Trial Outcomes & Findings for Investigation of Brain Functional MRI as an Early Biomarker of Recovery in Individuals With Spinal Cord Injury (NCT NCT03854214)

Investigation of Brain Functional MRI as an Early Biomarker of Recovery in Individuals With Spinal Cord Injury

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.

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.

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.

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.

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.

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.

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.

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.

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.