Early detection of response to therapeutic intervention is vital, as it will enable early termination of intervention in non-responding patients, prevent unnecessary financial burden, and allow for early changes to the intervention program. Previous functional MRI (fMRI) studies have shown that changes in brain functional network in spinal cord injury (SCI) patients can occur after as little as one week of intervention. Resting state fMRI (rsfMRI) is a type of fMRI that does not require performance of explicit motor tasks, which makes the method especially suitable for SCI patient population. In this project, the investigators propose that rsfMRI outcome measures can be used to detect early brain functional network changes that occur during intervention, and that the changes will be predictive of recovery in chronic SCI patients.
Early detection of response to spinal cord injury (SCI) therapeutic intervention programs is vital, as it will enable early termination of intervention in non-responding patients, prevent unnecessary financial burden, and allow for early changes of the programs. In this project, the investigators propose that resting state functional MRI (rsfMRI) can be used to detect early brain functional network changes that occur during intervention, and that the changes will be predictive of recovery in chronic SCI patients. The long-term goal of this study is to establish rsfMRI as a new imaging biomarker that is predictive of progress towards recovery in response to therapy. International Standard of Neurological Classification for Spinal Cord Injury (ISNCSCI) scoring system is the most widely used clinical classification system of SCI that describes neurological injury level and degree of functional preservation. It is also used to monitor the progress and response to interventions such as functional electrical stimulation (FES) therapy. However, monitoring responses using ISNCSCI is challenging, because its ability to describe the degree of functional loss is limited. Therefore, there is a need in the field of SCI for a biomarker that is more sensitive to changes in function. The investigators will recruit 2 groups of 24 chronic SCI patients. In one group, the investigators will characterize the baseline time profile of rsfMRI outcome measures acquired during a 4-weeks passive cycling program, where movement is driven only by the cycle's motor (no electric stimulation). RsfMRI data of the patients acquired at weeks 0, 2, and 4 will be used perform functional parcellation of the sensorimotor cortex using independent component analysis (ICA) and spectral clustering analysis (SCA) approaches. BNC will be calculated between pairs of sensory and motor brain parcels. Sensory and motor ISNCSCI scores will also be measured at weeks 0, 2, and 4. The investigators will then test the hypothesis that the investigators will observe stable baseline measures of sensory and motor cortex BNC and ISNCSCI scores of the patients during the 4-week passive cycling program, with minimal to no change in values. In the second group, the investigators will characterize the time profile of the cortical reorganization in chronic SCI patients that occurs during the four-week FES cycling. Specifically, the investigators predict that the investigators will observe early functional network changes in the sensorimotor cortex of SCI patients (measured using BNC) at week 2 of the four-week FES cycling program, which will be predictive of changes in ISNCSCI scores (neurological outcomes) at week 4. Finally, the longitudinal intra-subject reproducibility of the two parcellation methods will be investigated. If successful, the study will: 1) provide a new and effective clinical tool to study plastic cortical changes that occur after SCI, 2) provide a new non-invasive imaging biomarker that is predictive of progress towards recovery in response to therapy, and 3) extend our knowledge about the functional reorganization that takes place during and after therapeutic intervention.
Study Type
INTERVENTIONAL
Allocation
RANDOMIZED
Purpose
BASIC_SCIENCE
Masking
DOUBLE
Enrollment
14
The Functional Electrical Stimulation (FES) cycling group will use RT300 ergometer (Restorative Therapies, Inc). Bilateral glutei, quadriceps and hamstrings will be stimulated. The stimulation parameters will be set as follows: waveform biphasic, charged balanced; phase duration of 250 microseconds; pulse rate 33-45 pps. The stimulus intensity will be adjusted for individual patients and muscle group so that a tolerable stimulation is provided that will generate a cycling action. Target cycling speed is 50 revolutions per minute (RPM). Resistance will be automatically adjusted by the FES bike according to the subject's performance. When fatigue occurs, participants will continue cycling with electrical stimulation and motor support. FES therapy will be administered for one hour per session 3 times a week.
The passive cycling group will use the same RT300 ergometer however during this period stimulation will not be turned on. Instead, continuous motor support will be activated resulting in passive cycling. Target cycling speed is 50 RPM. Participants assigned to passive cycling will be required to have one hour of passive therapy 3 times a week for the entire duration of treatment assignment.
Kennedy Krieger Institute, International Center for Spinal Cord Injury
Baltimore, Maryland, United States
International Standard of Neurological Classification for Spinal Cord Injury (ISNCSCI) Score
Developed by the American Spinal Injury Association (ASIA), the International Standards for Neurological Classification of Spinal Cord Injury (ISNCSCI) provides an overall assessment of motor and sensory function following spinal cord injury. For this study, a single composite ISNCSCI score is reported, which ranges from 0 (indicating the worst overall function) to 324 (indicating normal overall function). The data table presents this composite score as the sole outcome measure for each Arm/Group.
Time frame: Baseline
International Standard of Neurological Classification for Spinal Cord Injury (ISNCSCI) Score
Developed by the American Spinal Injury Association (ASIA), the International Standards for Neurological Classification of Spinal Cord Injury (ISNCSCI) provides an overall assessment of motor and sensory function following spinal cord injury. For this study, a single composite ISNCSCI score is reported, which ranges from 0 (indicating the worst overall function) to 324 (indicating normal overall function). The data table presents this composite score as the sole outcome measure for each Arm/Group.
