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• W3089121505 abstract "•The parietal cortex integrates a variety of sensorimotor inputs to guide reaching•GLM disentangled the effect of various reaching parameters upon cell activity•V6A neurons were not functionally clustered, but characterized by mixed selectivity•Spatial selectivity was dynamic and reached its peak during the movement phase The activity of neurons of the medial posterior parietal area V6A in macaque monkeys is modulated by many aspects of reach task. In the past, research was mostly focused on modulating the effect of single parameters upon the activity of V6A cells. Here, we used Generalized Linear Models (GLMs) to simultaneously test the contribution of several factors upon V6A cells during a fix-to-reach task. This approach resulted in the definition of a representative “functional fingerprint” for each neuron. We first studied how the features are distributed in the population. Our analysis highlighted the virtual absence of units strictly selective for only one factor and revealed that most cells are characterized by “mixed selectivity.” Then, exploiting our GLM framework, we investigated the dynamics of spatial parameters encoded within V6A. We found that the tuning is not static, but changed along the trial, indicating the sequential occurrence of visuospatial transformations helpful to guide arm movement. The activity of neurons of the medial posterior parietal area V6A in macaque monkeys is modulated by many aspects of reach task. In the past, research was mostly focused on modulating the effect of single parameters upon the activity of V6A cells. Here, we used Generalized Linear Models (GLMs) to simultaneously test the contribution of several factors upon V6A cells during a fix-to-reach task. This approach resulted in the definition of a representative “functional fingerprint” for each neuron. We first studied how the features are distributed in the population. Our analysis highlighted the virtual absence of units strictly selective for only one factor and revealed that most cells are characterized by “mixed selectivity.” Then, exploiting our GLM framework, we investigated the dynamics of spatial parameters encoded within V6A. We found that the tuning is not static, but changed along the trial, indicating the sequential occurrence of visuospatial transformations helpful to guide arm movement. The ability to integrate and filter in a flexible way the multitude of sensory stimuli coming from inside and outside the body relies on associative cortices. How these computations occur at single neuron and population levels is still an unsolved problem. It is well accepted that the information is distributed in neural networks, with single cells modulated by many parameters at the same time, in a non-linear way. Thus, the actual view is that neuronal activity is characterized by what has been called “mixed selectivity” (Johnston et al., 2019Johnston W.J. Palmer S.E. Freedman D.J. Nonlinear mixed selectivity supports reliable neural computation.PLoS Comput. Biol. 2019; 16: e1007544Crossref Scopus (7) Google Scholar; Parthasarathy et al., 2017Parthasarathy A. Herikstad R. Bong J.H. Medina F.S. Libedinsky C. Yen S.C. Mixed selectivity morphs population codes in prefrontal cortex.Nat. Neurosci. 2017; 20: 1770-1779Crossref PubMed Scopus (56) Google Scholar; Fusi et al., 2016Fusi S. Miller E.K. Rigotti M. Why neurons mix : high dimensionality for higher cognition.Curr. Opin. Neurobiol. 2016; 37: 66-74Crossref PubMed Scopus (195) Google Scholar; Rigotti et al., 2013Rigotti M. Barak O. Warden M.R. Wang X. Nathaniel D. Miller E.K. Fusi S. The importance of mixed selectivity in complex cognitive tasks.Nature. 2013; 497: 585-590Crossref PubMed Scopus (581) Google Scholar). According to this hypothesis, just a minority of cells would be strongly modulated by a specific factor, whereas most of them would code for combinations of different features. The associative cortices, like the posterior parietal cortex (PPC), include a variety of multimodal associative areas whose neurons are modulated by a plethora of different factors (Binkofski and Buccino, 2018Binkofski F. Buccino G. Section III. The Parietal Lobe and Brain Networks for Action and Perception.in: Vallar G. Coslett H.B. The Parietal Lobe, Volume 151. 