3Department of Biomedical Engineering, Northwestern University, IL, USA, 4Sensory Motor Performance. Program, Rehabilitation Institute of Chicago, IL, USA.
Proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference Shanghai, China, September 1-4, 2005
Mechanisms And Rehabilitation Of Discoordination Following Stroke Using A Cortical Imaging Method 1
Jun Yao1, member, IEEE, Michael D. Ellis1 and Julius Dewald1, 2, 3, 4, member, IEEE Department of Physical Therapy and Human Movement Sciences, 2 Department of Physical Med & Rehab., 3 Department of Biomedical Engineering, Northwestern University, IL, USA, 4Sensory Motor Performance Program, Rehabilitation Institute of Chicago, IL, USA
Abstract- Mechanisms underlying discoordination, expressed in the form of obligatory coupling between the shoulder and the elbow muscles following stroke, are probed by simultaneously recording 163-channel EEG together with elbow/shoulder torques and EMGs from the upper arm. With this unique experimental protocol, we were able to have strict post hoc control of a subject’s motor performance. Using this novel approach, this paper provides the first evidence of a linear relationship between an overlap in the cortical activities and obligatory shoulder/elbow torque coupling. Furthermore, results obtained from an 8 weeks multi-degree of freedom isometric training protocol showed that a well-designed treatment intervention could reduce obligatory torque coupling following stroke. Preliminary data indicates that this change in torque coupling appears to be associated with brain reorganization. Other potential rehabilitation methods based on an increased understanding of the mechanisms underlying discoordination following stroke are discussed.
I. INTRODUCTION Discoordination, reflected as the loss of independent control of joint movement, in the impaired limb is a cardinal sign of movement discoordination following stroke. An example of this behavior is the obligatory coupling between shoulder abduction (SABD) and elbow flexion (EF) in the paretic upper limb, described as the ‘flexor synergy’ by Twitchell (1951) and Brunnstrom (1970) of the paretic upper limb [1, 2]. Recently, discoordination was further quantified both using electromyographic recordings (EMGs) and mechanical measurements [3-5]. The abnormal torque coupling and associated muscle coactivation patterns indicate changes in descending motor commands. Changes in descending commands could be related to a reorganization of the sensorimotor cortex (SMC) following a stroke [6, 7]. Reorganization of the SMC following brain lesion has been intensively studied using different functional cortical imaging modalities over the last 15 years. A general finding using these various approaches is that brain reorganization enhances brain activities in preexisting networks, reflecting as the involvement of enlarged active areas [8-11] and increased amplitudes of cortical activity [11, 12]. However, most of previous reports are based on poorly controlled finger/hand experimental methods using imaging modalities with limited time resolutions, such as fMRI and PET. Furthermore, due to the lack of simultaneous recordings of imaging data and periphery performance, the possible link between changes in cortical activities and the lack of independent joint control following stroke has never been probed.
0-7803-8740-6/05/$20.00 ©2005 IEEE.
In this study, we implemented a highly controlled motor task by concurrently measuring EMGs from both arms and torques/forces generated with the paretic elbow and shoulder during the EEG recording. This insured a repeatable task and rigorous verification of the lack of unwanted muscle activity in the paretic and non-paretic limbs. More importantly, this novel approach enabled us not only to observe the changes in cortical activity related to torque generation at the shoulder and elbow, but also investigated the possible link between changes in cortical activity and the lack of independent joint control following stroke. In the latter parts of this paper, we provided evidence to show the correlation between overlap of cortical activity and the lack of independent joint control. Finally, preliminary results of brain reorganization in a chronic hemiparetic stroke subject, who followed a well-designed rehabilitation method, are reported followed by a discussion of other potential rehabilitation methods. II. METHODS A. Subjects We recruited 7 subjects with chronic hemiparetic stroke and 7 control subjects. The hemiparetic subjects suffered from a stroke at least 1.5 years before the testing are all moderately to severely impaired according to clinical assessment scales. All of the control subjects are right-hand dominant, denied a history of neurological injury or impairment and were not taking neuromuscular mediated medications. All subjects provided written consent prior to participation in the study that was approved by the Institutional Review Board of Northwestern University and in compliance with the principles of the Declaration of Helsinki. B. Motor paradigm for cortical imaging Participants sat in a high back Biodex chair that completely supported the trunk. The trunk was restrained to the back of chair with straps crossing the chest and abdomen to prevent trunk and pelvis motion during the experiment. Subjects were casted at the wrist and secured to a six degree of freedom (DOF) load cell with the shoulder at 75° abduction, 40° flexion and the elbow at a 90° flexion angle. In this position, the tip of the hand was approximately aligned with the median sagittal plane of the subject. The motor tasks involved in the study were self-initiated torque generations the directions of SABD or EF from resting to 25% of subject’s maximum voluntary torque (MVT). In each of the directions, subjects were required to
