Biology subjects

Hsu, A. I.

Publications and source records attributed to Hsu, A. I..

3 recordsLinked to original sources

bioRxiv2024

Mimicking opioid analgesia in cortical pain circuits

The anterior cingulate cortex is a key brain region involved in the affective and motivational dimensions of pain, yet how opioid analgesics modulate this cortical circuit remains unclear. Uncovering how opioids alter nociceptive neural dynamics to produce pain relief is essential for developing safer and more targeted treatments for chronic pain. Here we show that a population of cingulate neurons encodes spontaneous pain-related behaviors and is selectively modulated by morphine. Using deep-learning behavioral analyses combined with longitudinal neural recordings in mice, we identified a persistent shift in cortical activity patterns following nerve injury that reflects the emergence of an unpleasant, affective chronic pain state. Morphine reversed these neuropathic neural dynamics and reduced affective-motivational behaviors without altering sensory detection or reflexive responses, mirroring how opioids alleviate pain unpleasantness in humans. Leveraging these findings, we built a biologically inspired gene therapy that targets opioid-sensitive neurons in the cingulate using a synthetic mu-opioid receptor promoter to drive chemogenetic inhibition. This opioid-mimetic gene therapy recapitulated the analgesic effects of morphine during chronic neuropathic pain, thereby offering a new strategy for precision pain management targeting a key nociceptive cortical opioid circuit with safe, on-demand analgesia. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=40 SRC="FIGDIR/small/591113v2_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@d294c0org.highwire.dtl.DTLVardef@123d4f4org.highwire.dtl.DTLVardef@1720deborg.highwire.dtl.DTLVardef@efc632_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2022

A-SOiD, an active learning platform for expert-guided, data efficient discovery of behavior

To identify and extract naturalistic behavior, two schools of methods have become popular: supervised and unsupervised. Each approach carries its own strengths and weaknesses, which the user must weigh in on their decision. Here, a new active learning platform, A-SOiD, blends these strengths and, in doing so, overcomes several of their inherent drawbacks. A-SOiD iteratively learns user-defined groups and can considerably reduce the necessary training data while attaining expansive classification through directed unsupervised classification. In socially-interacting mice, A-SOiD outperformed other methods and required 85% less training data than was available. Additionally, it isolated two additional ethologically-distinct mouse interactions via unsupervised classification. Similar performance and efficiency were observed using non-human primate 3D pose data. In both cases, the transparency in A-SOiDs cluster definitions revealed the defining features of the supervised classification through a game-theoretic approach. Lastly, we show the potential of A-SOiD to segment a large and rich variety of human social and single-person behaviors with 3D position keypoints. To facilitate use, A-SOiD comes as an intuitive, open-source interface for efficient segmentation of user-defined behaviors and discovered sub-actions.

animal behavior and cognition↗

bioRxiv2019

B-SOiD: An Open Source Unsupervised Algorithm for Discovery of Spontaneous Behaviors

Studying naturalistic behavior remains a prohibitively difficult objective. Recent machine learning advances have enabled limb localization. Extracting behaviors, however, requires ascertaining the spatiotemporal patterns of these positions. To provide the missing bridge from poses to actions and their kinematics, we developed B-SOiD - an open-source, unsupervised algorithm that identifies behavior without user bias. By training a machine classifier on pose pattern statistics clustered using new methods, our approach achieves greatly improved processing speed and the ability to generalize across subjects or labs. Using a frameshift alignment paradigm, B-SOiD overcomes previous temporal resolution barriers that prevent the use of other algorithms with electrophysiological recordings. Using only a single, off-the-shelf camera, B-SOiD provides categories of sub-action for trained behaviors and kinematic measures of individual limb trajectories in an animal model. These behavioral and kinematic measures are difficult but critical to obtain, particularly in the study of pain, OCD, and movement disorders.

neuroscience↗