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# Distinct connectivity patterns across striatal cell types and projections from sensory cortices

Juliette Contadini, Elodie Fino, Ingrid Bureau

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Juliette Contadini, Elodie Fino, Ingrid Bureau. Distinct connectivity patterns across striatal cell types and projections from sensory cortices. 2026. hal-05483713

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# Distinct connectivity patterns across striatal cell types and projections from sensory cortices

Juliette Contadini¹, Elodie Fino¹*, Ingrid Bureau¹*

¹Aix Marseille University, INSERM U 1249, INMED, Marseille, France.

* These authors contributed equally to this work

## Abstract

Striatal interneurons control the activity of spiny projection neurons (SPN), the principal output neurons of the striatum. Among these interneurons, parvalbumin-expressing (PV) and somatostatin-expressing (SOM) cells exhibit markedly distinct electrophysiological and anatomical properties, suggesting differential integration into striatal circuits and distinct modes of feedforward inhibition onto SPNs. However, whether such differences already emerge at the level of corticostriatal connectivity and whether they influence interactions with cortical inputs to SPN remain unknown. Here, we investigated the spatial organization of the projections from two sensory areas, the primary (S1) and secondary (S2) somatosensory cortices, onto PV and SOM interneurons and SPN in the dorsolateral striatum. Using a combination of retrograde anatomical tracing and functional circuit mapping, we characterized key features of corticostriatal connectivity, including input cortical origins, density, convergence, and redundancy. We found divergent connectivity patterns in both density and spatial organization, with the strongest differences observed between interneuron cell types and across cortical areas. Our findings suggest two potential modes of interaction with SPN: one in which PV interneurons receive broad band S1 inputs that densely overlap with those of large SPN populations, and another in which SOM interneurons integrate patchy, more heterogeneous cortical inputs across S1 and S2 that may shape SPN integration through cross-interactions. These cell-type and area-specific principles of corticostriatal wiring may reveal distinct modes of striatal computation.
INTRODUCTION

Striatal function critically depends on a small but diverse population of interneurons. These exert powerful control over the spiny projection neurons (SPN) and have been implicated in the genesis of striatal activity underlying motor learning, automatized behaviors, and goal-directed action sequences (O'Hare et al. 2016; Martiros et al. 2018; Lee and Reeve 2017; Rotariu et al. 2025). Among striatal interneurons, the parvalbumin-expressing (PV) and somatostatin-expressing (SOM) interneurons have rather distinct electrophysiological and anatomical features, leading to different feed-forward inhibition onto SPNs. More precisely, PV cells display fast-spiking properties (Kawaguchi 1993; Koós and Tepper 1999; Tepper and Plenz 2006) and exert region-specific modulation of SPN activity (Fino et al. 2018). Anatomically, they are particularly numerous in the dorsolateral striatum (DLS) integrating sensorimotor inputs (Gerfen 1985; Fino et al. 2018) and they present both dense and compact dendrites, harboring more and stronger cortical synapses and local axonal arborization than other striatal neurons, (Kawaguchi 1993; Koós and Tepper 1999; Tepper and Bolam 2004). PV-mediated feed-forward inhibition of SPN is strong, tightly controlling the timing and amplitude of their firing upon cortical inputs (Koós and Tepper 1999; Tepper et al. 2018). On the other hand, SOM interneurons are electrically compact and more excitable than PV, identifying them as potent modulators of striatal activity (Kawaguchi 1993; Tepper et al. 2010; Rotariu et al. 2025). While being more homogeneously distributed, region-specific excitability leads to a weaker weight on SPN in DLS (Fino et al. 2018). Both their dendritic and axonal arbors are sparse and span a larger striatal territory, extending approximately twice as far as those of PV (DiFiglia and Aronin 1982; Kawaguchi 1993; Kubota and Kawaguchi 2000; Tepper et al. 2010). This suggests that SOM might form different networks compared to PV, perhaps reaching out to more distant pre- and postsynaptic cells. Overall, these distinct properties suggest that PV and SOM interneurons may be differently integrated into striatal networks and understanding the logic of their wiring could be central to assess their respective role in controlling striatal output.

