Cross-correlations between gluteal muscle thickness derived from ultrasound imaging and hip biomechanics during walking gait

Journal Pre-proofs Cross-Correlations between Gluteal Muscle Thickness Derived from Ultrasound Imaging and Hip Biomechanics during Walking Gait Alexandra F. DeJong, Rachel M. Koldenhoven, Jay Hertel PII: DOI: Reference:

S1050-6411(20)30021-3 https://doi.org/10.1016/j.jelekin.2020.102406 JJEK 102406

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Journal of Electromyography and Kinesiology

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10 September 2019 11 February 2020 12 February 2020

Please cite this article as: A.F. DeJong, R.M. Koldenhoven, J. Hertel, Cross-Correlations between Gluteal Muscle Thickness Derived from Ultrasound Imaging and Hip Biomechanics during Walking Gait, Journal of Electromyography and Kinesiology (2020), doi: https://doi.org/10.1016/j.jelekin.2020.102406

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Cross-Correlations between Gluteal Muscle Thickness Derived from Ultrasound Imaging and Hip Biomechanics during Walking Gait Alexandra F. DeJong MEd, ATC1 Rachel M. Koldenhoven PhD, ATC2 Jay Hertel PhD, ATC1 Affiliations: 1University of Virginia Exercise and Sports Injury Lab, 210 Emmet Street South, Charlottesville, VA, USA 22904-4407 2 Texas State University Biomechanics/Sports Medicine Lab, 601 University Drive, San Marcos, TX, USA 78666-4616 Corresponding Author: Alexandra F. DeJong

Phone: (434) 924-6184 Fax: (434) 924-1389 [email protected] PO Box 400407 Memorial Gymnasium, Charlottesville, VA, USA, 22904

Abstract Ultrasound imaging (USI) of muscle thickness offers different insights into musculoskeletal function than kinematics, kinetics, and surface electromyography (sEMG), however it is unknown how USI-derived measures correlate to traditional measures during walking. The purpose of this study was to compare USI-derived gluteus maximus (GMAX) and medius (GMED) thickness measures to tri-planar hip kinematics and kinetics, and GMED thickness to sEMG amplitude. Fourteen females walked on a treadmill at 1.34 m/s. GMAX and GMED thickness, hip tri-planar kinematics, kinetics, and GMED sEMG were simultaneously recorded. USI-derived thickness measures were compared to other biomechanical outcomes using crosscorrelation analyses, computed at each 1% (11-ms) of the gait cycle with lag times from -20% to 20%. GMED and GMAX thickness measures were most strongly correlated with hip extension and abduction angles at 150-220-ms lags (cross-correlation coefficients [CCF]: -0.34; -0.83). GMED thickness was most correlated to abduction and external rotation moments simultaneously (CCF: -0.28; -0.47). GMAX thickness and flexion moments were most strongly correlated at a 66-ms lag (CCF: 0.33). GMED sEMG amplitude was most strongly correlated to muscle thickness at a 99-ms lag (CCF: 0.39). These results elucidate the unique information provided from USI-derived measures of gluteal muscle thickness during walking. Keywords: electromechanical delay, gait analysis, neuromuscular adaptations, musculoskeletal ultrasound

Introduction Gait analyses are an integral component of clinical evaluations to provide precise estimates of patients’ movement patterns, and to isolate aberrant biomechanical profiles in the context of injury and lower extremity dysfunction (Davis and Futrell, 2016). The majority of biomechanical analyses have been performed in the laboratory setting, and include kinematic, kinetic, and electromyographical (EMG) assessments. Kinematic and kinetic assessments provide insight into lower extremity joint excursions and forces throughout movement, while EMG assessments provide measures of muscle onset timing and amplitude through detection of neural impulses using either surface (sEMG) or intra-muscular approaches. Though these biomechanical measurement techniques are well-established, recent research has begun to incorporate novel techniques to evaluate muscle function through ultrasound imaging (USI) of the hip musculature during gait (DeJong et al., 2018, 2019). USI provides a non-invasive means to visualize skeletal muscles and determine the extent of muscle thickness changes during movement (Dieterich et al., 2014). USI as a muscle measurement technique is especially beneficial for examining layered muscle groups as there is not a potential for neuromuscular cross-talk as is seen with sEMG, thus the basis of the appeal for quantifying gluteus maximus (GMAX) and medius (GMED) activity using USI (DeJong et al., 2019; A. V. Dieterich et al., 2015). USI provides a different insight into muscle activity through a mechanical change in muscle thickness as opposed to an electrical impulse to the muscle during activation. Therefore, USI may be appealing to clinically assess muscle function, and may be considered as a supplement to traditional assessment tools. However, one of the current barriers of the gluteal USI during gait is the interpretation of muscle thickness outcomes as they relate to traditional gait biomechanics outcome measures including sEMG, kinematics, and kinetics. Understanding these relationships is important to contextualize the unique information that measures of muscle thickness provide to walking gait analyses.