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perform 100~ 150 trials. At the very beginning of each trial, an auditory signal was given to the subject indicating the start of the task. After that, subjects were required to maintain a resting state for 5-7 seconds, and then, to selfinitiate the generation of torque in the required direction to 25%±8.75% of their MVT holding it for 0.3s. Subjects were instructed to avoid eye movements and movements of other parts of their body during the performance of each trial. Prior to the data collection session, subjects went through a training session to make sure that they were able to perform the elbow/shoulder torque generation task without visual feedback. Following the training session, an anatomical MRI of the brain was taken in a 3T magnet scanner (Siemens, Erlangen, Germany) with a head coil using a T1weighted gradient echo pulse sequence for each subject (0.9760.9761.0 mm) and the actual data collection session was scheduled. During the data collection session, MVT and maximum EMG of each subject was recorded in four randomly ordered blocks consisting of the torque generation of shoulder flexion /extension (SF/E), shoulder abduction/adduction (SABD /ADD), shoulder external/internal rotation (SEXT/INT), and elbow flexion/extension (EF/E). Subjects then performed the required motor tasks without visual feedback. In an effort to avoid fatigue, subjects completed 100 to 150 trials in several randomly ordered blocks (20 ~ 30 trials for one block) consisting of SABD or EF with rest periods of 15 seconds between trials, and 20 minutes between blocks. C. Intervention protocol We implemented a novel intervention study on chronic hemiparetic stroke subjects to alter abnormal torque coupling between SABD with EF using a multi-degree of freedom isometric upper extremity training protocol. Eight individuals with severe chronic stroke participated in the study. They were progressively trained across eight weeks (3 sessions/wk) to generate joint torque combinations away from the obligatory shoulder abduction/ elbow flexioncoupling pattern. Subjects were required to generate the following upper extremity joint torque combinations during each training session: Task 1) Maximum EE with maximum SF; Task 2) Sub-maximal SABD (% limb weight) with maximum EE; and Task 3) SABD (% limb weight), maximum EE and maximum SF combined. Each task progressively trained subjects to generate combinations of shoulder and elbow torque away from their abnormal torque patterns. Subjects trained in the same limb configuration as described for the imaging component of this study (see section B of the methods). The 6-DOF load cell was used to concurrently measure torques in all DOFs while the subject was given real-time feedback of the volitional torques generated in each specific task. D. Data collection for cortical imaging We collected force/moment signals, 13-channel EMG signals (i.e., the biceps brachii (BIC), brachioradialis (BRD), triceps brachii long head and lateral head (TRILO
and TRILA) muscles at the elbow; the anterior (ADL), intermediate (IDL), posterior deltoids (PDL), pectoralis major vertical and horizontal fibers (PMJV and PMJH) in the paretic (stroke)/dominant (control) arm and BIC, BRD, TRILA and IDL on the contralateral arm) and 163-channel EEG signals simultaneously during each data collection session. Data were sampled at 1000 Hz and stored on a desktop computer for off-line analysis. Furthermore, subject’s MRI and positions of EEG electrodes with respect to a coordinate system defined by the nasion, and preauricular notches were recorded using a three-dimensional magnetic digitizer (Polhemus, Colchester, VT). This allowed for the co-registration EEG electrodes with each subject’s MRI data. E. Data Analysis Torque and EMG signals were initially visually inspected. Trials with artifacts were eliminated. Remaining shoulder and elbow torques were baseline-corrected and averaged through a 250 ms moving window. The torque responses were then aligned with the off-line adjusted toque onset based on the TTL signal and ensemble-averaged. Torque responses were then normalized by the MVT yielding in % MVT for each degree of freedom (DOF). Remaining EMG signals were rectified and baseline corrected. Movement artifacts were removed and a low pass filter (zero lag, 6th order Butterworth) with a cutoff frequency of 30 Hz was applied. Off-line detected torque onsets were used to align EMGs from individual trials and compute the ensemble-average across trials. Averaged EMGs were then normalized to the highest EMG value obtained during the MVT efforts across the different DOFs. Muscle selectivity during the generation of the torque was measured by the Muscle Selection (MS) index (a scalar ranging from 0 to 1) [13]. This index is based on the mechanical action of each muscle about the shoulder and elbow joints in a 3D space (SF/E, SABD/ADD and EF/E) and its activation as measured by EMG. A MS index close to 1 indicates a high degree of muscle selectivity during a motor task, i.e., only the muscles that contribute to the specific torque direction are activated. (Note: a MS index equal to 1 cannot be achieved). A MS close to 0 indicates a lesser degree of muscle selectivity, i.e., muscle coactivation/co-contraction is present during the motor task. The MS index takes into account the presence of both single- and multi-joint muscle co-activations [13]. The offset between off-line and on-line detected torque onsets (i.e., TTL signal) was used to adjust markers for the onset of torque in the EEG data files. The adjusted markers were used to align the trials. The 163-channel EEG signals were visually inspected for the presence of artifacts. In addition to trials that were already eliminated due to the noisy torque or EMG, EEG trials that exhibited artifacts were eliminated from further analysis. The remaining EEG trials were segmented, baseline-corrected (-2000 to –1800 ms) and ensemble-averaged from 2000 ms prior to and 500 ms after the torque onset.
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showed that there was no significant difference in the maximum strength of currents in the SMCs during the generation of either SABD (p=0.3884) or EF (p=0.059) between the two groups. We didn’t find significant difference for the active area ratio between the two groups for either SABD (p=0.6862) or EF (p=0.3392) torque. 0.30
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Figure 1. Group means and standard errors for current strength (unit: µAmm), active area ratio (Area) and overlapping active area (OAA) in the SMCs for SABD and EF generation of stroke and control groups (*p