We hypothesize that wiring differences between PV and SOM may already emerge at the
level of projections connecting the sensorimotor cortical areas to the DLS. While cortical
stimulations have provided initial evidence for such differences in projection strength, with
PV receiving stronger inputs than SOM (Johansson and Silberberg 2020), key features of
connectivity patterns, such as convergence and laminar origins, remain uncharacterized.
Importantly, it is unknown whether projections to interneurons and SPN are similarly
organized. Yet, the degree of their overlap is an essential issue for understanding the
function of feedforward inhibition. A first characterization of SPN connectivity pattern in the
primary somatosensory cortex (S1) showed that, although projections from multiple cortical
columns converged onto the same region of DLS, individual SPN received largely distinct
sets of cortical inputs, even when closely positioned in the DLS (Amroune et al. 2025). One
may hypothesize that broad cortical inputs to interneurons, intersecting with those of many
SPN, would enable scaled and temporally precise feedforward inhibition, thereby promoting
the formation of functionally coherent assemblies. Conversely, non-overlapping
connectivities could support other striatal computations, based on the interaction between
distinct cortical inputs to interneurons and SPN. Finally, how generalizable these connectivity
patterns are across cortical areas is also a determinant question (Wall et al. 2013; Assous
and Tepper 2019; Johansson and Silberberg 2020). Differences in selectivity and perceptual
representations precision between S1 and the secondary somatosensory cortex (S2)
(Carvell and Simons 1986; Kwegyir-Afful and Keller 2004; Hubatz et al. 2020; El-Boustani et al. 2020) may shape specific corticostriatal projections and cell pattern interactions in the DLS.

To test these hypotheses, we investigated the spatial organization of S1 and S2 projections onto DLS PV, SOM and SPN using a combination of anatomical retrograde tracing and functional mapping approaches. We found that PV and SOM anatomical and functional innervations differed both in density and shape. Most pronounced differences were observed in projections from S1. These differences suggest 2 potential modes of interactions with SPN: One, in which PV S1 inputs overlap with those received by a large population of SPN and the second in which both S1 and S2 inputs to SOM, but also PV S2 inputs, may shape SPN integration by conveying inputs of different spectrum.

## RESULTS

In order to map cortical projections to individual interneurons, we performed whole cell patch-clamp recordings in the DLS and laser scanning photostimulation (LSPS) with glutamate uncaging in the cortex to stimulate potential presynaptic cells (Amroune et al. 2025). In corticostriatal slices prepared from PV-cre::Ai14 mice and SST-cre::Ai14 mice, mCherry-labeled PV-expressing or SOM-expressing cells were recorded in voltage-clamp configuration while LSPS was performed in the barrel field of S1 (2.1 × 1.1 mm, 75 µm spacing; Methods). Evoked EPSC amplitudes were reported in a grid to visualize the origin and synaptic strength of excitatory projections to individual interneurons (Fig. 1B). From this grid was then generated a connectivity map reporting the position of connecting sites in the barrel field (Fig. 1C,D). We observed more of these sites for PV interneurons, over 2 fold compared to SOM (17 ± 2 vs. 6.7 ± 1.8; n = 38 and 15 respectively; Fig. 1E). In a subset of experiments, recordings of SPN were performed to allow direct pair comparisons. These revealed interneuron-specific relationships with SPN connectivity, as PV but not SOM connecting sites were significantly more frequent compared to SPN, over 2 fold (Fig. 1E, F; Wilcoxon, PV-SPN, p < 0.001, n = 19; SOM-SPN, p = 0.25, n = 5). Evoked EPSCs from unitary connections (Methods) had larger amplitudes in PV interneurons compared to SOM ((Johansson and Silberberg 2020; Mallet et al. 2005), Fig. 1G,H; Kruskal-Wallis p < 0.001), whereas they differed in kinetics between interneurons and SPN, the former typically displaying shorter decay times (Gerfen 1985; Fino et al. 2018; Kawaguchi 1993; Straub et al. 2016), Fig. 1G,H; Kruskal-Wallis p=0.0185). These results suggest that PV interneurons dominated SOM and SPN cells both in the number and strength of corticostriatal connections (Johansson and Silberberg 2020; Mallet et al. 2005).