1st ed. Elsevier, 2018: 467-480Crossref Scopus (4) Google Scholar, Galletti and Fattori, 2018Galletti C. Fattori P. The dorsal visual stream revisited: stable circuits or dynamic pathways?.Cortex. 2018; 98: 203-217Crossref PubMed Scopus (58) Google Scholar, Gamberini et al., 2020Gamberini M. Passarelli L. Fattori P. Galletti C. Structural connectivity and functional properties of the macaque superior parietal lobule.Brain Struct. Funct. 2020; 225: 1349-1367Crossref PubMed Scopus (17) Google Scholar). Within macaque PPC area V6A, located in the anterior bank of parieto-occipital sulcus, (Galletti et al., 1999Galletti C. Fattori P. Kutz D.F. Gamberini M. Brain location and visual topography of cortical area V6A in the macaque monkey.Eur. J. Neurosci. 1999; 11: 575-582Crossref PubMed Scopus (189) Google Scholar), is a higher-order visuomotor area modulated by visual signals coming from the whole visual field and somatic signals coming from the upper limbs (Gamberini et al., 2011Gamberini M. Galletti C. Bosco A. Breveglieri R. Fattori P. Is the medial posterior parietal area V6A a single functional area?.J. Neurosci. 2011; 31: 5145-5157Crossref PubMed Scopus (73) Google Scholar, Gamberini et al., 2018Gamberini M. Dal Bò G. Breveglieri R. Briganti S. Passarelli L. Fattori P. Galletti C. Sensory properties of the caudal aspect of the macaque’s superior parietal lobule.Brain Struct. Funct. 2018; 223: 1863-1879PubMed Google Scholar), as well as by oculomotor signals related to gaze position and eye movement (Galletti et al., 1995Galletti C. Battaglini P.P. Fattori P. Eye position influence on the parieto-occipital area PO (V6) of the macaque monkey.Eur. J. Neurosci. 1995; 7: 2486-2501Crossref PubMed Scopus (219) Google Scholar; Kutz et al., 2003Kutz D.F. Fattori P. Gamberini M. Breveglieri R. Galletti C. Early- and late-responding cells to saccadic eye movements in the cortical area V6A of macaque monkey.Exp. Brain Res. 2003; 149: 83-95Crossref PubMed Scopus (51) Google Scholar Breveglieri et al., 2012Breveglieri R. Hadjidimitrakis K. Bosco A. Sabatini S. Galletti C. Fattori P. Eye position encoding in three-dimensional space: integration of version and vergence signals in the medial posterior parietal cortex.J. Neurosci. 2012; 32: 159-169Crossref PubMed Scopus (37) Google Scholar). V6A neurons are also strongly modulated by reaching movements, both during action preparation and execution (Bosco et al., 2010Bosco A. Breveglieri R. Chinellato E. Galletti C. Fattori P. Reaching activity in the medial posterior parietal cortex of monkeys is modulated by visual feedback.J. Neurosci. 2010; 30: 14773-14785Crossref PubMed Scopus (38) Google Scholar, Bosco et al., 2015Bosco A. Breveglieri R. Reser D. Galletti C. Fattori P. Multiple representation of reaching space in the medial posterior parietal area V6A.Cereb. Cortex. 2015; 25: 1654-1667Crossref PubMed Scopus (20) Google Scholar, Bosco et al., 2016Bosco A. Breveglieri R. Hadjidimitrakis K. Galletti C. Fattori P. Reference frames for reaching when decoupling eye and target position in depth and direction.Sci. Rep. 2016; 6: 21646Crossref PubMed Scopus (18) Google Scholar; Fattori et al., 2001Fattori P. Gamberini M. Kutz D.F. Galletti C. “Arm-reaching” neurons in the parietal area V6A of the macaque monkey.Eur. J. Neurosci. 2001; 13: 2309-2313Crossref PubMed Google Scholar, Fattori et al., 2005Fattori P. Kutz D.F. Breveglieri R. Marzocchi N. Galletti C. Spatial tuning of reaching activity in the medial parieto-occipital cortex (area V6A) of macaque monkey.Eur. J. Neurosci. 2005; 22: 956-972Crossref PubMed Scopus (124) Google Scholar, Fattori et al., 2009Fattori P. Breveglieri R. Marzocchi N. Filippini D. Bosco A. Galletti C. Hand orientation during reach-to-grasp movements modulates neuronal activity in the medial posterior parietal area V6A.J. Neurosci. 2009; 29: 1928-1936Crossref PubMed Scopus (113) Google Scholar; Hadjidimitrakis et al., 2017Hadjidimitrakis K. Bertozzi F. Breveglieri R. Galletti C. Fattori P. Temporal stability of reference frames in monkey area V6A during a reaching task in 3D space.Brain Struct. Funct. 