Intrinsic dopamine and acetylcholine dynamics in the striatum of mice.

External rewards such as food and money are potent modifiers of behaviour1,2. Pioneering studies established that these salient sensory stimuli briefly interrupt the tonic discharge of neurons that produce the neuromodulators dopamine (DA) and acetylcholine (ACh): midbrain DA neurons (DANs) fire a burst of action potentials that broadly elevates DA in the striatum3,4 at the same time that striatal cholinergic interneurons (CINs) produce a characteristic pause in firing5,6. These phasic responses are thought to create unique, temporally limited conditions that motivate action and promote learning7-11. However, the dynamics of DA and ACh outside explicitly rewarded situations remain poorly understood. Here we show that extracellular DA and ACh levels fluctuate spontaneously and periodically at a frequency of approximately 2 Hz in the dorsal striatum of mice and maintain the same temporal relationship relative to one another as that evoked by reward. We show that this neuromodulatory coordination does not arise from direct interactions between DA and ACh within the striatum. Instead, we provide evidence that periodic fluctuations in striatal DA are inherited from midbrain DANs, while striatal ACh transients are driven by glutamatergic inputs, which act to locally synchronize the spiking of CINs. Together, our findings show that striatal neuromodulatory dynamics are autonomously organized by distributed extra-striatal afferents. The dominance of intrinsic rhythms in DA and ACh offers new insights for explaining how reward-associated neural dynamics emerge and how the brain motivates action and promotes learning from within.

Artificial Intelligence Enables Quantitative Assessment of Ulcerative Colitis Histology.

Ulcerative colitis is a chronic inflammatory bowel disease that is characterized by a relapsing and remitting course. Assessment of disease activity critically informs treatment decisions. In addition to endoscopic remission, histologic remission is emerging as a treatment target and a key factor in the evaluation of disease activity and therapeutic efficacy. However, manual pathologist evaluation is semiquantitative and limited in granularity. Machine learning approaches are increasingly being developed to aid pathologists in accurate and reproducible scoring of histology, enabling precise quantitation of clinically relevant features. Here, we report the development and validation of convolutional neural network models that quantify histologic features pertinent to ulcerative colitis disease activity, directly from hematoxylin and eosin-stained whole slide images. Tissue and cell model predictions were used to generate quantitative human-interpretable features to fully characterize the histology samples. Tissue and cell predictions showed comparable agreement to pathologist annotations, and the extracted slide-level human-interpretable features demonstrated strong correlations with disease severity and pathologist-assigned Nancy histological index scores. Moreover, using a random forest classifier based on 13 human-interpretable features derived from the tissue and cell models, we were able to accurately predict Nancy histological index scores, with a weighted kappa (κ = 0.91) and Spearman correlation (⍴ = 0.89, P < .001) when compared with pathologist consensus Nancy histological index scores. We were also able to predict histologic remission, based on the absence of neutrophil extravasation, with a high accuracy of 0.97. This work demonstrates the potential of computer vision to enable a standardized and robust assessment of ulcerative colitis histopathology for translational research and improved evaluation of disease activity and prognosis.