LASSI-L detects early cognitive changes in pre-motor manifest Huntington's disease: a replication and validation study.

Background and objectives: Cognitive decline is an important early sign in pre-motor manifest Huntington's disease (preHD) and is characterized by deficits across multiple domains including executive function, psychomotor processing speed, and memory retrieval. Prior work suggested that the Loewenstein-Acevedo Scale for Semantic Interference and Learning (LASSI-L)-a verbal learning task that simultaneously targets these domains - could capture early cognitive changes in preHD. The current study aimed to replicate, validate and further analyze the LASSI-L in preHD using larger datasets. Methods: LASSI-L was administered to 50 participants (25 preHD and 25 Healthy Controls) matched for age, education, and sex in a longitudinal study of disease progression and compared to performance on MMSE, Trail A & B, SCWT, SDMT, Semantic Fluency (Animals), and CVLT-II. Performance was then compared to a separate age-education matched-cohort of 25 preHD participants. Receiver operating curve (ROC) and practice effects (12 month interval) were investigated. Group comparisons were repeated using a preHD subgroup restricted to participants predicted to be far from diagnosis (Far subgroup), based on CAG-Age-Product scaled (CAPs) score. Construct validity was assessed through correlations with previously established measures of subcortical atrophy. Results: PreHD performance on all sections of the LASSI-L was significantly different from controls. The proactive semantic interference section (PSI) was sensitive (p = 0.0001, d = 1.548), similar across preHD datasets (p = 1.0), reliable on test-retest over 12 months (spearman rho = 0.88; p = <0.00001) and associated with an excellent area under ROC (AUROC) of 0.855. In the preHD Far subgroup comparison, PSI was the only cognitive assessment to survive FDR < 0.05 (p = 0.03). The number of intrusions on PSI was negatively correlated with caudate volume. Discussion: The LASSI-L is a sensitive, reliable, efficient tool for detecting cognitive decline in preHD. By using a unique verbal learning test paradigm that simultaneously targets executive function, processing speed and memory retrieval, the LASSI-L outperforms many other established tests and captures early signs of cognitive impairment. With further longitudinal validation, the LASSI-L could prove to be a useful biomarker for clinical research in preHD.

Prevalence of neurocognitive disorder in Huntington's disease using the Enroll-HD dataset.

Background: Cognitive decline in Huntington's disease (HD) begins early in the disease course, however the reported prevalence and severity of cognitive impairment varies based on diagnostic approach. A Movement Disorders Society Task Force recently endorsed the use of standardized DSM-5-based criteria to diagnose neurocognitive disorder (NCD) in Huntington's disease. Objectives: To determine the prevalence and severity of cognitive impairment across different stages of HD by applying NCD criteria (mild and major) to participant data from the Enroll-HD database. Methods: Enroll-HD participants were triaged into either premanifest (preHD), manifest or control groups. PreHD was further dichotomized into preHD near or preHD far based on predicted time to diagnosis using the scaled CAG-age product score (CAPs). Embedded cognitive performance and functional independence measures were used to determine prevalence of NCD (mild and major) for all groups. Results: Prevalence of NCD-mild was 25.2%-38.4% for manifest HD, 22.8%-47.3% for preHD near, 11.5%-25.1% for preHD far, and 8.8%-19.1% for controls. Prevalence of NCD-major was 21.1%-57.7% for manifest HD, 0.5%-16.3% for preHD near, 0.0%-4.5% for preHD far, and 0.0%-3.0% for controls. Conclusion: The prevalence of NCD in HD is elevated in preHD and demonstrates a sharp rise prior to diagnosis. In manifest HD, the vast majority of participants meet criteria for NCD. These findings are important for optimizing clinical care and/or anticipating the need for supportive services.

Electrical Impedance Myography in Dogs With Degenerative Myelopathy.