2017; 222: 1959-1970Crossref PubMed Scopus (11) Google Scholar; Santandrea et al., 2018Santandrea E. Brev R. Bosco A. Galletti C. Fattori P. Preparatory activity for purposeful arm movements in the dorsomedial parietal area V6A : beyond the online guidance of movement.Sci. Rep. 2018; 8: 6926Crossref PubMed Scopus (5) Google Scholar) and attentional shifts (Galletti et al., 2010Galletti C. Breveglieri R. Lappe M. Bosco A. Ciavarro M. Fattori P. Covert shift of attention modulates the ongoing neural activity in a reaching area of the macaque dorsomedial visual stream.PLoS One. 2010; 5: e15078Crossref PubMed Scopus (36) Google Scholar, see also Fattori et al., 2017Fattori P. Breveglieri R. Bosco A. Gamberini M. Galletti C. Vision for prehension in the medial parietal cortex.Cereb. Cortex. 2017; 27: 1149-1163PubMed Google Scholar; Galletti and Fattori, 2018Galletti C. Fattori P. The dorsal visual stream revisited: stable circuits or dynamic pathways?.Cortex. 2018; 98: 203-217Crossref PubMed Scopus (58) Google Scholar for reviews). In addition to single parameters, in some of the previous V6A studies the influence of pairwise parameters has also been studied, as for instance, target and gaze positions (Marzocchi et al., 2008Marzocchi N. Breveglieri R. Galletti C. Fattori P. Reaching activity in parietal area V6A of macaque: eye influence on arm activity or retinocentric coding of reaching movements?.Eur. J. Neurosci. 2008; 27: 775-789Crossref PubMed Scopus (82) Google Scholar), target position and visual condition (Bosco et al., 2010Bosco A. Breveglieri R. Chinellato E. Galletti C. Fattori P. Reaching activity in the medial posterior parietal cortex of monkeys is modulated by visual feedback.J. Neurosci. 2010; 30: 14773-14785Crossref PubMed Scopus (38) Google Scholar), target and initial hand positions (Hadjidimitrakis et al., 2017Hadjidimitrakis K. Bertozzi F. Breveglieri R. Galletti C. Fattori P. Temporal stability of reference frames in monkey area V6A during a reaching task in 3D space.Brain Struct. Funct. 2017; 222: 1959-1970Crossref PubMed Scopus (11) Google Scholar), etc. Recently, Bosco et al., 2019Bosco A. Breveglieri R. Filippini M. Galletti C. Fattori P. Reduced neural representation of arm/hand actions in the medial posterior parietal cortex.Sci. Rep. 2019; 9: 936Crossref PubMed Scopus (3) Google Scholar used a novel dimensionality reduction method (dPCA) to extract from V6A information about a number of different modulating signals (visual condition, target position, wrist orientation, grip type), obtaining a compact view of the population activity. However, the authors did not address the question of whether all parameters were encoded in single cells, in separate subpopulations, or were distributed through the whole network. To our knowledge, no comprehensive work has yet tried to correlate V6A single-cell activity with a full set of modulating variables concurrently acting upon the same neuron during the execution of the same task. Because of the multimodal nature of V6A, we expected that its neurons are characterized by “mixed selectivity.” Moreover, given the sensorimotor transformations occurring in this area, we also expected that the influence of spatial parameters changes over time. To test our hypotheses, we first uncoupled different parameter contributions at single-cell level, and then we moved to population level to study the dynamics of these contributions. Among the mathematical probabilistic models, the family of Generalized Linear Models (GLM) offers a valid statistical framework to check how a variable of interest (i.e., the neural activity) is explained by several features or “regressors.” As the Poisson distribution is the most appropriate to model single-cell activity (see Transparent Methods), we used a Poisson GLM to study the effect on neuronal activity of the many factors that are known to modulate V6A. We collected spiking activity of V6A neurons and gaze positions during a delayed reaching task, and then fitted neural activity of a single cell with eye-related and reaching-related parameters. Our analysis allowed us to represent activity modulations of each cell with a set of weights that we named “functional fingerprint.” Across the population, units were not clustered in homogeneous groups according to their fingerprints, but were distributed in a functional continuum, suggesting that V6A is characterized by “mixed selectivity.” Influences of spatial parameters were not static but changed through space and time, indicating the