The shape of connectivity maps varied across recordings, often consisting of clusters and gaps of various lengths (Fig. 1B,C). This prompted us to ask whether connectivity patterns distinguished striatal cell populations. Indeed, different organizations in the horizontal or vertical axis may underlie distinct selectivities for cortical columns or layers, respectively. Based on the horizontal positions of connecting sites, we measured for each cell the number of clusters, of cortical columns, and the width of the entire cortical stretch with inputs, termed input field hereafter (Fig. 2A; see Methods). The number of innervating cortical layers characterized the vertical organization of connectivity maps. Canonical variate analysis, visualizing group separation in the multivariate space, showed that, despite variability, connectivity patterns differed significantly across the 3 cell types, PV, SOM and SPN (permanova, pseudo-F = 14.8, *p* = 0.0005; Fig. 2B; Methods). Strongest contributors were
the numbers of connecting sites, layers and cortical columns (Fig. 2C), suggesting that the
overall density of inputs, both in the horizontal and vertical axis of the input field, had great
weight over group separation. Thus, PV connectivity maps were distinctively denser, with
more layers and cortical columns than in the connectivity maps of SOM and SPN (Fig. 2A).
This separation was further captured in the occupancy of connecting sites per micron of
input field in the barrel field, which showed a steeper slope for PV and overlapping
correlations for SOM and SPN (Fig. 2D; *r* differences: PV-SOM, *p* = 0.018; PV-SPN, *p* <
0.001). In contrast, the lower weight of cluster numbers over group separation was
consistent with the stable distribution of this parameter across cell types (Fig. 2A). This was
in spite of PV cells having more connecting sites suggesting, that organization in small
aggregates was less a feature of their connectivity maps. Indeed, clusters were 333 ± 40 µm
long in PV connectivity maps, against 153 ± 17 and 164 ± 9 µm in SOM and SPN (*p* <
0.001; *PV-SOM* and *PV-SPN*, *p* < 0.001; *Kruskal-Wallis and Dunn-Sidak*). Altogether, these
results indicate similar patchy organization of individual neuron innervation in SOM and SPN
populations and both larger and fuller input fields for PV interneurons along the cortical
horizontal axis.

The number of innervating cortical layers being one determinant of cell group separation, we further studied the connectivity maps along their vertical axis. Like for SPN, L5 dominated the inputs of both PV and SOM interneurons. However, connecting sites were ~4 fold denser in L5 for PV compared to SOM (Fig. 2E; one-way anova, *p* < 0.001). This could not be predicted from the likelihood of these interneurons being connected to layer 5, which was 90 % for PV but still 60 % for SOM (Fig. 2F). This suggests that PV presynaptic cells were particularly aggregated in L5, potentiating the difference with other layers and with the innervation of SOM interneurons, whose presynaptic cells were broadly scattered in the barrel field, throughout L2/3, 5 and 6 (Fig. 2F).

Our results showed that the spatial organization of projections from the barrel field differed
significantly between PV and SOM interneurons. We next tested whether their specific
characteristics were generalizable to projections from other cortical areas. Like the barrel
field, the second somatosensory cortex (S2) is organized in functionally distinct layers (El-
Boustani et al. 2020). However, its horizontal organization is thought to be different, as S2
has no barrels and, *in vivo*, it holds a compact but less precise representation of whiskers
than the barrel field (Carvell and Simons 1986; Kwegyir-Afful and Keller 2004; Hubatz et al.
2020). Given these similarities and differences, we asked whether S1 and S2 projections to
DLS interneurons exhibited the same connectivity patterns.

We first traced the presynaptic cells of PV and SOM interneurons in the somatosensory
cortex using monosynaptic modified rabies virus. In SOM-cre and PV-cre mouse lines,
sequential unilateral injections of floxed TVA helper virus and glycoprotein (GFP fused
N2cG) and rabies virus EnvA-CVS-N2c-ΔG (mCherry fused) were performed in the DLS
(Fig. 3A-B). Systematic brain serial section and acquisition reconstructed the S1 and S2
connectivity to DLS interneurons (Fig. 3C). Presynaptic cell density showed a pronounced
bias toward S1 compared to S2 for both interneuron cell types (~ 3 fold), particularly on the
ipsilateral side (Fig. 3E). Within S1, the distributions of presynaptic cells across
somatosensory territories were different between interneurons. PV presynaptic cells covered
all territories while those of SOM were heterogeneously distributed. In particular, SOM
presynaptic cells were proportionally more present in the barrel field compared to PV (Two-
way Anova, interaction p=0.0144) (Fig. 3F). Within both S1 and S2, presynaptic cells were in majority present in L5 (Fig. 3G). However, their distribution in S2 showed a slight but significant shift towards upper layers for SOM compared to PV (Fig. 3G).