Canine degenerative myelopathy (DM) leads to disuse and neurogenic muscle atrophy. Currently there is a lack of non-invasive quantitative measures of muscle health in dogs with DM. Muscle pathology has been previously quantified in other disorders using the technique of electrical impedance myography (EIM) but it has not been reported for DM. The objective of this study was to compare EIM between DM-affected and similar aged healthy dogs as well as assess EIM changes over time in DM-affected dogs. Multifrequency EIM was performed on DM affected dogs at baseline and during disease progression and on age-matched healthy dogs. Muscles evaluated in the pelvic limbs included the craniotibialis, gastrocnemius, gracilis, sartorius, and biceps femoris. The 100 kHz phase angle was extracted from the full frequency set for analysis. Phase values were lower in DM dogs as compared to healthy controls. Specifically, phase of the gastrocnemius was lower on the left (θ = 7.69, 13.06; p =0.002) and right (θ= 6.11, 11.72; p = 0.001) in DM vs. control dogs, respectively. The mean phase value of all measured muscles was also lower on the left (θ = 9.24, 11.62; p = 0.012) and right (θ = 9.18, 11.72; p = 0.021). Other individual muscles measured did not reach statistical significance, although values were consistently lower in DM-affected dogs. With disease progression, downward trends in phase values were detected in DM-affected dogs when monitored serially over time. This study demonstrates that EIM 100 kHz phase values are sensitive to muscle pathology in DM and that phase values are decreased in dogs with DM. Measurements from the gastrocnemius muscle show the greatest differences from similar aged healthy dogs suggesting it may be the preferred muscle for future EIM studies.

Using machine learning algorithms to enhance the diagnostic performance of electrical impedance myography.

INTRODUCTION/AIMS: We assessed the classification performance of machine learning (ML) using multifrequency electrical impedance myography (EIM) values to improve upon diagnostic outcomes as compared to those based on a single EIM value. METHODS: EIM data was obtained from unilateral excised gastrocnemius in eighty diseased mice (26 D2-mdx, Duchenne muscular dystrophy model, 39 SOD1G93A ALS model, and 15 db/db, a model of obesity-induced muscle atrophy) and 33 wild-type (WT) animals. We assessed the classification performance of a ML random forest algorithm incorporating all the data (multifrequency resistance, reactance and phase values) comparing it to the 50 kHz phase value alone. RESULTS: ML outperformed the 50 kHz analysis as based on receiver-operating characteristic curves and measurement of the area under the curve (AUC). For example, comparing all diseases together versus WT from the test set outputs, the AUC was 0.52 for 50 kHz phase, but was 0.94 for the ML model. Similarly, when comparing ALS versus WT, the AUCs were 0.79 for 50 kHz phase and 0.99 for ML. DISCUSSION: Multifrequency EIM using ML improves upon classification compared to that achieved with a single-frequency value. ML approaches should be considered in all future basic and clinical diagnostic applications of EIM.

Electrical impedance myography in healthy dogs: Normative values, repeatability, and the impact of age.

Convenient tools to assess canine skeletal muscle health would be useful for a variety of applications, including standard veterinary assessments of dog fitness, as well as studies of muscle deterioration due to age or disease. One technology that can be applied conveniently to awake dogs with minimal restraint is electrical impedance myography (EIM). In EIM, a weak electrical current is applied via surface electrodes to a muscle of interest and consequent impedance characteristics of the muscle are obtained, providing insight into muscle condition and composition. In this study, we assessed a total of 73 dogs (42 males and 31 females), of varied neutering status and breed, ages 0.6 to 13.5 years. We identified age-dependent reference values for the 100 kHz phase value in three pelvic limb muscles, caudal sartorius, cranial tibial, and gastrocnemius. While phase values were generally higher in males than females, the difference did not reach significance. In general, values declined on average with age at about 0.5 degrees/year, but with the decline being most substantial in the oldest dogs. Limited reproducibility assessment of the technique suggested good repeatability with variation in values between measurements being under 5%. These results show that EIM has the potential for the assessment of canine muscle health and may find value in aging muscle research.

Relationships between in vivo surface and ex vivo electrical impedance myography measurements in three different neuromuscular disorder mouse models.

