. to 0.05/26 = 0.0019 (Tsujimoto et al., 2008). If at least one bin of 26 was significantly different from zero based on this criteria, the neuron pair was considered to have a significant correlation and identified as a FIX&DLY and/or FB significant neuron pair. In addition, the correlation was marked as positive, negative, or ambiguous (both positive

and negative) based on its nature. We found that the number of negatively (or ambiguously) correlated pairs was so low to be negligible, thus, we decided to limit the analysis to the positively correlated pairs.

RESULTS We analyzed the broad correlation of 76 pairs of goalselective neurons and 76 pairs of NS cells focusing on the main diagonal of their JPSTHs in the FIX&DLY and the FB epoch. To test whether these cells showed a specific change in correlations related to their goal coding function in the FB epoch, we compared the time course of correlations of the goal-selective pairs with that of the NS pairs.

FPC Populations and Pair Construction As described in the Material and Methods section, we selected the goal-selective neurons from a pool of 567 cells recorded during the visually cued strategy task. This database included 314 from Monkey 1 and 253 from Monkey 2. The two-way ANOVA (factors: Goal left/right and Strategy shift/stay) identified 123 neurons that showed a significant main effect of Goal during the feedback period (0.3 s before and 0.2 s after the feedback): 77 in Monkey 1 and 46 in Monkey 2, respectively. From this pool of goal-selective cells, we kept 81 neurons that were recorded across 29 sessions, removing those that were not suitable for cross-correlation analysis. On the other hand, NS neurons were selected from the 444 neurons that did not show the Goal effect. From this pool, we specified 101 cells as NS cells using the criteria described above. 76 pairs were constructed from goal-selective neurons, another 76 pairs were constructed from the NS group and an additional 201 mixed goal-NS pairs were formed from the two pools. On average, 76 ± 39 co-recorded trials were included in each pair.

Nature of Correlation and Pair Analysis We compared the number of significantly correlated pairs between the two groups. We considered a pair as significantly correlated if the coefficient of correlation was significantly different from zero (Bonferroni corrected) in at least one of the 26 bins (20 for FIX&DLY and 6 for FB epochs). The nature of the correlation was characterized by both the epoch in which a significant correlation was observed, and by its sign (positive or negative). In the goal-selective group, 36 of 76 pairs showed significant correlation: 32 of them were positive pairs (18 in FIX&DLY, 9 in FB, 5 both epochs), 3 of them were negative (2 FB, 1 both epochs), and 1 showed both positive and negative correlations in different bins (both epochs). For the NS group, 31 of 76 pairs showed significant correlation: 24 pairs were positive (23 in FIX&DLY and 1 in both epochs), 6 were negative (5 in FIX&DLY and 1 in FB), and only 1 showed both positive and negative correlations in FB epoch. In the mixed group 74 of 201 pairs showed significant correlations: 53 were positive pairs (40 in FIX&DLY, 8 in FB, 5 both epochs), 17 were negative (14 in FIX&DLY

Valentina Mione et al. / Neuroscience 404 (2019) 519–528

groups. In the FB period, the proportion of significant pairs in the NS group dropped to 1%, which was significantly smaller than that of the goal-selective pairs (χ 2 test, χ 2 = 12.5 p = 4.1*10 −4). Significant NS pairs found in the FB period were not only small in number (only 1%), but also maintained their correlation from the FIX&DLY. In the FIX&DLY period, on the other hand, the proportion of significant pairs did not differ between goal-selective and NS groups (30% vs 32%, respectively, χ 2 test, χ2 = 0.03 p = 0.86). Thus, in the FB period, the goal-selective pairs, but not the NS pairs, showed a significant positive correlation, while in the FIX&DLY epoch, the two groups had a comparable amount of pairs with significant positive correlation. These results did not change when the negatively correlated pairs, as well as the pairs with both positive and negative correlation, were considered (see Tables 1 and 2 and Fig. 2). Fig. 3A, B shows a population JPSTH averaged across JPSTHs of all pairs with a significant positive correlation in at least one of the two epochs. Even though we performed all the analyses exclusively during the two specific epochs, for this Figure, we calculated a larger period of ~ 4 s aligned to the FB onset and moving backwards to the start of the FIX&DLY epoch. As a confirmation of these results, the mixed goal-NS group showed a significant decrease in the proportion of positively correlated pairs in the FB compared to the FIX&DLY epoch (22% vs 7%, respectively, χ 2 test, χ2 = 20.6 p < 1*10 −5). Moreover, the only significant difference in the proportions of significant pairs across groups was found between the mixed group compared to the goalselective group in the FB epoch (respectively, 7% vs 18%, χ 2 test, χ 2 = 8.95 p < 2.7*10 −3, Table 3; all others across group comparisons - mixed vs NS for FIX&DLY and FB and mixed vs goal-selective for FIX&DLY epoch - showed a p > 0.05 on the same test).