These results suggest a globally lower S2 innervation of interneurons and different laminar organization between PV and SOM in S2 compared to S1. To test how these anatomical differences transposed functionally, recordings and LSPS were repeated in a preparation containing S2 and the connected striatal region in the same slice (Fig. 4A-C; Fino et al., 2005). A striking observation was the number of PV connecting sites in S2 which was ~ 2 fold lower compared to the barrel field (Fig. 4D). Multivariate analysis revealed in S2 as well significant differences between PV, SOM and SPN (p = 0.041, pseudo-F = 2.862), only this time SOM showed the greatest separation (Fig. 4E). Overall, this was due to these interneurons being less innervated than PV and SPN, as shown in the lower connecting sites (3.6 ± 0.9 - 7.7 ± 1.8 - 7.3 ± 1.1), layers and columns numbers (Fig. 4D, two-way anova; cell-type effect: p < 0.001). Evoked EPSC were also smaller in SOM interneurons (Fig. 4F,G, Kruskal-Wallis p=0.0163). A second striking feature of S2 innervation was the major decrease in L5 inputs. This was visible in the vertical profile of PV innervation, now less phasic, suggesting that inputs were principally shaved from layer 5 compared to in the barrel field (Fig. 4H,I). In addition, the difference in SOM input density with other neurons was pronounced in this layer (Fig. 4I). These results suggest that within a relatively sparse S2 projection, PV and SPN have close connectivity patterns, with inputs evenly distributed across cortical layers, and SOM connectivity is distinctively weak.

Both anatomical tracing and functional mapping converged on the finding that somatosensory projections to the DLS are biased towards S1. However, they diverged in terms of proportions between layers. One intriguing difference is that, anatomically, the projections to PV were sharply centered on L5 both in the barrel field and in S2 when functionally, they were sharp only in the barrel field. A difference between the two mapping approaches was in the number of interneurons whose presynaptic cells were mapped: a population of interneurons (starter cells) with rabies virus and individual neurons with patch recordings. Since interneurons in a population could be innervated by overlapping sets of presynaptic cells in cortex, we hypothesized that L5 projections to PV interneurons were characterized by different degrees of overlapping in the barrel field and S2. To test this hypothesis, we analyzed the rise in labelled presynaptic cells as a function of increasing starter cell numbers in DLS. We found that L5 presynaptic cells increased more sharply in S2 than in the barrel field (Fig. 5A), even before correction for differences in volume (S2 is smaller). This supported our hypothesis that L5 projections from the barrel field were more largely shared among PV interneurons. The comparison of connectivity maps of PV interneurons recorded in the same slice showed that 65 % of pairs presented overlap in L5 (n = 31; Fig. 5B). On average, only 7.7 ± 1.4 % of all L5 connecting sites of a PV interneuron were shared with a neighbor. (Fig. 5C). But interneurons shared connecting sites with multiple neighbors over long distances of separation, up to 1.5 mm in our sample (Fig. 5C). The likelihood of PV cells receiving L5 inputs from S2 was low in recordings (Fig. 4I), making this investigation difficult in this projection. But 3 out of 3 pairs with L5 inputs showed no overlap (6 out of 6 pairs still showed no overlap, all layers considered; Fig. 5C). Thus, heterogeneous levels of overlapping connections across layers may explain discrepancies between the vertical profiles of projections obtained with the two mapping methods. Altogether, these findings suggest that the dense innervation of PV interneurons from L5 in
the barrel field is associated with a high density of shared presynaptic projecting cortical cells among PV interneurons in the barrel field. In contrast, innervation of PV within the S2 projection seems relatively heterogeneous between neurons.

To evaluate the potential consequences of specific PV and SOM innervations in controlling SPN input integration, we studied the overlap between connectivity maps of SPN and interneurons. The projection of these connectivity maps on the horizontal cortical axis illustrated well their differences: in the barrel field, input fields of PV cells could overlap densely with those of SPN, irrespectively of where the latter's inputs were disseminated (Fig. 6A). Comparing the connectivity maps of cells recorded in the same slice, we found that nearly all SPN and PV located within 700 µm of each other had connecting sites vertically aligned, hence probably originating from the same column (Fig. 6B, top). They were still 40 % for larger distances, up to 1 mm. These sites constituted as much as 40 % of SPN connectivity, for each PV-SPN pair. Given that corticostriatal inputs originated from different layers, overlap was then investigated while maintaining the position of connecting sites in the 2 dimensions. This did not reduce the chance that PV and SPN connectivity maps showed overlaps (80 % of pairs, Fig. 6B, bottom). However, these sites now constituted 10 % of SPN connectivity, indicating better horizontal than vertical alignment. Overlap between connectivity maps of SOM and SPN was sparser but in theory could still occur at great distances separating cells (Fig. 6A). Indeed, 80 % of pairs with SOM and SPN recorded within 500 µm of each other were likely to have connecting sites vertically aligned in the barrel field (Fig. 6B, top). However, the chance that these sites overlapped was low, 5%, suggesting larger divergence along the laminar axis compared to PV-SPN pairs (Fig. 6B, bottom).

