Each year,
11,000 Americans suffer spinal cord injury. Victims of severe spinal
cord injury may suffer symptoms as severe as paraplegia, quadriplegia,
and death. Currently we have no means of restoring locomotor function
to patients who have suffered severe neural tissue damage resulting
from spinal cord injury. While ideal treatments for such injuries involve
regenerating the damaged tissues or developing compensatory neural connections,
these options are not yet feasible. For patients who have lost the ability
to walk, however, promising studies indicate that properly conducted,
systematic motor training may help them walk again.
Our research focuses on developing robotic systems that provide the
requisite level of precision and systematic control for retraining spinally
injured patients to walk. Manual methods of training are inconsistently
effective, and the reason is clear. Human trainers cannot consistently
apply training protocols from step-to-step, session-to-session, or subject-to-subject.
When training protocols are not uniformly applied, they cannot be adequately
evaluated. Robotic systems eliminate the limitations of human hands,
enabling systematic application and evaluation of training protocols.
Whereas manual methods only provide a qualitative notion of locomotor
performance improvements, robotic systems provide quantitative results.
Systematic training, such as that provided by robotic systems, is crucial
to advancing our knowledge of the rehabilitation procedures that must
be applied following traumatic spinal cord injury.
The goal of our research is to improve our understanding of how learning
occurs in the spinal cord following traumatic injury. Our current project
is the development of a robotic step training device that will enable
us to precisely control and record gait patterns of normal and spinally
injured mice. The wide availability of naturally mutated and genetically
engineered mice provides us a unique opportunity to identify the biochemical
cascades that enable learning-related motor behaviors to occur. Using
our device in conjunction with these strains will allow us to observe
the phenotypic manifestations of genetic alterations on the learning
process, thereby helping us identify factors important to spinal learning.
The prescription for teaching spinal-injured patients to walk is an
efficient combination of systematic training protocols and pharmacological
agonists. Blending robotic analysis with transgenic mouse models will
enable us to find this combination.
The use of transgenic mice pervades biological research, and thus the
utility of a mouse robotic stepper extends well beyond our research
goals. The mouse stepper provides researchers with extensive quantitative
data directly relating genetic alterations in transgenic mice to motor
activity. Understanding gene expression is the goal of proteomics. Our
device will help identify proteins related to learning and will enable
mapping of their origins in the genetic code. Thus the mouse stepper
will not only help us study spinal learning, it will be a valuable tool
to all investigators studying transgenic mice.
To date, we have successfully completed a working prototype of the mouse
robotic stepper along with basic data analysis software. (Figure 1)
We have also completed a pilot experiment examining the effect of robotic
training in conjunction with quipazine, a serotonin agonist. The results
of which indicate that mice can successfully step while in the robot
and that the device can reliably distinguish various gait patterns.
(Figure 2) Looking at the number of steps performed, as well as other
measurements (not presented here), we can conclude that the trained
subjects both outperformed the controls throughout the study and retained
a higher level of performance after training ceased. Thus a long-term
learning effect had indeed been produced. Based on these results, we
will pursue our first full study within six weeks, pending animal preparation.
Meanwhile, we will concentrate on developing video synchronization software
that will provide video data time-matched to our robotic data. This
software will help us understand how manifestations in our robotic data
correspond to visual stepping events. Ultimately, as we become more
proficient in our understanding of the raw data, we hope to predict
gait patterns before viewing the video footage.
Figure 1. Current Step training system
Our work
plan includes four near-term studies. The goal of the first study is
to show that the robot can distinguish normal and abnormal modes of
stepping and quantify the differences between transected and non-transected
animals. We expect, for instance, that normal mice will spend more time
in an alternating gait than transected mice. The differences between
transected and control mice will provide the criteria for gauging performance
improvement in training studies. The second study will investigate the
effect of body position on step trajectory. Body position greatly affects
how a subject steps, and we will determine if there is a preferred body
angle that is particularly amenable to step training. Our third experiment
will compare two training protocols. The first will impose a fixed trajectory
pattern recorded from normal mice onto a group of transected neonatal
mice. The second protocol will use the uncontrolled trajectory of one
leg to control the trajectory of the contralateral leg. The basis for
this method arose from the observations that neonatal mice often develop
some degree of locomotor capability in one leg following transection.
Finally, our fourth study will initiate our study of transgenic mice.
We are collaborating with another lab on campus to investigate neural
degradation of the spinal cord following stab injuries. Animals of this
particular strain are unique in that they maintain locomotor capacity
immediately following the infliction of the wound, but slowly and completely
degrade in performance over fourteen days. These animals will allow
us to monitor the temporal pattern of changes following injury and may
indicate the key tragic events that lead to step failure.
Figure 2. Total number of steps performed during a two minute
interval. First data point corresponds to the test date after controls
group received no training while the trained group received three weeks
of manual training. The second data point corresponds to the test date
after the trained group received three weeks robotic training while
the control groups still received no training. Third data point corresponds
to the test date where both controls and trained group where given quipazine
for two weeks but only the trained group received robotic training.
The fourth data point corresponds to last testing day where both the
control and trained group hasn’t received training or quipazine
and weren’t handled for two weeks. Last data point corresponds
to the same test day after both group where administrated quipazine.
With continued research of step training methods, the outlook is good
that spinal cord injured patients who have lost their ability to walk
will walk again. Transgenic mice models are crucial in advancing this
goal because they give us insight into both physical training methods
and the biochemical cascades involved in learning. They will provide
cues as to how to pursue pharmacological agonists that can facilitate
step training. The key to our research is the use of robotics. The precise
control that robotic systems afford us enables us to rid ourselves of
the imprecision of manual control approaches. Whereas previously only
inconclusive, qualitative results could be obtained, robotics will allow
us to implement consistent and systematic training methods and enable
us to draw quantitative conclusions regarding step training. Combining
engineering and biological research will facilitate quantum leaps in
our understanding of learning and learning-related phenomena. Pursuing
research with our mouse robotic stepper will help bring the 250,000
people in the United States suffering from spinal cord injury one step
closer to walking again.
