Brox, T. | Search bioRxiv

bioRxiv2022

3D pose estimation enables virtual head-fixation in freely moving rats

The impact of spontaneous movements on neuronal activity has created the need to quantify behavior. We present a versatile framework to directly capture the 3D motion of freely definable body points in a marker-free manner with high precision and reliability. Combining the tracking with neural recordings revealed multiplexing of information in the motor cortex neurons of freely moving rats. By integrating multiple behavioral variables into a model of the neural response, we derived a virtual head-fixation for which the influence of specific body movements was removed. This strategy enabled us to analyze the behavior of interest (e.g., front paw movements). Thus, we unveiled an unexpectedly large fraction of neurons in the motor cortex with tuning to the paw movements, which was previously masked by body posture tuning. Once established, our framework can be efficiently applied to large datasets while minimizing the experimental workload caused by animal training and manual labeling.

neuroscience↗

bioRxiv2021

Requirements for mammalian promoters to decode transcription factor dynamics

In response to different stimuli many transcription factors (TFs) display different activation dynamics that trigger the expression of specific sets of target genes, suggesting that promoters have a way to decode them. Combining optogenetics, deep learning-based image analysis and mathematical modeling, we find that decoding of TF dynamics occurs only when the coupling between TF binding and transcription pre-initiation complex formation is inefficient and that the ability of a promoter to decode TF dynamics gets amplified by inefficient translation initiation. Furthermore, we propose a theoretical mechanism based on phase separation that would allow a promoter to be activated better by pulsatile than sustained TF signals. These results provide an understanding on how TF dynamics are decoded in mammalian cells, which is important to develop optimal strategies to counteract disease conditions, and suggest ways to achieve multiplexing in synthetic pathways.

systems biology↗

bioRxiv2021

Conserved structures of neural activity in sensorimotor cortex of freely moving rats allow cross-subject decoding

Our knowledge about neuronal activity in the sensorimotor cortex relies primarily on stereotyped movements that are strictly controlled in experimental settings. It remains unclear how results can be carried over to less constrained behavior like that of freely moving subjects. Toward this goal, we developed a self-paced behavioral paradigm that encouraged rats to engage in different movement types. We employed bilateral electrophysiological recordings across the entire sensorimotor cortex and simultaneous paw tracking. These techniques revealed behavioral coupling of neurons with lateralization and an anterior-posterior gradient from the premotor to the primary sensory cortex. The structure of population activity patterns was conserved across animals despite the severe under-sampling of the total number of neurons and variations in electrode positions across individuals. We demonstrated cross-subject and cross-session generalization in a decoding task through alignments of low-dimensional neural manifolds, providing evidence of a conserved neuronal code One-sentence summarySimilarities in neural population structures across the sensorimotor cortex enable generalization across animals in the decoding of unconstrained behavior. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=87 SRC="FIGDIR/small/433869v2_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@4dffbborg.highwire.dtl.DTLVardef@d07d55org.highwire.dtl.DTLVardef@1d459eborg.highwire.dtl.DTLVardef@5b5d78_HPS_FORMAT_FIGEXP M_FIG Conserved structures of neural activity in freely moving rats allow for cross-subject decoding. (a) We conducted electrophysiological recordings across the bilateral sensorimotor cortex of six freely moving rats. Neural activities were projected into a low-dimensional space with LEMs (22). (b) In a decoding task, points in the aligned low-dimensional neural state space were used as input for a classifier that predicted behavioral labels. Importantly, training and testing data originated from different rats. (c) Our procedure led to successful cross-subject generalization for sessions with sufficient numbers of recorded units. The rat and brain drawings are adapted from scalablebrainatlas.incf.org and SciDraw. C_FIG

neuroscience↗