A different picture emerged from the connectivity maps in S2 where overlap could occur for similar long cell distances separating cells, but in a sparse manner for both PV and SOM (Fig. 6A). Compared to the barrel field, the chance that PV and SPN had connecting sites vertically aligned was still relatively high (60 % of pairs; Fig. 6C, top), but the chance that the PV sites overlapped with those of SPN decreased by ~ 4 fold (Fig. 6C, bottom). Finally, SOM and SPN had little input in common in S2, less than 5 % of the SPN connecting sites (Fig. 6A,C).

# MATERIEL ET METHODES

## *Mice and ethics*

PV-Cre and SOM-Cre lines were used for these experiments (mouse lines #008069 and #013044 from Jackson Laboratory), mated with ai14 mice (Jackson 007909). Both sexes were used on postnatal day 30 to 45 and housed in temperature-controlled rooms with standard 12 h light/dark cycles. Food and water were available *ad libitum*. All experiments were performed in accordance with EU institutional guidelines and French national institutional animal care guidelines (protocol #33185).

## *Brain slices preparation and electrophysiology*

Mice were deeply anesthetized with isoflurane (4 %) prior to cervical dislocation and decapitation. Corticostriatal slices of 350 µm thickness were cut in a chilled cutting solution containing (in mM): 110 choline chloride, 25 NaHCO3, 25 D-glucose, 11.6 sodium ascorbate, 7 MgCl2, 3.1 sodium pyruvate, 2.5 KCl, 1.25 NaH2PO4, and 0.5 CaCl2 (Sigma
Aldrich). Barrel field-corticostriatal slices were cut with a 60° angle from the midline and a 10° angle in the dorso-ventral axis (Amroune et al., 2025). S2-corticostriatal slices obtained were horizontal (Fino et al., 2005). Slices were then placed in artificial cerebrospinal fluid (ACSF) containing (in mM): 127 NaCl, 25 NaHCO3, 25 D-glucose, 2.5 KCl, 1 MgCl2, 2 CaCl2, and 1.25 NaH2PO4, aerated with 95% O2/5% CO2. Slices were then transferred in an incubator at 34 °C for 30 min and then maintained at room temperature for 20 min prior to use. PV and SOM interneurons were visualized under infrared and fluorescent lights under a BX61WI microscope (Olympus) and patched with borosilicate electrodes (3–6 MΩ). Whole-cell recordings were performed in voltage-clamp using a Multiclamp 700A amplifier (Axon Instrument, Molecular Devices). The holding membrane potential was -70 mV for interneurons or -80 mV for SPN. The intracellular solution contained (in mM) 128 Cs-methylsulfate, 4 MgCl2, 10 HEPES, 1 EGTA, 4 Na2ATP, 0.4 Na2GTP, 10 Na-phosphocreatine, 3 ascorbic acid; pH 7.25; 290-300 mOsm. Non fluorescent cells were considered as SPN.

## Laser scanning photostimulation (LSPS) with glutamate uncaging

LSPS was performed as previously described (Amroune et al, 2025). Slices were kept during recordings in circulating ACSF solution containing (in mM): 0.2 MNI-caged glutamate (Tocris), 0.005 CPP [(-)-3-(2-carboxypiperazin-4-yl) propyl-1-phosphonic acid], 4 CaCl2, and 4 MgCl2. Focal photolysis of caged glutamate was accomplished with a 2 ms 25 mW pulse of a pulsed UV (355 nm) laser (DPSS Lasers Inc.) through a 0.16 NA 4 × objective (Olympus). 464 positions spaced by 75 µm on a 29 × 16 grid (2.1 × 1.1 mm) were scanned over the cortex. The slice and the LSPS grid were oriented in such a way that layer 5a was laid out horizontally. UV stimuli were applied every 700 ms and their successive positions on the LSPS grid were such as to maximize the time between stimulations of neighboring sites. Electrophysiological traces consisted of 100 ms baseline, a 450 ms window followed by a -5 mV 100 ms test pulse. A minimum of 3 and up to 4 stimulations were performed at each site at several minutes intervals. Custom software for instrument control and acquisition (Suter et al. 2010) was written in Matlab (Mathworks).

