Dopamine is essential for striatal function and learning. Striatal dopamine release can be triggered by dopamine cell firing, but also by coordinated cholinergic interneuron activity, which stimulates dopamine release via presynaptic nicotinic acetylcholine receptors on dopamine axons. While acetylcholine-dependent dopamine release is well-documented ex vivo and under artificial optogenetic stimulation in vivo, its role during natural behavior has remained unclear. One possible endogenous driver of acetylcholine-dependent dopamine release is thalamic input, which provides strong excitatory drive to cholinergic interneurons. To examine whether thalamic input provokes acetylcholine-dependent dopamine release during behavior, we performed simultaneous fiber photometry recordings of striatal dopamine (GRAB-rDA3m) and thalamic axon activity (gCaMP8m) in the dorsomedial (DMS) and dorsolateral striatum (DLS) of mice learning the accelerating rotarod, a striatal-dependent task that demands precise and effortful motor control. Recordings were obtained on- and off-task and across days of training to capture the full arc of learning. Dopamine transients in DMS, but not DLS, were frequently coupled to peaks in thalamic axon activity via an acetylcholine-dependent mechanism. The occurrence of these thalamic-evoked DMS dopamine transients depended on learning, task engagement, and the recent history of dopamine activity, but did not contribute to motor error signals. Together, these findings establish thalamic input as a physiological driver of acetylcholine-dependent dopamine release in DMS. Moreover, they reveal that striatal sensitivity to this local release mechanism is dynamically gated by dopaminergic history, providing a compelling framework for understanding how local and soma-triggered dopamine signals are coordinated to support learning.

High-resolution extracellular electrophysiology is the gold standard for recording spikes from distributed neural populations and is especially powerful when combined with optogenetics for manipulation of specific cell types with high temporal resolution. We integrated these approaches into prototype Neuropixels Opto probes, which combine electronic and photonic circuits. These devices pack 960 electrical recording sites and two sets of 14 light emitters onto a 70-μm-wide, 1-cm-long shank, allowing spatially addressable optogenetic stimulation with blue and red light. In mouse cortex, Neuropixels Opto probes delivered high-quality recordings together with spatially addressable optogenetics, differentially activating or silencing neurons at distinct cortical depths. In the mouse striatum and other deep structures, Neuropixels Opto probes delivered efficient optotagging, facilitating the identification of two cell types in parallel. Neuropixels Opto probes represent a promising tool for recording, identifying and manipulating neuronal populations.

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.

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.

Herbal medicine is indispensable and crucial to many healthcare systems. Its therapeutic value arises from diverse plant-derived compounds with potential pharmacological activities. This review summarizes recent advances in applying artificial intelligence (AI) and machine learning (ML) to elucidate the pharmacological mechanisms of herbal medicines, and offers a particular focus on revealing their bioactive compounds and pharmacological actions. It highlights how AI-driven analytical frameworks are enhancing the precision and efficiency of mechanism elucidation. Information relevant to herbal medicine and related computational studies was collected via keyword searching in common scientific databases including Google Scholar, PubMed, ACS Publications, Wiley Online Library, ScienceDirect, SpringerLink, Scopus, IEEE Xplore, ACM Digital Library, and Web of Science by using keywords "herbal medicine", "machine learning", "deep learning", "natural compounds", "docking", "QSAR", "toxicity", "mass spectrometry", "nuclear magnetic resonance", "feature extraction", "Absorption", "Distribution", "Metabolism", and "Excretion". The review finds that AI, particularly ML and deep learning approaches, has been applied to identify bioactive compounds, predict potential targets and mechanistic pathways, and thereby enable the identification of hidden patterns in complex chemical-biological datasets. Recent advances in AI have improved the understanding of the complex components and biological effects of herbal preparations. However, the variability of plant materials and the lack of systematic pharmacological studies remain the major challenges. Approaches that combine experimental data with computational analysis show promise for identifying bioactive compounds, predicting their targets, and clarifying the underlying mechanisms.

To evaluate the accuracy of a novel wrist-worn cuff-calibrated photoplethysmography (PPG)-based remote patient monitoring device as an alternative to ambulatory blood pressure monitoring (ABPM).

Guidelines recommend initial blood pressure assessment on both arms, with the higher-reading arm used for future measurements. As simultaneous measurement devices are often unavailable, we evaluated if sequential measurements are appropriate for assessing interarm differences. Blood pressure was measured in Shanghai using an automated system. Consecutive readings were taken with 1-min intervals, beginning with the right followed by the left arm, with a third reading if the difference between the first two exceeded 5 mmHg. In 7838 participants, right arm readings were consistently higher, with mean interarm systolic/diastolic differences of 4.59/1.01 mmHg if two readings on each arm, 6.24/1.45 mmHg if two readings on one and three readings on another arm, and 7.22/1.90 mmHg if three readings on each arm. This sequential measurement approach led to more frequent right arm selection for blood pressure measurement and potential overestimation of the interarm differences. Simultaneous measurements may be needed, but future studies are required to confirm this.