occurrence of visuospatial transformations helpful to guide movement. Single-cell activity was extracellularly recorded from area V6A (see Figure 1A) while two monkeys (M1 and M2) performed an instructed delay reaching task toward nine targets placed at eye level (Figure 1B). The monkey pressed a home button (HB) to begin the trial. As illustrated in the temporal sequence in Figure 1C, one of the nine LEDs lit up green and the monkey had to fixate it. After a variable delay, when the LED changed color from green to red, the animal was required to perform a hand movement to reach and press the LED and hold it until the LED turned off. After that, the animal returned the hand to the initial position, pressed the HB to receive liquid reward, and a new trial began. Our dataset consisted of cells that were recorded for 10 correct trials for each of the 9 targets (N = 181; 64 from M1, 117 from M2; see Figure S1 for the recording sites). To reduce sources of noise we performed a 1-way ANOVA (factor: epoch; levels: 8) on mean firing rates within epochs of interest (for more details see Transparent Methods) to identify task-related cells. The entire neural population resulted modulated, i.e., the mean firing rates differed significantly for at least one epoch, with no exceptions. Furthermore, we selected the cells according to the goodness-of-fit of our model (see below). Spikes were counted in 40 ms bins, and all trials were concatenated tip to tail in a unique vector for each cell that represented the dependent variable. In the GLM context a dependent variable is explained with a combination of independent variables called “regressors” or “features” in this article (Figure 1D). Each independent variable was a vector of the same length of the dependent variable. The regressors were organized in blocks. EYE POSITION block contained information about the average gaze position discretized in a 3D grid for each bin (see Transparent Methods); EYE SPEED/DIR block contained information about the velocity and the direction of eye movements; POSTSACC, FIX, PREP, PREMOV, MOV, HOLD, PREMOV2, and MOV2 were blocks that contained spatiotemporal information about the sequence of behavioral epochs occurring along the task (see Figure 1C for the temporal sequence of the task and Transparent Methods for details). We refer to these blocks of regressors as “extrinsic.” The SPIKEHISTORY block contained information about firing activity of the neuron in the previous 200 ms. We refer to it as “intrinsic” block. For each cell, we first selected the most relevant regressors with LASSO regularization technique. This first step led us to retain on average 90 ± 20 (standard deviation) regressors over ≃150 across the entire population (non-zero beta coefficients estimated by LASSO). Then, for each cell, we built several Poisson GLMs: a complete model that included all the selected regressors, 10 nested models in which we removed each time a different block of extrinsic regressors, 1 nested model removing the intrinsic block of regressors (extrinsic-only model), and 1 nested model removing all the extrinsic blocks of regressors (intrinsic-only model). All the models were cross-validated, and reported results are relative to the test datasets never seen by the no-LASSO-regularized models during the fitting (see Transparent Methods). To discard the units for which the model failed to capture neural modulations, we chose to select only the cells for which pseudo-R2 of the complete model reached at least 0.05, a criterion used in literature (Goodman et al., 2019Goodman J.M. Tabot G.A. Lee A.S. Suresh A.K. Rajan A.T. Hatsopoulos N.G. Bensmania S. Postural representations of the hand in the primate sensorimotor cortex article postural representations of the hand in the primate sensorimotor cortex.Neuron. 