## Analysis of electrophysiology recordings

Synaptic input maps of neurons were constructed by taking the peak amplitude of EPSCs detected in a 50 ms time window starting at stimulation onset for each position in the LSPS grid. Measures were averaged across repetitions of stimulations (2-5). The threshold for EPSC detection was 3 standard deviations from baseline, or 8.3 ± 0.3 pA for PV cells and 9.1 ± 0.5 pA for SPN. To disambiguate evoked responses from spontaneous activity, synaptic responses occurring less than 2 to 3 times across repetitions of maps were set to zero. For the barrel field-DLS slices, an orthonormal reference frame is defined on each slice: the first axis passes through the tip of the striatum and the junction between the striatum, the external globus pallidus (GPe), and the internal capsule, while the second axis is oriented at 120° to the first. For the S2-DLS slices, a reference frame is also established on each slice, with the x-axis aligned with the horizontal portion of the corpus callosum located under S2, and a second axis, perpendicular to the first, passing through the tip of the striatum. This allows to obtain the relative position of recorded striatal cells and connecting sites within an unique reference frame. Pattern analyses were written in Matlab (Bureau, 2025). In order to detect connectivity clusters in the LSPS map, we used the binary version of the map ('connectivity map') reporting the location of connected and nonconnected sites (i.e., yielding EPSCs or none in the recorded cells). To detect clusters, the binary map was
collapsed along its vertical axis. A cluster comprised 1 or more consecutive connected sites that were framed by 1 or more non-connected sites. Thus, a cluster here may include connected sites that were not adjacent on the vertical axis in the original map, and may combine synaptic inputs from different layers. The number of cortical columns was estimated based on the number of consecutive connected sites in the collapsed map and the mean width of a barrel in the slice, 150 µm, which is equal to 2 connected sites in the map. Hence, the number of cortical columns = Σ (ceiling(ConsSites/2)). The input field width corresponds to the cortical distance from the more lateral to the more medial cluster. The amplitude and the decay time of EPSC was determined on monophasic detected EPSCs by manually pinning the onset time (t), the max amplitude and the offset time. The decay time is equal to t(max_amplitude)-t(offset).

## Pseudo rabies virus based tracing

### Stereotaxic injections

Animals were anesthetized with isoflurane (5% for induction and 2.5% for maintenance) and mounted on a stereotaxic device (Kopf Instruments; Tujunga, CA). After applying an external antiseptic and antifungal (Vetedine) and injection of local analgesic under the scalp, an incision was made along the midline of the skull. PV-cre and SOM-cre mice were injected with pseudo rabies virus to map inputs of either cell type. We previously confirmed the efficient and specific cre-dependent labelling with either GFP or mCherry in each interneuron using those cre- mouse lines (Fino et al. 2018; Rotariu et al. 2025). A small hole was drilled at the left dorsolateral (AP +0.8; ML +2.3; DV -2.5). 200µl of a mix of viruses (AAV2.8.EEF1a.flex.GTc and AAV2.8.syn.flex.H2BG.N2cG) were injected through a pulled glass pipette (pulled with a P-97 model Sutter Instrument Co. pipette puller) using a nanoinjector (World Precision Instruments, Germany) at a rate of 2 nL/sec. The injecting pipette was left in place for 2 to 3 minutes and then rapidly retracted to limit viral backflow. Mice were sutured and sterile saline was subcutaneously injected to minimize dehydration. After post-operative monitoring on a heating pad, mice were placed back to their home cage and monitored daily for 5 days. Five days later, 200µl of modified rabies virus (RABV41) were injected at the same coordinates in the same conditions. Mice were then monitored in their home cage during 10 days of viral expression. Different dilutions of the first mix of AAVs and RAB41 allowed to vary the number of starter cells.

## Histology and microscopy acquisitions

Ten days after injection, mice were anesthetized with an overdose of zoletil/domitor (20µl/10g) and transcardially perfused with phosphate buffered saline (PBS) followed by 4% paraformaldehyde (AntigenFix, Diapath) with a peristaltic perfusion pump. The brain was carefully extracted and post-fixed in 4% PFA (48 hr). The brain was then transferred to PBS and kept at 4°C. Whole-brain coronal slices (50 µm) were collected with a vibratome (Leica) and simultaneously plated on super frost plus slides (Thermo Fisher Scientific, Waltham, MA). Slides were counterstained with DAPI (0.8 µg/ml) and cover slipped with mounting media containing (in mM): 115 Tris, 1870 Glycerol and 11.5% Mowiol. When mounted, slices were acquired with a widefield microscope (DM6000, Leica) and a 10x objective. Acquisitions are done at three wavelengths: 358 nm (for DAPI staining), 395 nm (GFP, labelling of primary infected PV and SOM interneurons) and finally 550 nm (mCherry, labelling of presynaptic cells).

## Brain slices alignment on Allen Brain Atlas
Acquired slices are then aligned to a reference coronal mouse brain atlas (Allen Brain Atlas, Allen Institute for Brain Science) using the Aligning Big Brains & Atlases plugging (EPFL, Chiaruttini et al., 2025 Switzerland) and its alignment protocol. Once slices are aligned, the reference atlas is transformed through manual and automatic procedures in order to fit in the more optimized way to the acquired slices.