Electrical impedance myography (EIM) using surface techniques has shown promise as a means of diagnosing and tracking disorders affecting muscle and assessing treatment efficacy. However, the relationship between such surface-obtained impedance values and pure muscle impedance values has not been established. Here we studied three groups of diseased and wild-type (WT) animals, including a Duchenne muscular dystrophy model (the D2-mdx mouse), an amyotrophic lateral sclerosis (ALS) model (the SOD1 G93A mouse), and a model of fat-related atrophy (the db/db diabetic obese mouse), performing hind limb measurements using a standard surface array and ex vivo measurements on freshly excised gastrocnemius muscle. A total of 101 animals (23 D2-mdx, 43 ALS mice, 12 db/db mice, and corresponding 30 WT mice) were studied with EIM across a frequency range of 8 kHz to 1 MHz. For both D2-mdx and ALS models, moderate strength correlations (Spearman rho values generally ranging from 0.3-0.7, depending on the impedance parameter (i.e., resistance, reactance and phase) were obtained. In these groups of animals, there was an offset in frequency with impedance values obtained at higher surface frequencies correlating more strongly to impedance values obtained at lower ex vivo frequencies. For the db/db model, correlations were comparatively weaker and strongest at very high and very low frequencies. When combining impedance data from all three disease models together, moderate correlations persisted (with maximal Spearman rho values of 0.45). These data support that surface EIM data reflect ex vivo muscle tissue EIM values to a moderate degree across several different diseases, with the highest correlations occurring in the 10-200 kHz frequency range. Understanding these relationships will prove useful for future applications of the technique of EIM in the assessment of neuromuscular disorders.

Estimating myofiber cross-sectional area and connective tissue deposition with electrical impedance myography: A study in D2-mdx mice.

INTRODUCTION: Surface electrical impedance myography (sEIM) has the potential for providing information on muscle composition and structure noninvasively. We sought to evaluate its use to predict myofiber size and connective tissue deposition in the D2-mdx model of Duchenne muscular dystrophy (DMD). METHODS: We applied a prediction algorithm, the least absolute shrinkage and selection operator, to select specific EIM measurements obtained with surface and ex vivo EIM data from D2-mdx and wild-type (WT) mice (analyzed together or separately). We assessed myofiber cross-sectional area histologically and hydroxyproline (HP), a surrogate measure for connective tissue content, biochemically. RESULTS: Using WT and D2-mdx impedance values together in the algorithm, sEIM gave average root-mean-square errors (RMSEs) of 26.6% for CSA and 45.8% for HP, which translate into mean errors of ±363 µm2 for a mean CSA of 1365 µm2 and of ±1.44 µg HP/mg muscle for a mean HP content of 3.15 µg HP/mg muscle. Stronger predictions were obtained by analyzing sEIM data from D2-mdx animals alone (RMSEs of 15.3% for CSA and 34.1% for HP content). Predictions made using ex vivo EIM data from D2-mdx animals alone were nearly equivalent to those obtained with sEIM data (RMSE of 16.59% for CSA), and slightly more accurate for HP (RMSE of 26.7%). DISCUSSION: Surface EIM combined with a predictive algorithm can provide estimates of muscle pathology comparable to values obtained using ex vivo EIM, and can be used as a surrogate measure of disease severity and progression and response to therapy.

Predicting myofiber cross-sectional area and triglyceride content with electrical impedance myography: A study in db/db mice.

BACKGROUND: Electrical impedance myography (EIM) provides insight into muscle composition and structure. We sought to evaluate its use in a mouse obesity model characterized by myofiber atrophy. METHODS: We applied a prediction algorithm, ie, the least absolute shrinkage and selection operator (LASSO), to surface, needle array, and ex vivo EIM data from db/db and wild-type mice and assessed myofiber cross-sectional area (CSA) histologically and triglyceride (TG) content biochemically. RESULTS: EIM data from all three modalities provided acceptable predictions of myofiber CSA with average root mean square error (RMSE) of 15% in CSA (ie, ±209 µm2 for a mean CSA of 1439 µm2 ) and TG content with RMSE of 30% in TG content (ie, ±7.3 nmol TG/mg muscle for a mean TG content of 25.4 nmol TG/mg muscle). CONCLUSIONS: EIM combined with a predictive algorithm provides reasonable estimates of myofiber CSA and TG content without the need for biopsy.