Table 1. Statistics on positive pairs only

Only positive pairs

Goal-selective (n = 76)

NS (n = 76)

χ

FIX&DLY FB χ2 χ2 (p)

23 (30%) 14 (18%) 2.89 0.09

24 (32%) 1 (1%) 25.3 < 1*10−6

0.03 12.5

2

χ (P) 2

.86 4.1*10−4

Numbers and % of significant pairs considering only positive correlations.

Table 2. Statistics on all pairs

All significant pairs

Goal-selective (n = 76)

NS (n = 76)

χ2

χ2 (P)

FIX&DLY FB χ2 χ2 (p)

25 (33%) 18 (24%) 1.58 0.21

30 (39%) 3 (4%) 26.7 2.3*10−7

0.45 12.4

.50 4.2*10−4

Numbers and % of significant pairs considering every type of correlation.

and 3 in FB) and 4 showed both signs of correlations (2 in FIX&DLY and 2 in FB epoch). In the present analysis, we decided to include only positively correlated pairs. First, we tested whether the number of goal-selective correlated neuron pairs increased in the FB and we found that the number of significantly correlated pairs did not differ between the FIX&DLY (30%) and the FB period (18%) (χ 2 test, χ 2 = 2.89 p = 0.09). However, it is notable that it was only 5 of 32 pairs that had positive correlation in both of the two epochs. The majority of pairs showed significant correlation in either FIX&DLY or the FB period. Thus, the pairs with significant correlation changed from the FIX&DLY to the FB period, although the number of significant pairs did not differ. Secondly, we tested whether the number of significant pairs was different between the goal-selective and NS

pos

neg

both

Significant pairs (%)

Correlation Strength

* *

40

30

*

*

20

10

0 Goal-selective pairs (n=76)

NS pairs (n=76)

Fixation & Delay epoch

mixed pairs (n=201)

523

Goal-selective pairs (n=76)

NS pairs (n=76)

mixed pairs (n=201)

Feedback epoch

Fig. 2. Percentage of neuron pairs with positive, negative and ambiguous (both) correlations, based on the sign of the significant bins during the Fixation & Delay epoch and the Feedback epoch (FB).

Even though goal-selective pairs did not show a significant change in the proportion of correlated pairs from FIX&DLY to FB periods, it is possible that there was a change in correlation strength. To examine this possibility, Fig. 4A, B compared the mean correlation coefficient (averaged across bins' value) of the significant pairs between two epochs. We observed a significantly higher mean correlation value in the FB (n = 14; mean: 0.17 ± 0.05) than in the FIX&DLY (n = 23; mean: 0.06 ± 0.03) epoch (Fig. 4A, B; one-way ANOVA factor: EPOCH; F1,24 = 37.8; p = 2.4*10 −6) showing that goal-selective cells increased correlation when they were involved in the goal encoding. We did not perform the same analysis on the NS group because we found just 1 significant pair in the FB epoch.