### Semi-automatized cell detections and quantifications

After atlas transformation, slices were exported to a Qupath project (Queen's University Belfast, Northern Ireland) together with associated aligned atlas. Then starter cells and presynaptic cells were detected using a positive cell detection tool based on thresholded values of cells morphological features, and this for all channels, in a list of brain regions of interest. Starter cells were defined as detected co-infected cells with identical coordinates in both fluorescence channels and presynaptic cells were defined as cells detected in GFP-channel.

### Statistical analysis

We used non-parametric Wilcoxon, Mann-Whitney or Kruskal-Wallis tests, followed by Dunn-Sidak post hoc tests. Two-way anova was performed on ranks. For vertical plots, row-wise differences between cell types were assessed using a permutation-based one-way anova (5,000 permutations). *p* values were FDR-corrected across rows (Benjamini-Hochberg). Multivariate differences in connectivity patterns across neuronal types were evaluated using permutational multivariate analysis of variance (permanova). Five connectivity parameters were standardized (z-score) prior to analysis. The global effect of neuronal type was tested using 999 permutations of the data. To visualize multivariate separation, canonical variate analysis was applied to the standardized variables, and individual observations were projected onto the first two canonical variates. The contribution of individual variables to group separation was assessed using a permutation-based variable importance analysis, in which each variable was permuted independently and the resulting reduction in the permanova pseudo-F statistic was quantified. Of the 50 SPN included in this analysis for connectivity patterns in the barrel field, 35 were presented in Amroune et al., 2025, using the same preparation.

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**Figure legends**

**Figure 1: Corticostriatal projections from the barrel field preferentially target PV over SOM and SPN cells.**

A, Montage of a DIC image of a barrel cortex corticostriatal slice with superimposed schematic representation of the experiment. Striatal cells (PV, SOM or SPN) were recorded in the DLS while LSPS with glutamate uncaging was performed in the barrel cortex (blue grid). IC, internal capsule; hipp., hippocampus. B, Top, examples of synaptic input maps for individual PV and SOM interneuron. Each pixel of color indicates the peak amplitude of EPSCs evoked by LSPS in the barrel field. The cortical layers are indicated by solid white vertical lines. Bottom, EPSC traces evoked at the sites indicated by letters in the maps above. Four repetitions are superimposed. C, Example connectivity maps (polygons representing connected sites) of one PV (green) and one SOM (purple) interneuron. D, Overlay of sites in the barrel cortex where stimulations evoked EPSC in PV (n = 38) and SOM (n = 15) interneurons. E, Number of cortical sites connected to PV, SOM and SPN (n = 15) cells. \*, Kruskal-Wallis, p < 0.001, Dunn-Sidak, PV-SOM and PV-SPN p < 0.001. F, Paired comparison of the number of connecting cortical sites in PV-SPN pairs (n = 19) and SOM-SPN pairs (n = 5) recorded in the same slice. \*, PV-SPN, p < 0.001, SOM-SPN, p = 0.25, Wilcoxon). G, Representative EPSC traces. \*, analyzed events. H, Amplitude and decay time of evoked EPSCs from unitary connections recorded in PV (n=31), SOM (n=15) and SPN (n=10). \* Amplitudes: p <0.05; PV-SOM, p = 0.0027. Decay times: p<0.05; PV-SPN, p = 0.0185; Kruskal-Wallis and Dunn-Sidak post hoc tests.

**Figure 2: Different spatial organization of projections from the barrel field between PV, SOM and SPN.**

A: Number of cortical layers, columns, clusters and input field width in connectivity maps of PV (n=38), SOM (n=15) and SPN (n=50) cells. \*, layers: PV-SOM: p=0.0181, PV-SPN: p <0.001; columns: PV-SOM: p=0.0132, PV-SPN: p<0.001; input field width: PV-SPN: p<0.001; two-way anova; cell-type effect: p < 0.001; cell-type × parameter interaction : p = 0.0432. C, Canonical variate analysis of connectivity parameters across neuronal types. Points represent individual observations projected onto the first two canonical variates. Ellipses show 95% confidence regions for each group, and large red symbols indicate group centroids. (permanova, pseudo-F = 14.8, p < 0.001). C, Variable importance analysis identified relative contributions to centroid separation. D, PV cells exhibit a steeper scaling of connecting site occupancy with input field size than SOM or SPN (slope comparison, PV-SOM: p = 0.018; PV-SPN: p < 0.001). Dashed grey line is for 100% occupancy of the input field width. E, Number of connecting sites across cortical layers. \*, p < 0.001, permutation-based one-way anova. F, Proportion of cells with inputs across layers.

