Although learning in response to extrinsic reinforcement is theorized to be driven by dopamine signals that encode the difference between expected and experienced rewards, skills that enable verbal or musical expression can be learned without extrinsic reinforcement. Instead, spontaneous execution of these skills is thought to be intrinsically reinforcing. Whether dopamine signals similarly guide learning of these intrinsically reinforced behaviours is unknown. In juvenile zebra finches learning from an adult tutor, dopamine signalling in a song-specialized basal ganglia region is required for successful song copying, a spontaneous, intrinsically reinforced process. Here we show that dopamine dynamics in the song basal ganglia faithfully track the learned quality of juvenile song performance on a rendition-by-rendition basis. Furthermore, dopamine release in the basal ganglia is driven not only by inputs from midbrain dopamine neurons classically associated with reinforcement learning but also by song premotor inputs, which act by means of local cholinergic signalling to elevate dopamine during singing. Although both cholinergic and dopaminergic signalling are necessary for juvenile song learning, only dopamine tracks the learned quality of song performance. Therefore, dopamine dynamics in the basal ganglia encode performance quality during self-directed, long-term learning of natural behaviours.

Linking activity in specific cell types with perception, cognition, and action, requires quantitative behavioral experiments in genetic model systems such as the mouse. In head-fixed primates, the combination of precise stimulus control, monitoring of motor output, and physiological recordings over large numbers of trials are the foundation on which many conceptually rich and quantitative studies have been built. Choice-based, quantitative behavioral paradigms for head-fixed mice have not been described previously. Here, we report a somatosensory absolute object localization task for head-fixed mice. Mice actively used their mystacial vibrissae (whiskers) to sense the location of a vertical pole presented to one side of the head and reported with licking whether the pole was in a target (go) or a distracter (no-go) location. Mice performed hundreds of trials with high performance (>90% correct) and localized to <0.95 mm (<6 degrees of azimuthal angle). Learning occurred over 1-2 weeks and was observed both within and across sessions. Mice could perform object localization with single whiskers. Silencing barrel cortex abolished performance to chance levels. We measured whisker movement and shape for thousands of trials. Mice moved their whiskers in a highly directed, asymmetric manner, focusing on the target location. Translation of the base of the whiskers along the face contributed substantially to whisker movements. Mice tended to maximize contact with the go (rewarded) stimulus while minimizing contact with the no-go stimulus. We conjecture that this may amplify differences in evoked neural activity between trial types.

The substantia nigra pars reticulata (SNr) functions as the principal inhibitory output of the basal ganglia, with the timing of its spikes critically controlling downstream disinhibition required for movement initiation. The external globus pallidus (GPe) and D1-expressing medium spiny neurons (D1-MSNs) in the striatum provide GABAergic inputs to the SNr that differ in their amplitude and kinetic properties. How these inputs interact with the intrinsic membrane currents that determine SNr firing is only partially understood. Using optogenetics, computational modeling, and electrophysiology in acute mouse brain slices, 47 animals of either sex were used for measurements, and we found an unexpected interaction between GABAergic inputs and hyperpolarization-activated currents (Ih) that tunes inhibitory efficacy in a pathway-specific manner. GPe inputs evoke fast, large IPSCs that transiently suppress SNr firing within a narrow window but whose rapid decay enables depolarization from Ih to restore firing after only a brief pause. In contrast, the slower decay kinetics of striatal IPSCs enables more sustained inhibition that counters the depolarizing drive from Ih to produce longer pauses, despite their lower conductance amplitudes. Pharmacological blockade of Ih with ZD7288 eliminated the rapid recovery of firing after GPe inhibition and equalized the inhibitory efficacy between GPe and striatal pathways. These findings establish an important interplay between synaptic kinetics and intrinsic membrane conductances in establishing pathway-specific inhibitory balance in the basal ganglia. Our study reveals that inhibitory pathways to the substantia nigra pars reticulata are differentially shaped by the interplay between synaptic kinetics and intrinsic membrane conductances. Using optogenetics, electrophysiology, and modeling, we showed that fast-decaying GABAergic inputs from the external globus pallidus are rapidly overcome by Ih, producing only brief pauses in SNr firing, whereas slower striatal inputs generate longer-lasting inhibition. Blocking Ih abolishes this difference, demonstrating that intrinsic currents tune inhibitory efficacy in a pathway-specific manner. These results identify a biophysical mechanism that helps set the balance of basal ganglia output essential for movement control.

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.

How the brain signals prediction errors for non-rewarding, yet significant, sensory events remains a central question. Although the cortical mismatch negativity provides a well-known signature for deviance detection, the contribution of subcortical dopamine remains unclear. This study tested the hypothesis that phasic dopamine in the nucleus accumbens encodes the salience associated with the violation of an ongoing statistical regularity. Using fiber photometry in freely moving rats, we contrasted an auditory oddball paradigm with a many-standards control. Deviant stimuli elicited a significantly amplified dopamine response compared with standard stimuli. Crucially, this dopamine response enhancement was absent in the control condition, demonstrating that the nucleus accumbens dopamine responds specifically to rule violation rather than mere stimulus rarity. The long latency of this signal (~500 ms) relative to the cortical mismatch negativity argues against a direct role in the initial detection of deviance. Instead, our findings support a model in which subcortical dopamine acts as a distinct salience signal, operating in parallel with cortical deviance detection, to evaluate unexpected events and guide subsequent behavioral adjustments.

Quantitative imaging is an emerging field that may allow prediction of oncological outcomes. We investigate whether radiomics and deep learning can predict outcomes in metastatic non-small cell lung cancer utilizing randomized trials of PD-1 inhibitors + /- stereotactic ablative body radiotherapy: PEMBRO-RT(NCT02492568), NIVORAD(ACTRN12616000352404) and MDACC(NCT02444741). A random forest model developed on PEMBRO-RT using radiomics features had an AUC of 0.57 for prediction of per-lesion progressive disease on immunotherapy compared to an AUC of 0.92 for a deep learning model. A random forest survival model using radiomics features for overall survival (progression free survival) had a concordance index of 0.63(0.59) and improved to 0.67(0.65) by adding clinical features, including PD-L1 and treatment arm. Validation on NIVORAD and MDACC revealed reduced AUCs. Overall, a deep learning compared to a radiomics model demonstrated excellent predictive value for per-lesion progressive disease for patients on immunotherapy. Models had reduced performance on external validation. Research improving generalizability is required for clinical translation.

The unfolding or melting temperature (TM) is a central quantity to characterize the stability of proteins and other biopolymers. The accurate prediction of protein melting temperatures by molecular mechanics force field simulations is highly desirable for many biophysical and biotechnological applications. Since the time scales for protein (un)folding are hardly accessible in conventional molecular dynamics simulations, enhanced sampling techniques such as Temperature Replica Exchange Molecular Dynamics (TREMD) are typically employed. However, TREMD simulations are computationally very demanding, especially if large temperature ranges need to be covered. In addition, if TM is initially unknown, setting up TREMD simulations is often challenging. To find the optimal initial conditions for such simulations, we describe their performance using a theoretical model, which we validate on a minimalistic Markov chain Monte Carlo simulation setup. In an effort to reduce the computational demand, we have investigated the possibility of using small sets of TREMD temperature ladders placed iteratively in the vicinity of a TM estimate. Different TREMD setups were extensively tested on the fast-folding protein chignolin. We found that appropriate starting conformations lead to significantly faster convergence. Furthermore, we found that, in practice, combining multiple small temperature ladders can be advantageous in comparison to a single temperature ladder. Based on our findings, we formulate practical recommendations on how to setup TREMD for protein melting with optimal efficiency.