Time frame: 2 weeks
International Standard of Neurological Classification for Spinal Cord Injury (ISNCSCI) Score
Developed by the American Spinal Injury Association (ASIA), the International Standards for Neurological Classification of Spinal Cord Injury (ISNCSCI) provides an overall assessment of motor and sensory function following spinal cord injury. For this study, a single composite ISNCSCI score is reported, which ranges from 0 (indicating the worst overall function) to 324 (indicating normal overall function). The data table presents this composite score as the sole outcome measure for each Arm/Group.
Time frame: 4 weeks
Resting State fMRI Functional Connectivity
Resting state functional magnetic resonance imaging (RsfMRI) functional connectivity is defined as the temporal dependency of neuronal activation patterns (represented by the blood oxygenation level dependent (BOLD) signal time courses as measured using rsfMRI) of anatomically separated brain regions. There are number of methodologies one can use to characterize the degree and type of rsfMRI functional connectivity. One example is between-network-connectivity (BNC), which is defined as the degree of correlation between two time courses obtained from a pair of brain regions. Summary statistics of BNC (e.g., mean, variance), as well as the dynamic properties of BNC (e.g., dynamic functional connectivity) can be used to further summarize the characteristics of the functional connectivity in SCI population. Note that the BNC values reported in the Outcome Measure data table represent "Pearson's Correlation Coefficient" and not z-transformed Pearson's Correlation Coefficients.
Time frame: Baseline
Resting State fMRI Functional Connectivity
RsfMRI functional connectivity is defined as the temporal dependency of neuronal activation patterns (represented by the blood oxygenation level dependent (BOLD) signal time courses as measured using rsfMRI) of anatomically separated brain regions. There are number of methodologies one can use to characterize the degree and type of rsfMRI functional connectivity. One example is between-network-connectivity (BNC), which is defined as the degree of correlation between two time courses obtained from a pair of brain regions. Summary statistics of BNC (e.g., mean, variance), as well as the dynamic properties of BNC (e.g., dynamic functional connectivity) can be used to further summarize the characteristics of the functional connectivity in SCI population. Note that the BNC values reported in the Outcome Measure data table represent "Pearson's Correlation Coefficient" and not z-transformed Pearson's Correlation Coefficients.
Time frame: 2 weeks
Resting State fMRI Functional Connectivity
RsfMRI functional connectivity is defined as the temporal dependency of neuronal activation patterns (represented by the blood oxygenation level dependent (BOLD) signal time courses as measured using rsfMRI) of anatomically separated brain regions. There are number of methodologies one can use to characterize the degree and type of rsfMRI functional connectivity. One example is between-network-connectivity (BNC), which is defined as the degree of correlation between two time courses obtained from a pair of brain regions. Summary statistics of BNC (e.g., mean, variance), as well as the dynamic properties of BNC (e.g., dynamic functional connectivity) can be used to further summarize the characteristics of the functional connectivity in SCI population. Note that the BNC values reported in the Outcome Measure data table represent "Pearson's Correlation Coefficient" and not z-transformed Pearson's Correlation Coefficients.
Time frame: 4 weeks
Resting-State fMRI Brain Parcels Outcome Measure: Sensorimotor Network (SMN) Recruitment Coefficient
Resting-state functional connectivity can also identify functionally homogeneous brain regions, or "parcels." By examining each parcel's properties, such as the center of mass and recruitment coefficient value, we can gain insights into the brain's functional reorganization. Given its importance in the SCI population, we focused on the sensorimotor network (SMN) parcel. RsfMRI data were collected and preprocessed. The brain data was then parcellated into 200 parcels. Next, a multi-layer community detection algorithm was applied to identify cohesive subnetworks over time, and the SMN Recruitment Coefficient was calculated - which is a dimensionless metric that quantifies how strongly the SMN parcels cohere, or preferentially connect, with one another compared to parcels in other networks. Higher values suggest a more internally cohesive SMN, indicating stronger functional segregation and potentially more intact sensorimotor function.
Time frame: Baseline
Resting-State fMRI Brain Parcels Outcome Measure: Sensorimotor Network (SMN) Recruitment Coefficient
Resting-state functional connectivity can also identify functionally homogeneous brain regions, or "parcels." By examining each parcel's properties, such as the center of mass and recruitment coefficient value, we can gain insights into the brain's functional reorganization. Given its importance in the SCI population, we focused on the sensorimotor network (SMN) parcel. RsfMRI data were collected and preprocessed. The brain data was then parcellated into 200 parcels. Next, a multi-layer community detection algorithm was applied to identify cohesive subnetworks over time, and the SMN Recruitment Coefficient was calculated - which is a dimensionless metric that quantifies how strongly the SMN parcels cohere, or preferentially connect, with one another compared to parcels in other networks. Higher values suggest a more internally cohesive SMN, indicating stronger functional segregation and potentially more intact sensorimotor function.
Time frame: 2 weeks
Resting-State fMRI Brain Parcels Outcome Measure: Sensorimotor Network (SMN) Recruitment Coefficient
Resting-state functional connectivity can also identify functionally homogeneous brain regions, or "parcels." By examining each parcel's properties, such as the center of mass and recruitment coefficient value, we can gain insights into the brain's functional reorganization. Given its importance in the SCI population, we focused on the sensorimotor network (SMN) parcel. RsfMRI data were collected and preprocessed. The brain data was then parcellated into 200 parcels. Next, a multi-layer community detection algorithm was applied to identify cohesive subnetworks over time, and the SMN Recruitment Coefficient was calculated - which is a dimensionless metric that quantifies how strongly the SMN parcels cohere, or preferentially connect, with one another compared to parcels in other networks. Higher values suggest a more internally cohesive SMN, indicating stronger functional segregation and potentially more intact sensorimotor function.
Time frame: 4 weeks
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