2019; 104: 1000-1009.e7Abstract Full Text Full Text PDF PubMed Scopus (8) Google Scholar; see Transparent Methods). As a result, 50/64 (78%) M1 units and 65/117 (56%) M2 units met this criterion and were retained for further analyses. For selected cells, pseudo-R2 was 0.102 [0.073, 0.147] (median [25th, 75th percentile]). To evaluate the influence of the different parts of our model (blocks of regressors) on neural activity, we first calculated for each nested model a relative pseudo-R2 that compared its goodness of fit with the complete model goodness of fit. We then adopted a metric that we called “w-value” (1 – relative pseudo-R2) directly proportional to the weight (i.e., the importance) of each block of regressors on the fitting of the complete model. Each w-value ranged from 0 (no influence of the removed block of regressors on spiking activity) to 1 (greatest influence of the block of regressors; see Transparent Methods for further details). The set of the 10 extrinsic w-values functionally qualifies each cell and represents what we called “functional fingerprint”. Figure 2 shows data of two different example neurons (A, B). The column plots in the bottom part of the figure represent the “functional fingerprint” of cells shown in A and B, respectively. The first neuron (Figure 2A) showed strong changes in the discharge during the PREMOV and MOV epochs (light and dark blue, Figure 2A top), whereas during the other time epochs little or no effects were observed. In fact, the firing rate (dashed line) suddenly increased in PREMOV (just before the onset of arm movement) and remained high throughout the whole movement phase. A few motor-related parameters (linked to PREMOV and MOV epochs) explained most of cell modulation (highest columns, Figure 2A bottom). In the column plot, we marked with asterisks the blocks that resulted the most important to explain the neural activity from our analysis (see Transparent Methods). The neuron reported in Figure 2B had more complex modulations. It showed a clear preference for far positions and a peak in the activity when the animal reached them. Indeed, starting from the beginning of fixation, cell discharge was remarkably higher for far positions than for the others. Moreover, for far positions, it increased just before the movement onset (PREMOV) and reached a peak after it (MOV). On the contrary, for intermediate positions the neural activity was generally low, and even lower for near positions. Interestingly, during the epochs of major activity for far positions (PREMOV and MOV), the neuron was almost completely inhibited when the animal gazed and reached near positions. The “functional fingerprint” (Figure 2B bottom) shows the mixed selectivity of this cell, with six regressor blocks that significantly influence cell discharge (mainly PREMOV, MOV, HOLD, but also EYE POSITION, PREP, and MOV2). It is worth noting that the “functional fingerprint” does not provide information about the spatial tuning of the unit, because it accounts for the overall influence of different parameters upon cell discharge (averaging the effect of parameters in different spatial positions) rather than accounting for changes in cell discharge according to the spatial position. Indeed, the functional fingerprint of cell in Figure 2A is much more selective (only a few parameters do influence cell discharge) than the one of cell in Figure 2B, even if the spatial selectivity of cell in Figure 2B is strikingly higher than the spatial selectivity of cell in Figure 2A. To understand how the functional properties were encoded in the V6A population, we further analyzed the results obtained cell by cell at population level. As described above, for each recorded neuron, we calculated 10 w-values, one for each of the extrinsic blocks of regressors. We first checked whether these w-values across the population were consistent between the two animals (Figures 3A and 3B ; medians and intervals in Table S1). Despite some differences for single blocks (such as PREMOV that was higher in M1 than M2 or MOV and HOLD values that were slightly lower), the overall distribution of the median values of each block of regressors was consistent (Kolmogorov-Smirnov test, p = 0.6751). Figure 3A shows that in both animals the extrinsic regressors with higher w-values were EYE POSITION, MOV, and HOLD (median values for M1 + M2: 0.018, 0.015, and 0.016 respectively). Data of M1 and M2 pooled together are shown in Figure S2. The distribution of w-values sorted separately for each block of regressors showed that just a few cells in both animals had very high w-value, whereas the remaining cells (the large majority) showed gradually descending values (Figure 3B). It is possible to identify an elbow that splits the curve in two parts (see Transparent Methods). On average, the dividing point