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Presented by Malaika Mahmood

Spinal cord injury (SCI) disrupts communication between the brain and the body, completely altering a person’s function. Current Challenges in SCI Research: inability to understand how individual movements are affected after injury. Due to the complexity of the disorder, there is also lack of understanding of its neuropathology, which makes it difficult to treat. Currently, research in SCI recovery is assessed using the standard Basso Mouse Scale (BMS). This is a famous scale that was established for thoracic SCI in mice.

Problem with BMS: dependent on human observation and unable to assess overall behavior.

Proposed solution: Behavior Biomarker Scale (BBS), which uses a Microsoft Kinect camera to record mice in an open field, making it easier to track complex motor behavior.

  • Motion Sequencing (MoSeq) is the original machine learning algorithm developed at Harvard to identify 3D movements at sub-second interval.
  • Our lab further developed this for neuropathological models, specifically thoracic SCI in mice.
  • In conjunction to assigning BMS scores, mice were put in an open field under the supervision of BBS for 20 mins. Through the advancement of BBS, every motion was sequenced by the software into modules (behaviors). BBS Reports creates clear and interactive model that displays the behavioral data into various formats.Objective:ØTest predictability of BMS in regards to histological dataØTest sensitivity of BMS and BBS by observing module usage

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