Accessible and reliable neurometric testing in humans using a smartphone platform

Tests of human brain circuit function typically require fixed equipment in lab environments. We have developed a smartphone-based platform for neurometric testing. This platform, which uses AI models like computer vision, is optimized for at-home use and produces reproducible, robust results on a battery of tests, including eyeblink conditioning, prepulse inhibition of acoustic startle response, and startle habituation. This approach provides a scalable, universal resource for quantitative assays of central nervous system function.

Cerebellum-dependent associative learning is not impaired in a mouse model of neurofibromatosis type 1

Individuals with Neurofibromatosis type 1 (NF1) experience a high degree of motor problems. The cerebellum plays a pivotal role in motor functioning and the NF1 gene is highly expressed in cerebellar Purkinje cells. However, it is not well understood to what extent NF1 affects cerebellar functioning and how this relates to NF1 motor functioning. Therefore, we subjected global Nf1^+/- mice to a cerebellum-dependent associative learning task, called Pavlovian eyeblink conditioning. Additionally, we assessed general motor function and muscle strength in Nf1^+/- mice. To our surprise, we found that Nf1^+/- mice showed a moderately increased learning rate of conditioned eyeblink responses, as well as improved accuracy in the adaptive timing of the eyeblink responses. Locomotion, balance, general motor function, and muscle strength were not affected in Nf1^+/- mice. Together, our results support the view that cerebellar function in Nf1^+/- mice is unimpaired.

Automated gesture tracking in head-fixed mice

BACKGROUND

The preparation consisting of a head-fixed mouse on a spherical or cylindrical treadmill offers unique advantages in a variety of experimental contexts. Head fixation provides the mechanical stability necessary for optical and electrophysiological recordings and stimulation. Additionally, it can be combined with virtual environments such as T-mazes, enabling these types of recording during diverse behaviors.

NEW METHOD

In this paper we present a low-cost, easy-to-build acquisition system, along with scalable computational methods to quantitatively measure behavior (locomotion and paws, whiskers, and tail motion patterns) in head-fixed mice locomoting on cylindrical or spherical treadmills.

EXISTING METHODS

Several custom supervised and unsupervised methods have been developed for measuring behavior in mice. However, to date there is no low-cost, turn-key, general-purpose, and scalable system for acquiring and quantifying behavior in mice.

RESULTS

We benchmark our algorithms against ground truth data generated either by manual labeling or by simpler methods of feature extraction. We demonstrate that our algorithms achieve good performance, both in supervised and unsupervised settings.

CONCLUSIONS

We present a low-cost suite of tools for behavioral quantification, which serve as valuable complements to recording and stimulation technologies being developed for the head-fixed mouse preparation.