Although USI is used to assess a different biomechanical property of the hip compared to sEMG, kinematics, and kinetics, the outcome measurements are undoubtedly interrelated, albeit asynchronously. Excitation-contraction coupling of skeletal muscle dictates that there is an inherent delay between neural impulses and muscle fiber shortening, or thickness changes (Frontera and Ochala, 2015). Measures of sEMG root mean square (RMS) amplitude and muscle thickness changes thus have underlying commonalities, however are measuring unequivocal components of muscle function and cannot be reasonably correlated at the same time point due to this electromechanical delay (Cavanagh and Komi, 1979; Go et al., 2018). Similarly, changes in muscle tension have been determined to relate to hip joint angle changes (Hoy et al., 1990; Neumann, 2010). When acting concentrically, the GMAX primarily extends the hip, while the GMED primarily produces hip abduction and external rotation (Neumann, 2010). The GMAX and GMED also help to control hip flexion, and adduction and internal rotation respectively when acting eccentrically, particularly during gait (Castermans et al., 2013; Neptune and McGowan, 2016). Muscular contractions must elicit enough force and fiber overlap to produce motion, which would logically relate to an extent of delay between muscle thickening and resultant movement (Hoy et al., 1990). External hip joint moments would also be important to examine in conjunction with USI-derived muscle thickness measures; external joint forces influencing pelvic motion would inherently change the demand on the gluteal muscles and in turn the extent of muscle thickness during walking (Castermans et al., 2013; Neptune et al., 2004; Neptune and McGowan, 2016; Neumann, 2010). Despite the theoretical associations among these measures, there are no investigations that have simultaneously evaluated USI-derived measures of muscle thickness and traditional biomechanical measures during walking to determine the relationship. Further, there is no current statistical evidence to demonstrate the electromechanical lag in gluteal sEMG measures to hip kinematic and kinetic changes during gait. Traditional approaches to correlation analyses

solely allow for comparisons across measures at single matched time-points, which would prove ineffective for determining asynchronous relationships on time-series gait data. Conversely, cross-correlation analyses allow for a similarity assessment of two distinct signals as a function of displacement, or time-lags, of one signal relative to the other (Li and Caldwell, 1999; NelsonWong et al., 2009). Cross-correlation analyses can therefore assess both the strength and temporal associations across measures, which can provide insights into how outcome measures of interests are related. This approach has previously been used to assess jointcoupling in ankle sprain populations during walking relative to the timing of signal similarities in the gait cycle (Herb et al., 2014; Lilley et al., 2017). Additionally, cross-correlations have been employed in biomechanical research to assess activation delay between sEMG signals across paraspinal muscle levels during walking (Prince et al., 1994), and across abdominal and gluteal muscles during squatting (Nelson-Wong et al., 2009). This statistical technique would help identify specific time-lags in which USI-derived measures of muscle thickness are most strongly related to kinematic, kinetic, and sEMG changes. The primary purpose of this study was to compare USI-derived gluteal muscle thickness measures against hip kinematics, kinetics, and sEMG using a cross-correlation analytic approach. GMAX thickness was compared to hip sagittal plane kinematics and kinetics, and GMED thickness was compared to hip frontal and transverse plane kinematics, kinetics, and GMED sEMG RMS amplitudes. We hypothesized that both the GMAX and GMED thickness measures would almost directly correspond with hip joint moments, but would precede hip kinematics due to morphological muscle changes and expected joint motions throughout gait (Castermans et al., 2013). We also hypothesized GMED sEMG RMS amplitude would precede GMED thickness measures due to electromechanical delay. Additionally, we aimed to compare GMED sEMG RMS amplitude to frontal and transverse plane kinematics and kinetics, with the hypothesis that sEMG would precede kinematic and kinetic changes. Methods