**Figure 3: Different S1 and S2 connectivity patterns between SOM and PV revealed with anatomical tracing.**

A: Experimental workflow showing sequential unilateral viral injections for cell type-specific monosynaptic modified-RV tracing of cells projecting to DLS. B: Left, magnification of the injection site in DLS. In green, GFP in the infected SOM cells, in red, mCherry in the striatal presynaptic cells and in yellow the co-infected SOM cells, i.e. the starter cells (arrow). Scale bar, 100 µm. Right, coronal section of a SOM-cre mouse, with DLS SOM cells infection and retrograde labelling of presynaptic cells. In blue, DAPI staining. Scale bar, 500 µm. C: Magnification of a representative coronal section including S1 and S2 (cyan solid lines). In
red the cortical presynaptic cells, in blue the DAPI staining. Scale bar, 400 µm. D: The numbers of starter cells in PV-cre (green, n=4) and SOM-cre (purple, n=4) mice are not different. E: Number of presynaptic cells as a function of PV and SOM starter cells (each point represents one mouse). F: Proportion of presynaptic cells in S1 and S2, ipsilateral and contralateral, normalized over the total somatosensory cortex (S1+S2). G: Proportion of presynaptic cells in different territories of S1. H: Proportion of presynaptic cells in the different cortical layers, in S1 and in S2. Two-way Anova, **, p<0.001.

**Figure 4: S2-specific distinction between interneurons functional connectivity patterns.**

A, Montage of a DIC image of a S2 corticostriatal slice with superimposed schematic representation of the experiment. Striatal cells (PV, SOM or SPN) were recorded in the DLS while LSPS with glutamate uncaging (blue grid) was performed in S2. IC, internal capsule; hipp., hippocampus. B, Example connectivity maps (polygons) of one PV (green) and one SOM (purple) interneuron. C, Overlay of sites in S2 where stimulations evoked EPSC in PV (n=15) and SOM (n=9) interneurons. D, From left to right, number of cortical columns, sites, layers and input field width connected to PV, SOM and SPN (n=15) cells, p<0.001; two-way anova; cell-type effect: p < 0.001, no interaction with parameters. E, Canonical variate analysis of connectivity parameters across neuronal types (permanova, p = 0.041, pseudo-F = 2.862). F, Representative EPSC traces. G, Amplitude of evoked EPSCs from unitary connections recorded in PV (n=12), SOM (n=9) and SPN (n=9) p<0.005, PV-SOM : p = 0.0163, Kruskal-Wallis and Dunn-Sidak post hoc test. H, Number of connecting sites across cortical layers. I, Proportion of cells with inputs across layers. *, p < 0.001, permutation-based one-way anova.

**Figure 5: Shared cortical innervations between PV interneurons in the barrel field.**

A, Relationship between presynaptic cells number in layer 5 of S2 and barrel cortex (bfd) and PV starter cells number. B, Proportion of PV pairs with overlapped connecting sites in L5, in the barrel field and in S2 (n = 31). C, Proportion of connecting sites overlap located in L5 for various distances between PV cells within a pair. Large light green symbols are for S2 pairs (n = 3) and others are for the barrel field (n = 31). Solid green bar is the mean overlap in the barrel field.

**Figure 6: Projections to PV and SOM interneurons overlap with projections to SPN with different degrees.**

A, Proportion of cells with connecting sites in the barrel field or S2, centered on peak input for PV, SOM (solid lines) and SPN (area). B, Left, In the barrel field, proportion of PV-SPN pairs (n = 19) and SOM-SPN pairs (n = 6) with overlapping columns (top) or connecting sites (bottom) for increasing distances between cells. Right, Overlap of SPN columns (top) or SPN connecting sites (bottom) with PV. C, Same as in B for S2. PV-SPN pairs, n = 5; SOM-SPN pairs n= 6.
<figure><img src="image_3.png" /><figcaption>Figure 1</figcaption></figure>
<figure><img src="image_4.png" /><figcaption>Figure 2</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>Figure 3</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>Figure 4</figcaption></figure>
A

Presynaptic cells in L5

<figure><img src="image_7.png" /></figure>

B

Pairs with
overlap

<figure><img src="image_8.png" /></figure>

C

Overlap in L5 (%)

<figure><img src="image_9.png" /></figure>

Figure 5
<figure><img src="image_10.png" /><figcaption>Figure 6</figcaption></figure>