Analysis of responses to L-3,4-dihydroxyphenylalanine (L-DOPA) in hemiparkinsonian rodent models can enhance understanding of Parkinson's disease (PD). Traditionally, these responses have been classified into medication OFF and ON states, with ON defined as the period after sufficient time has elapsed following drug administration. However, this binary classification may lead to neurophysiological analyses that overlook interindividual variability in the onset and duration of therapeutic effects. Although measuring plasma L-DOPA concentrations has been proposed to quantify this variability, technical difficulties in direct measurement limit the application of this approach. Here, we investigated whether acute L-DOPA responses can be quantitatively distinguished into behavioral states without direct plasma measurements, and whether these states correspond to neurophysiological dynamics. Using quantitative analysis of rotation direction in hemiparkinsonian rat model, we classified L-DOPA responses into four temporally distinct states: OFF, early contralateral rotation (ECR), rotational pause (RP), and late contralateral rotation (LCR). These behaviorally defined states exhibited statistically distinct neurophysiological profiles, characterized by simultaneously measured subthalamic firing rates and striatal dopamine levels. Although single-unit activity was insufficient to decode state transitions, neural population activity enabled robust decoding via a support vector machine (SVM). Furthermore, linear dynamical system modeling revealed transition-specific trajectories in a low-dimensional state space, suggesting that state transitions are represented by coordinated population dynamics. These findings demonstrate that temporal variability in L-DOPA responses can be identified behaviorally, providing a neurophysiologically validated framework that extends the conventional binary classification into a multi-state transition for PD research.

Adaptive sequential decision-making requires animals to learn how actions shape future states and rewards while adjusting behavioral strategies as experience accumulates. Although the hippocampus is implicated in state-structure representation and the striatum in value learning, how learning strategies evolve from exploratory sampling to efficient exploitation and how hippocampal-striatal interactions reorganize alongside this transition remain unclear. We trained six pigeons of undetermined sex to perform a probabilistic two-step decision task while simultaneously recording behavioral choices and hippocampal-striatal local field potentials. Strategy evolution was tracked using a dynamic reinforcement-learning framework in which model-based, model-free, and weighted model-based/model-free models were fitted across sliding windows. Hippocampal-striatal functional coordination was assessed using phase-locking analysis, and directed predictive influence was quantified using kernel Granger causality. The weighted model best explained choice dynamics, indicating a shift from model-based-like control during early acquisition toward model-free-like control during later stable performance. This behavioral shift was accompanied by two complementary neural changes: hippocampal-striatal phase coordination increased across learning, whereas hippocampus-to-striatum directed predictive influence decreased and covaried positively with model-based-like strategy weight. Direction-ratio analysis showed that hippocampus-to-striatum remained the predominant direction throughout learning, indicating reduced influence rather than directional reversal. After acquisition, hippocampus-to-striatum directed influence was not uniformly reduced but remained relatively preserved during rare, low-value, unrewarded, or less expected events. Together, these findings reveal a learning-dependent reorganization of hippocampal-striatal dynamics, in which increasing functional coordination is accompanied by reduced but condition-dependent hippocampal-to-striatal influence as behavior becomes more stable and efficient. Adaptive learning requires coordinated changes in behavioral strategy and neural interactions as animals progress from exploration to stable performance. Here, we show that pigeons gradually shifted from flexible, model-based-like control toward more stable, model-free-like behavior during sequential learning. In parallel, hippocampal-striatal interactions underwent a complementary reorganization: phase coordination progressively increased, whereas hippocampus-to-striatum directed predictive influence decreased with declining model-based-like control. Importantly, this directed influence remained relatively preserved during rare, low-value, or unrewarded events. These findings link behavioral strategy transition to the dynamic reorganization of hippocampal-striatal interactions, revealing how neural systems may simultaneously support efficient learned behavior while retaining sensitivity to behaviorally informative events.

We present an enhancer-AAV toolbox for accessing and perturbing striatal cell types and circuits. Best-in-class vectors were curated for accessing major striatal neuron populations including medium spiny neurons (MSNs), direct- and indirect-pathway MSNs, Sst-Chodl, Pvalb-Pthlh, and cholinergic interneurons. Specificity was evaluated by multiple modes of molecular validation, by three different routes of virus delivery, and with diverse transgene cargos. Importantly, we provide detailed information necessary to achieve reliable cell-type-specific labeling under different experimental contexts. We demonstrate direct pathway circuit-selective optogenetic perturbation of behavior and multiplex labeling of striatal interneuron types for targeted analysis of cellular features. Lastly, we show conserved in vivo activity for exemplary MSN enhancers in rats and macaques. This collection of striatal enhancer AAVs offers greater versatility compared to available transgenic lines and can readily be applied for cell type and circuit studies in diverse mammalian species beyond the mouse model.

Basal Ganglia Advances is a collection highlighting research on the structure, function, and disorders of the basal ganglia. It features studies spanning neuroscience, clinical insights, and computational models, serving as a hub for advances in movement, cognition, and behavior.

Recent advances in the field of Voltage Imaging, with a special focus on new constructs and novel implementations.

Work related to place tuning, spatial navigation, orientation and direction. Mainly includes articles on connectivity in the hippocampus, retrosplenial cortex, and related areas.

This brief review summarizes the most recent progress in in vivo voltage imaging in the neurosciences, as an extension to the work presented at the voltage imaging session of the 2025 FENS regional meeting in Oslo. First, we outline the latest advances in genetically encoded voltage indicators. Next, we examine technical innovations that have reshaped imaging platforms and lay the groundwork to investigate neural activity in real time across large fields of view and in deep tissue. Finally, we highlight a selection of compelling applications that have emerged over the past 5 years, illustrating how voltage imaging evolved from a proof of concept to being deployed to answer pending neuroscientific questions.

Activity of the mesolimbic dopamine system has long been implicated in encoding primary rewards and contributing to the addictive properties of drugs of abuse. Dopamine neurons in the ventral tegmental area (VTA) of the midbrain typically show patterns of spontaneous burst activity that align with the onset of salient events or rewarding stimuli, resulting in phasic dopamine release in the nucleus accumbens (NAc). Fiber photometry is increasingly being used as an accessible technique to quantify neural activity with high temporal resolution at sensors offering signal specificity in stable recordings over extended periods of time. It has been well established by multiple techniques that opioids increase mesolimbic dopamine activity, likely through disinhibition of VTA neurons. Here we used fiber photometry to compare sub-second transient events from VTA neurons with GCaMP6f and dopamine release in the lateral shell of the NAc with dLight1.3b and GRABDA2h in response to morphine treatment. In weekly sessions, one dose of morphine was administered in escalating order (2.5, 5,7.5, and 10 mg/kg, intraperitoneal). Consistent with prior literature, both GCaMP6f in VTA neurons and dLight1.3b in NAc showed patterns of increased signal following morphine treatment. In contrast, morphine suppressed transient activity at GRABDA2h sensors. Further analyses of whole signal streams from each sensor showed a generalized increase, but reduction in variability of the GRABDA2h signal, consistent with the interpretation of sensor saturation. Such results emphasize the importance of the inclusion of appropriate controls to contextualize the interpretation of biosensor responses, particularly in response to pharmacological treatment.