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Valentina Mione et al. / Neuroscience 404 (2019) 519–528

pre-feedback

pre-feedback

B

rate, we tested the relationship between firing rate and correlations. The mean 0.1 firing rate in the FIX&DLY 0 0 was 4.3 ± 5.4 and 3.1 ± 4.6 -0.5 -0.5 spikes/s for goal-selective and NS pairs, respectively. -1 -1 This difference was sta-1.5 -1.5 tistically not significant 0 -2 -2 (Student's t-test, t181 = 1.52, p = 0.13). No significant dif-2.5 -2.5 ference was found in either -3 -3 the FB epoch between pair groups (4.3 ± 5.6 and 3.1 ± -3.5 -3.5 -0.08 4.5: t181 = 1.65, p = 0.1) -3.5 -3 -2.5 -2 -1.5 -1 -0.5 0 -3.5 -3 -2.5 -2 -1.5 -1 -0.5 0 or between the two epochs time from Feedback (sec) time from Feedback (sec) in the same group (goalFig. 3. (A) Population average of JPSTHs for 32 goal-selective pairs with significant correlation in at least one epoch selective: t160 = − 0.01, p = (FIX&DLY or FB) with 150 ms bin width of. (B) Population average of JPSTHs of 24 NS pairs with significant correla0.99; NS: t202 = 0.11, p = tion in at least one epoch (FIX&DLY or FB), 150 ms bin width. 0.90) when the same trials used in the correlation analysis were considered. Two example neurons with similar firing rates from both groups Table 3. Statistics on positive pairs only are shown in Fig. 5 during each of the two epochs analyzed. 2 2 Only positive GOAL-SELECTIVE mixed GOAL-NS pairs (n = 76) (n = 201)

FIX&DLY FB χ2 χ2 (p)

NS

time from Feedback (sec)

time from Feedback (sec)

Goal-selective

23 (30%) 14 (18%) 2.89 0.09

45 (22%) 13 (7%) 20,63 1*10−5

χ

χ (P)

1.88 .17 8.9 2.7*10−3

DISCUSSION

A

B

Correlation coefficient

Average correlation coefficient (per pair)

We have previously shown that FPC represents the behavioral goals only during the feedback time (Tsujimoto et al., Numbers and % of significant mixed goal-NS pairs vs goal-selective pairs. 2010, 2012) and not future or previous goals as with the lateral prefrontal cortex (Genovesio et al., 2006, 2008, 2012, Correlation Changes and Firing Rate 2014a, 2014b; Mushiake et al., 2006; Yamagata et al., 2012; Genovesio and Tsujimoto, 2014; Falcone et al., 2016; To rule out the possibility that the correlation-related results Marcos and Genovesio, 2016). described so far stemmed from the differences in firing Because of the properties of these neurons, we considered for the analyses only those neu0.3 0.3 goal-selective rons with goal-selective activity in the FB period. We studied 0.2 0.2 the network mechanism involved in the information processing of the FPC by examin0.1 0.1 ing the time course of broad correlations in two populations of cell pairs: goal-selective and 0 0 NS neuronal pairs. We used a typical normalized JPSTH and -0.1 -0.1 focused the analysis on the relaDelay Feedback Cue Fix tively loose synchrony across on on on a 150 ms window on the main Feedback Fixation Feedback diagonal. Fixation & Delay We found that the population epoch epoch & Delay epoch of goal-selective and NS neuepoch rons showed a similar correlation in the FIX&DLY periods Fig. 4. (A) Population average of coincidence histograms ± S.E. (diagonal width 150 ms) for significant goal-selective and that largely distinct subsets pairs that contribute either to the FIX&DLY epoch (N = 23) or to the FB epoch (N = 14). The plot on the left of goal-selective cells showed is aligned to the onset of fixation and the plot on the right is aligned to the feedback (−0.5 s before and 0.4 s after). significant correlation between (B) Mean correlation values for each goal-selective pair that contributes to the FIX&DLY or the FB epoch.

