Trends in NeuroAI - Meta's MEG-to-image reconstruction
Trends in NeuroAI is a reading group hosted by the MedARC Neuroimaging & AI lab (https://medarc.ai/fmri). This will be an informal journal club presentation, we do not have an author of the paper joining us. Title: Brain decoding: toward real-time reconstruction of visual perception Abstract: In the past five years, the use of generative and foundational AI systems has greatly improved the decoding of brain activity. Visual perception, in particular, can now be decoded from functional Magnetic Resonance Imaging (fMRI) with remarkable fidelity. This neuroimaging technique, however, suffers from a limited temporal resolution (≈0.5 Hz) and thus fundamentally constrains its real-time usage. Here, we propose an alternative approach based on magnetoencephalography (MEG), a neuroimaging device capable of measuring brain activity with high temporal resolution (≈5,000 Hz). For this, we develop an MEG decoding model trained with both contrastive and regression objectives and consisting of three modules: i) pretrained embeddings obtained from the image, ii) an MEG module trained end-to-end and iii) a pretrained image generator. Our results are threefold: Firstly, our MEG decoder shows a 7X improvement of image-retrieval over classic linear decoders. Second, late brain responses to images are best decoded with DINOv2, a recent foundational image model. Third, image retrievals and generations both suggest that MEG signals primarily contain high-level visual features, whereas the same approach applied to 7T fMRI also recovers low-level features. Overall, these results provide an important step towards the decoding - in real time - of the visual processes continuously unfolding within the human brain. Speaker: Dr. Paul Scotti (Stability AI, MedARC) Paper link: https://arxiv.org/abs/2310.19812
Versatile treadmill system for measuring locomotion and neural activity in head-fixed mice
Here, we present a protocol for using a versatile treadmill system to measure locomotion and neural activity at high temporal resolution in head-fixed mice. We first describe the assembly of the treadmill system. We then detail surgical implantation of the headplate on the mouse skull, followed by habituation of mice to locomotion on the treadmill system. The system is compact, movable, and simple to synchronize with other data streams, making it ideal for monitoring brain activity in diverse behavioral frameworks. https://dx.doi.org/10.1016/j.xpro.2022.101701
PiVR: An affordable and versatile closed-loop platform to study unrestrained sensorimotor behavior
PiVR is a system that allows experimenters to immerse small animals into virtual realities. The system tracks the position of the animal and presents light stimulation according to predefined rules, thus creating a virtual landscape in which the animal can behave. By using optogenetics, we have used PiVR to present fruit fly larvae with virtual olfactory realities, adult fruit flies with a virtual gustatory reality and zebrafish larvae with a virtual light gradient. PiVR operates at high temporal resolution (70Hz) with low latencies (<30 milliseconds) while being affordable (<US$500) and easy to build (<6 hours). Through extensive documentation (www.PiVR.org), this tool was designed to be accessible to a wide public, from high school students to professional researchers studying systems neuroscience in academia.