corresponded to the 90% ± 5% of the population (mean and standard deviation across all blocks and the two animals). In other words, focusing on each regressor, only a few cells (10%) were well modulated by that parameter, whereas most of the cells (the remaining 90%) showed mild or low modulations. Because each cell is described with a unique vector of w-values (functional fingerprint), to visualize this multidimensional data and investigate whether a functional structure does exist, we performed a principal-component analysis (PCA) on the extrinsic w-values (Figure 3C). No definite clustering of the data emerged from this analysis: the majority of cells were grouped together, with some outlier, sparse units. We tried to apply the most common clustering techniques (such as K-means and hierarchical clustering; see Transparent Methods), but any attempt failed to identify a segregation in our data. To visually check whether there was a trend within the population, we colored in Figure 3C the dots according to cell modulations. For graphical purposes, we chose only the three main blocks of regressors (EYE POSITION, MOV, HOLD). Given the three pure colors associated with each of the three regressors (black, blue, and purple, respectively, see color code in Figure 3), each neuron (dot) was colored with a mixture of these colors based on its series of w-values (i.e., with a linear combination of colors weighed with the three w-values). The resulting colors of the outlier cells were similar to three pure ones, and thus they were the most selective for these parameters, whereas the majority of cells showed a combination of the three colors. For instance, the unit of Figure 2A, with high w-values for PREMOV and MOV, is located outside the central cloud of points in Figure 3C (bull's-eye indicated by filled arrow) and almost purely blue. On the contrary, the unit of Figure 2B is located in the central part of the principal components' (PCs') space and is colored according to its “mixed modulation” nature (Figure 3C, bull's-eye indicated by empty arrow). The gradual transition from one color to another across the dots is in accordance with the continuum of the w-values distributions (see Figure 3B). To study cell selectivity for one block of regressors (versus non-selectivity, i.e., mixed selectivity), beyond the three main ones considered in Figure 3C, we computed how many blocks really matter for explaining the neural activity. Because of the variability both in the distribution and in the absolute values in the functional fingerprints, we adopted an adaptive threshold that took into account the cell total sum of w-values (see Transparent Methods). This procedure allowed us to calculate that, on average, that each cell was significantly modulated by 4.2 (±1.3) blocks of regressors (mean and standard deviation). Figure 3D shows the distribution across the entire population. Units on the left of the histogram were more selective (less parameters were needed to explain neural modulations), whereas units on the right were characterized by more complex and mixed modulations. In accordance with what has been already discussed, the example units of Figure 2 fell in opposite parts of the histogram: indeed, the cell of Figure 2A (filled arrow), more selective, with three modulating blocks of regressors, is in the left part; the cell of Figure 2B (empty arrow), with six modulating blocks, in the right part. As we partially expected, although V6A cells typically showed mixed selectivity, we never observed a “totally mixed” selectivity (i.e., cells with 8–9 important blocks). Up to here, we focused on the neural modulations explained by each block of regressors. However, our population analysis based on w-values did not give any direct indications about the spatial tuning, although it is known that area V6A is strongly influenced by direction and depth during reaching task (Fattori et al., 2017Fattori P. Breveglieri R. Bosco A. Gamberini M. Galletti C. Vision for prehension in the medial parietal cortex.Cereb. Cortex. 2017; 27: 1149-1163PubMed Google Scholar, Fattori et al., 2005Fattori P. Kutz D.F. Breveglieri R. Marzocchi N. Galletti C. Spatial tuning of reaching activity in the medial parieto-occipital cortex (area V6A) of macaque monkey.Eur. J. Neurosci. 