Tanaka, K. F. F. | Search bioRxiv

bioRxiv2023

Shared GABA transmission pathology in dopamine agonist- and antagonist-induced dyskinesia

Dyskinesia is involuntary movement caused by long-term medication with dopamine-related agents: the dopamine agonist, L-DOPA, to treat Parkinsons disease (L-DOPA-induced dyskinesia [LID]) or dopamine antagonists to treat schizophrenia (tardive dyskinesia [TD]). However, it remains unknown why distinct types of medications for distinct neuropsychiatric disorders induce similar involuntary movements. Here, we searched for a shared structural footprint using magnetic resonance imaging-based macroscopic screening and super-resolution microscopy-based microscopic identification. We identified the enlarged axon terminals of striatal medium spiny neurons in both LID and TD model mice. The striatal overexpression of vesicular gamma-aminobutyric acid transporter (VGAT) was necessary and sufficient for modeling these structural changes; VGAT levels gated the functional and behavioral alterations in dyskinesia models. Our findings indicate that lowered type 2 dopamine receptor signaling with repetitive dopamine fluctuations is a common cause of VGAT overexpression and late-onset dyskinesia formation, and that reducing dopamine fluctuation rescues dyskinesia pathology via VGAT downregulation. HighlightsO_LIEnhancement of GABAergic transmission is a shared mechanism between LID and TD. C_LIO_LIVGAT levels in MSNs govern the structure and function of MSN presynaptic terminals. C_LIO_LIGain and loss of VGAT function in MSNs exacerbates and ameliorates dyskinesia. C_LIO_LILowered D2 signaling with repetitive DA fluctuations causes VGAT overexpression. C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=188 HEIGHT=200 SRC="FIGDIR/small/550763v1_ufig1.gif" ALT="Figure 1"> View larger version (47K): org.highwire.dtl.DTLVardef@15041b4org.highwire.dtl.DTLVardef@9bf41org.highwire.dtl.DTLVardef@1eb7ee4org.highwire.dtl.DTLVardef@1d7dae8_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2022

Pupil dynamics-derived sleep stage classification of a head-fixed mouse using a recurrent neural network

The standard method for sleep state classification is thresholding amplitudes of electroencephalography (EEG) and electromyography (EMG), followed by an experts manual correction. Although popular, the method entails some shortcomings: 1) the time-consuming manual correction by human experts is sometimes a bottleneck hindering sleep studies; 2) EEG electrodes on the skull interfere with wide-field imaging of the cortical activity of a head-fixed mouse under a microscope; 3) invasive surgery to fix the electrodes on the thin skull of a mouse risks brain tissue injury; and 4) metal electrodes for EEG and EMG are difficult to apply to some experiment apparatus such as that for functional magnetic resonance imaging. To overcome these shortcomings, we propose a pupil dynamics-based vigilance state classification for a head-fixed mouse using a long short-term memory (LSTM) model, a variant of recurrent neural networks, for multi-class labeling of NREM, REM, and WAKE states. For supervisory hypnography, EEG and EMG recording were performed for a head-fixed mouse, combined with left eye pupillometry using a USB camera and a markerless tracking toolbox, DeepLabCut. Our open-source LSTM model with feature inputs of pupil diameter, location, velocity, and eyelid opening for 10 s at a 10 Hz sampling rate achieved vigilance state estimation with a higher classification performance (macro F1 score, 0.77; accuracy, 86%) than a feed forward neural network. Findings from diverse pupillary dynamics implied subdivision of a vigilance state defined by EEG and EMG. Pupil dynamics-based hypnography can expand the scope of alternatives for sleep stage scoring of head fixed mice.

neuroscience↗