Valentina Mione et al. / Neuroscience 404 (2019) 519–528

A

525

spk/s

B

10

spk/s

Factor; FF) between goal-selective and NS cells in the same (but larger) FPC dataset (Tsujimoto and Genovesio, 2017). Comparing varia0 0 bility and correlation results, it appears that while the goalselective neurons presented lower variability than the NS pairs both after and before FB, no such difference 0 1 2 3 -0.5 0 0.5 was found between the two groups in terms of correlations before FB, Fixation & Delay (sec) Feedback (sec) i.e. FIX&DLY. The lower variability of the goal-selective cells compared 10 10 to the NS cells, observed before the neurons showed goal-selective activity prior to FB, has been interpreted 0 0 as an internal process reflecting the monkeys' own response in the absence of an external event (before feedback arrives). Considering the results on correlations, we show that the internal process in FPC 0 1 2 3 -0.5 0 0.5 revealed by the variability reduction Fixation & Delay (sec) Feedback (sec) is not related to changes in the pattern of correlations between goal Fig. 5. (A) Activity of one goal-selective neuron during fixation & delay epoch (left, aligned to the beginning of selective cells that showed a similar fixation) and feedback epoch (right, aligned to the feedback onset). (B) Activity of one NS neuron displayed as A. Raster displays show spike times with spike-density averages above. correlation as the NS cells before the FB epoch. This provides a general indication that the variability and correlations are two epochs. Comparing the two groups of cells, we found that distinct phenomena. while the profile of the correlations was similar in the two We have previously shown a stable temporal profile of the groups in the FIX&DLY epoch, it differed significantly in the FF in the population of goal-selective cells where the neuFB period, in which the proportion of correlated neuron pairs rons' FF was also consistent between fixation, cue, and feedbecome lower for NS cells than for the goal-selective group. back periods (Tsujimoto and Genovesio, 2017). Although at The same selective drop in the FB epoch, was confirmed the population level we observed a similar consistency of in the mixed goal-NS group and in its comparison with the the correlated neuron pairs across the task, the specific corpure goal-selective group. Furthermore, we observed an related neuron-pairs changed across the task in contrast to increase in the average correlation coefficient of the goalthe cell-by-cell persistence of the FF, described by selective cells in the FB period that is the only epoch when Tsujimoto and Genovesio (2017). the goal is encoded. The correlated firing of these functional populations of neurons can encode varying information. These so-called Functional Role of the Correlations cell assemblies have been reported to change their sizes Temporal correlations can be involved in different processes and functional connections dynamically, depending on (for an extended review see Salinas and Sejnowski, 2001 the task events being processed and on the distance and Cohen and Kohn, 2011.). A traditional view considers between the neurons. In the lateral PFC, synchronous the synchronous signals as an additional coding dimension firings were often described during retention epochs, which that enables the extraction of more information on a stimulus. suggests that they reflect the reverberating circuits retainThe brain also uses correlation structure to signal that an ing information in working memory (according to the cellexternal state has changed; for example, synchrony reflects assembly concept) (Sakurai and Takahashi, 2006). This the onset of a visual (Kohn and Smith, 2005) or an auditory interpretation is close to our finding regarding FPC groups stimulus even when the firing rate fails to do so (DeCharms of broadly correlated neurons changing size and connecand Merzenich, 1995). tions, proceeding from the working memory period (FIX&DLY Correlations can also shape the flow of information. Stuepoch) to the monitoring period of the FB epoch (Tsujimoto dies that focused on this aspect have investigated how correet al., 2010). lations are affected by changes in processes that are not Relation Between Correlation and Neural Variability linked to pure stimulus representation as attentional states and expectations (Riehle et al., 1997; Steinmetz et al., We will consider our results in the context of the recent findings 2000). In these studies, the spike trains are not just synchroon neural variability in FPC. We have previously described nized to an external event (like the onset of a stimulus), but differences in firing rate variability (measured by the Fano 10

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also pairs of neurons correlate with different strengths and in different epochs of the tasks, without the presence of a correspondent modulation in the firing rates. Even if we only consider loose correlation, our study can be considered in this dynamic framework, in which the correlated firing is tuned more on internal than external states. We hypothesized that it is possible that the principal role of these FPC goalselective cells is to keep track and to evaluate self-driven decisions most likely for retrospective monitoring, subservient to future scenarios (Tsujimoto et al., 2010, Tsujimoto et al., 2011, Tsujimoto and Genovesio, 2011). In this context, the main actors are the dynamic networks of differently tuned neurons that change their mutual relationships progressively with transition to different internal events. In the extended period of the task analyzed here, including fixation and delay, several external and internal events follow one another: the onset of fixation, the cue appearance, and the beginning of delay. In this extended period, a similar number of correlated pairs are recruited from both groups of goalselective and NS neurons (differentiated just by their mean firing rate). In an early epoch of the task, in which the representation of the rule was relevant, it is unclear whether the correlations in this wide network might contribute to the preparation for implementation of the rule because neurons were not yet modulated by the goal. Once the choice has been made, the transient connectivity with the downstream target areas needs to be reshaped with the transition to a new internal state after the goal is chosen in the FB epoch. Here, the changes depend on both task events and specific properties of the neurons: information that reflects the monitoring function could be transmitted more efficiently in a reconfigured network through new correlations of goal-selective pairs and the correlations of NS pairs should probably be turned down to enhance the contribution of the goal-selective pairs.

