ABSTRACT
BACKGROUND: Although important for dosing and dilution, there are few data describing botulinum toxin (BT) movement in human muscle. OBJECTIVE: To better understand BT movement within human muscle. DESIGN: Proof-of-concept study with descriptive case series. SETTING: Outpatient academic practice. PARTICIPANTS: Five subjects with stroke who were BT naive with a mean age of 60.4 ± 14 years and time poststroke of 4.6 ± 3.7 years. METHODS: Three standardized injections were given to the lateral gastrocnemius muscle (LGM): 2 contained 25 units (U) of onabotulinumtoxinA (Botox) in 0.25 mL of saline solution and the third 0.25 mL of saline solution only. The tibialis anterior muscle (TAM) was not injected in any subject. A leg magnetic resonance image was obtained at baseline, 2 months, and 3 months later with a 3.0 Tesla Siemens scanner. Three muscles, the LGM, lateral soleus muscle (LSM), and TAM, were manually outlined on the T2 mapping sequence at each time point. A histogram of T2 relaxation times (T2-RT) for all voxels at baseline was used to calculate a mean and standard deviation (SD) T2-RT for each muscle. Botulinum toxin muscle effect (BTME) at 2 months and 3 months was defined as a subject- and muscle-specific T2-RT voxel threshold ≥3 SD above the baseline mean at or near BT injection sites. MAIN OUTCOME MEASURES: BTME volume for each leg magnetic resonance imaging slice at 3 time points and 3 muscles for all subjects. RESULTS: One subject missed the 3-month scan, leaving 18 potential observations of BTME. Little to no BTME effect was seen in the noninjected TAM. A BTME was detected in the LGM in 13 of 18 possible observations, and no effect was detected in 5 observations. Possible BTME effect was seen in the LSM in 3 subjects due to either diffusion through fascia or needle misplacement. Volume of BTME, as defined here, appeared to be substantially greater than the 0.25-mL injection volume. CONCLUSIONS: This descriptive case series is among the first attempts to quantify BTME within human muscle. Our findings are preliminary and are limited by a few inconsistencies. However, we conclude that use of magnetic resonance imaging to detect the volume of BTME is feasible and may assist researchers in modeling the spread and diffusion of BT within human muscle. LEVEL OF EVIDENCE: IV.
Subject(s)
Botulinum Toxins, Type A/administration & dosage , Magnetic Resonance Imaging/methods , Muscle Contraction/physiology , Muscle, Skeletal/pathology , Stroke Rehabilitation/methods , Stroke/diagnosis , Aged , Female , Follow-Up Studies , Humans , Injections, Intramuscular , Male , Middle Aged , Muscle, Skeletal/physiopathology , Neuromuscular Agents/administration & dosage , Reproducibility of Results , Stroke/physiopathology , Time FactorsABSTRACT
The aim of this work was to quantitatively model cross-sectional relationships between structural connectome disruptions caused by cerebral infarction and measures of clinical performance. Imaging biomarkers of 41 ischemic stroke patients (72.0 ± 12.0 years, 20 female) were related to their baseline performance in 18 cognitive, physical and daily life activity assessments. Individual estimates of structural connectivity disruption in gray matter regions were computed using the Change in Connectivity (ChaCo) score. ChaCo scores were utilized because they can be calculated using routinely collected clinical magnetic resonance imagings. Partial Least Squares Regression (PLSR) was used to predict various acute impairment and activity measures from ChaCo scores and patient demographics. Statistical methods of cross-validation, bootstrapping and multiple comparisons correction were implemented to minimize over-fitting and Type I errors. Multiple linear regression models based on lesion volume and lateralization information were constructed for comparison. All models based on connectivity disruption had lower Akaike Information Criterion and almost all had better goodness-of-fit values (R(2) : 0.26-0.92) than models based on lesion characteristics (R(2) : 0.06-0.50). Confidence intervals of PLSR coefficients identified brain regions important in predicting each clinical assessment. Appropriate mapping of eloquent functions, that is, language and motor, and replication of results across pathologies provided validation of this method. Models of complex functions provided new insights into brain-behavior relationships. In addition to the potential applications in prognostication and rehabilitation development, this quantitative approach provides insight into the structural networks underlying complex functions like activities of daily living and cognition. Quantitative analysis of big data will be invaluable in understanding complex brain-behavior relationships.
