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Different corticostriatal suprathreshold responses in direct and indirect striatal projection neurons (SPNs) of rodents have been reported. Responses consist in prolonged synaptic potentials of polysynaptic and intrinsic origin, in which voltage‐gated Ca2⁺ currents play a role. Recording simultaneous Ca2⁺ imaging and voltage responses at the soma, while activating the corticostriatal pathway, we show that encoding of synaptic responses into trains of action potentials (APs) is different in SPNs: firing of APs in D1‐SPNs increase gradually, in parallel with Ca2⁺ entry, as a function of stimulus intensity. In contrast, D2‐SPNs attain a maximum number of evoked spikes at low stimulus intensities, Ca2⁺ entry is limited, and both remain the same in spite of increasing stimulus strength. Stimulus needs to reach certain intensity, to have propagated Ca2⁺ potentials to the soma plus a sudden step in Ca2⁺ entry, without changing the number of fired APs, phenomena never seen in D1‐SPNs. Constant firing in spite of changing stimulus, suggested the involvement of underlying inactivating potentials. We found that Caᵥ3 currents contribute to Ca2+ entry in both classes of SPNs, but have a more notable effect in D2‐SPNs, where a low‐threshold spike was disclosed. Blockade of CaV3 channels retarded the steep rise in firing in D2‐SPNs. Inhibition block increased the number of spikes fired by D2‐SPNs, without changing firing in D1‐SPNs. These differences in synaptic integration enable a biophysical dissimilarity: dendritic inhibition appears to be more relevant for D2‐SPNs. This may imply distinctions in the set of interneurons affecting each SPN class.

A pipeline is proposed here to describe different features to study brain microcircuits on a histological scale using multi-scale analyses, including the uniform manifold approximation and projection (UMAP) dimensional reduction technique and modularity algorithm to identify neuronal ensembles, Runs tests to show significant ensembles activation, graph theory to show trajectories between ensembles, and recurrence analyses to describe how regular or chaotic ensembles dynamics are. The data set includes ex-vivo NMDA-activated striatal tissue in control conditions as well as experimental models of disease states: decorticated, dopamine depleted, and L-DOPA-induced dyskinetic rodent samples. The goal was to separate neuronal ensembles that have correlated activity patterns. The pipeline allows for the demonstration of differences between disease states in a brain slice. First, the ensembles were projected in distinctive locations in the UMAP space. Second, graphs revealed functional connectivity between neurons comprising neuronal ensembles. Third, the Runs test detected significant peaks of coactivity within neuronal ensembles. Fourth, significant peaks of coactivity were used to show activity transitions between ensembles, revealing recurrent temporal sequences between them. Fifth, recurrence analysis shows how deterministic, chaotic, or recurrent these circuits are. We found that all revealed circuits had recurrent activity except for the decorticated circuits, which tended to be divergent and chaotic. The Parkinsonian circuits exhibit fewer transitions, becoming rigid and deterministic, exhibiting a predominant temporal sequence that disrupts transitions found in the controls, thus resembling the clinical signs of rigidity and paucity of movements. Dyskinetic circuits display a higher recurrence rate between neuronal ensembles transitions, paralleling clinical findings: enhancement in involuntary movements. These findings confirm that looking at neuronal circuits at the histological scale, recording dozens of neurons simultaneously, can show clear differences between control and diseased striatal states: “fingerprints” of the disease states. Therefore, the present analysis is coherent with previous ones of striatal disease states, showing that data obtained from the tissue are robust. At the same time, it adds heuristic ways to interpret circuitry activity in different states.