Interpretational Limitation and Conclusions While we could only assess broad correlations, previous studies have described different widths of temporal correlation that also coexist in the same population of neurons, suggesting a differentiated functional and structural meaning (Nelson et al., 1992; Nowak and Bullier, 2000). In particular, intermediate and broad correlations, ranging from 30 ms to hundreds of ms, were reported as a different phenomenon compared to ‘sharp’ correlation, < 10 ms. Previous works exploring functional connections across the cortex show a systematic dependence of the lag of peak on the cortical distance and tuning similarity, mostly associating the narrow synchrony with distances not greater than 0.2–0.3 mm between neurons receiving a monosynaptic shared input (Eggermont, 1992; Cardoso de Oliveira et al., 1997; Constantinidis and Goldman-Rakic, 2002; Smith and Kohn, 2008). On the other hand, the broad correlation was suggested to reflect slow and global modulations in synchrony changing, based on the behavioral state of the awake animal and most likely arising from assemblies of polysynaptic paths that converge on the neuron pair, making information transmission over long distances more efficient (Nelson et al., 1992; Nowak and

Bullier, 2000). The presence of slow covariations at timescales of hundreds of milliseconds has been identified in premotor cortex using a vibrotactile detection task also before the onset of a sensory stimulus, facilitating its detection as a result of common internal signals and the presentation of the stimulus later on (Carnevale et al., 2012). Considering that both low firing rates and the counting of spikes over short epochs (Cohen and Kohn, 2011) can lead to weak correlation, and, taking into account the peculiar low firing rate of this group of FPC neurons, we had to find a balance between these two factors and we decided to assess only broad correlation between neuron pairs. In addition, as we did not track the day-by-day spatial arrangement of the electrodes, we could not take into account the electrode distance in the analysis. Furthermore, we decided not to use neuron pairs that were recorded from the same channel to avoid possible artifacts. Because of these limitations, we could not analyze the relation between JPSTHs and electrode distance, and we could only hypothesize that the broad correlations might reflect multiple and possibly indirect polysynaptic pathways. In conclusion, similar to many other areas, in FPC, information is not only represented in individual cells, but could also spread across the neural population, even in the absence of firing modulation in individual neurons. This emergent coding dimension might be the cornerstone for the exceptional adaptability of the neural coding observed in higher-order cortices (Duncan, 2001; Rigotti et al., 2013).

AUTHORS' CONTRIBUTIONS S.T. conceived and designed the experiments. S.T. and A.G. performed the experiments. V.M., S.T., and A.G. analyzed the data and wrote the article.

COMPETING FINANCIAL INTEREST The authors declare no competing financial interests. Table 1. Numbers and % of significant pairs considering only positive correlations. Table 2: numbers and % of significant pairs considering every type of correlation. Table 3: numbers and % of significant mixed goal-NS pairs vs goal-selective pairs.

ACKNOWLEDGEMENTS The research was funded by the Division of Intramural Research of the National Institute of Mental Health (Z01MH01092) and in part by Grants-in-Aid from JSPS (15 K12049 and 26282218), ImPACT Program of Council for Science, Technology and Innovation (Cabinet Office, Government of Japan) and by the Italian Ministry of Research, “Fondo per gli investimenti della ricerca di base” FIRB 2010. We thank Dr. Steven P. Wise and Dr. Andrew R. Mitz for their support during all phases of this project and Mr. James Fellows and Ms. Ping-Yu Chen for technical support. We thank Andrew Emberton and Julie Fricke for constructive comments and suggestions.

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(Received 8 October 2018, Accepted 15 February 2019) (Available online 24 February 2019)