2005; 22: 956-972Crossref PubMed Scopus (124) Google Scholar; Hadjidimitrakis et al., 2014Hadjidimitrakis K. Bertozzi F. Breveglieri R. Bosco A. Galletti C. Fattori P. Common neural substrate for processing depth and direction signals for reaching in the monkey medial posterior parietal cortex.Cereb. Cortex. 2014; 24: 1645-1657Crossref PubMed Scopus (35) Google Scholar, Hadjidimitrakis et al., 2019Hadjidimitrakis K. Bakola S. Wong Y.T. Hagan M.A. Mixed spatial and movement representations in the primate posterior parietal cortex.Front. Neural Circuits. 2019; 13: 15Crossref PubMed Scopus (17) Google Scholar; Battaglia-Mayer et al., 2000Battaglia-Mayer A. Ferraina S. Mitsuda T. Marconi B. Genovesio A. Onorati P. Lacquaniti F. Caminiti R. Early coding of reaching in the parietooccipital cortex.J. Neurophysiol. 2000; 83: 2374-2391Crossref PubMed Scopus (129) Google Scholar). To study the dynamics (or the lack of) of the spatial selectivity at population level, we extracted information about changes in the beta coefficients (i.e., the modulations captured by the model) performing a correlation analysis, similarly to recent studies (e.g., Zhang et al., 2017Zhang C.Y. Aflalo T. Revechkis B. Rosario E.R. Ouellette D. Pouratian N. Andersen R.A. Partially mixed selectivity in human posterior parietal association cortex.Neuron. 2017; 95: 697-708.e4Abstract Full Text Full Text PDF PubMed Scopus (34) Google Scholar). Population activity for each target position and for each epoch can be expressed by a vector of beta coefficients extracted from the GL (generalized linear) complete models (one beta for each cell). In the graphs of Figures 4A and 4B , each node represents a target position. The colors of the edges represent the strength of the correlation between the two beta vectors of two different positions within the same epoch (Figure 4A) or between the two beta vectors of the same position across two subsequent epochs (Figure 4B). For a simpler interpretation, these results are averaged and summarized by the plots in Figures 4C and 4D. Figure 4A shows the correlations between two different positions within the same epoch. The three panels (DELAY, MOV, and HOLD) display the three epochs that resulted to be the most important ones after merging the data of the two animals (highest median w-values, see Figure S2/Table S1 and, for the other epochs, Figure S3). Moreover, for graphical purposes, we split all the pairwise correlations (36 edges) in two graphs for each epoch based on the physical distance between the linked nodes (top: short distance ≤ 10 cm, bottom: long distance >10 cm). We found differences among these epochs: HOLD was characterized by correlation coefficients generally higher than the others (0.58 ± 0.12, mean ± standard deviation), MOV had lower values (0.14 ± 0.25), and DELAY showed an intermediate strength in correlation (0.33 ± 0.15). Thus, the strength of spatial correlations (i.e., how similar population activity is between two different targets) varied along the trial. These fluctuations are shown in Figure 4C. In the figure, the plot represents the averaged correlation for all epochs (the three presented in Figure 4A and the remaining, presented in Figure S3). The peak of the plot corresponds to HOLD epoch, confirming what we already observed in Figure 4A (higher correlations). It is noteworthy that, within each epoch, there are huge differences in the strength of correlations depending on the physical distance of the two linked nodes (targets). Indeed, for all the three epochs in Figure 4A, within the same panel (epoch), bottom graphs (higher distances) show lower correlations than the upper ones (lower distances). This decrease in correlation strength with the increase of distance is highly significant (linear correlation, r = [-0.78; −0.91; −0.70], p ≃ [10−8; 10−14; 10−6] for DELAY, MOV and HOLD respectively; we observed the same effect in the remaining epochs, data not shown). To study the changes in regression coeffici" @default.
• W3089121505 created "2020-10-01" @default.
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• W3089121505 date "2020-10-01" @default.
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• W3089121505 title "Mixed Selectivity in Macaque Medial Parietal Cortex during Eye-Hand Reaching" @default.
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