Fourteen physically active females were collected as a sample of convenience from a larger study assessing differences in gait kinematics, kinetics, and sEMG measures (Koldenhoven et al., 2019). Individuals had to be free of lower extremity injuries at least 12 months, and were excluded if they had a history of lower extremity fracture or surgery, or any neuromuscular dysfunction. This study was approved by the university’s Institutional Review Board and all participants provided written informed consent. Procedures Walking trials were performed on a dual-belt treadmill with imbedded force plates (Bertec Corporation, Columbus, OH, USA) using a 1000 Hz sampling rate and a threshold of 20N to identify initial contact and toe off. Participants’ movement was tracked using a 12camera Vicon Motion Capture System (Vicon Motion Systems, Inc., Lake Forest, CA, USA) sampled at 250 Hz, and kinematic, kinetic, and sEMG data were recorded using MotionMonitorTM software (Innovative Sports Training, Chicago, IL, USA). Wireless rectangular 27x37x13 mm Ag/AgCl Trigno sEMG electrodes (Delsys, Boston, MA, USA: 80 dB common mode rejection rate) with an 11-mV signal input range were used to collect GMED activation data at a 2000 Hz sampling rate (Hermens et al., 2000). B-mode GMAX and GMED ultrasound images and video clips were recorded using a portable Siemens ACUSON Freestyle Ultrasound System (Siemens Medical Inc., Mountain View, CA, USA) and a wireless 8 MHz linear transducer. The transducer was held in place using a custom Velcro belt with foam block that has been previously described and depicted elsewhere (DeJong et al., 2018, 2019; A. Dieterich et al., 2015). Walking trials were obtained during a single laboratory session. Following completion of informed consent, participants were prepared for digitization in the MotionMonitorTM system. Ten retroreflective rigid cluster marker sets were placed bilaterally on participants’ heels, forefoot, lateral shanks, lateral thighs, and on the sacrum and thoracic spine. Segments were digitized to

identify the joint centers for the C7/T1, T12/L1, L5/S1, anterior superior iliac spine, and medial and lateral knee joint lines, and medial and lateral malleoli for each limb. Participants’ skin was shaved, debrided and cleaned with isopropyl alcohol midway between the greater trochanter and iliac crest, and an sEMG electrode was adhered unilaterally over the GMED muscle belly (Hermens et al., 2000). The USI transducer was then secured using the custom foam block and belt set-up unilaterally just next to the GMED sEMG electrode (DeJong et al., 2018, 2019; A. V. Dieterich et al., 2015). In this manner, the sound head was placed obliquely between the greater trochanter and posterior superior iliac spine such that the GMAX and GMED were in view on the USI monitor without exiting the screen during walking. A 10-second quiet standing recording was obtained for kinetics, kinematics and sEMG, and three quiet standing USI images were obtained by a researcher with three years of USI experience (AFD). Following a 10-second quiet standing recording for normalization purposes, participants walked for a 5-minute warm-up on the treadmill at a preferred speed. The treadmill was then adjusted to a standardized 1.34 m/s walking speed for testing. MotionMonitorTM and USI video clips were recorded simultaneously through communication between the two investigators controlling each system to ensure that data were synchronous. Each USI recording yielded a 10-second gait trial with approximately nine complete gait cycles. Following this recording, collection procedures were complete and participants were dismissed. Data Processing The USI data processing scheme has been extensively described in previous studies (DeJong et al., 2019, 2018). In brief, ground reaction force data was referenced on a separate computer using a 0- to 20-Newton threshold to demarcate initial contact timing for USI video clips. The USI video and MotionMonitorTM recordings were played synchronously during the middle five strides until the ground reaction force fell below the 20-Newton threshold, thus enabling analysis of the middle five strides of gluteal muscle data. Each USI video clip was then

reduced to 11 still image frames (10% interludes from 0-100% of the gait cycle) for each gait cycle. USI data reduction during dynamic activities is essential to obtaining still images for measurement. This reduction technique yielded 55 total measurement frames per video clip. Measurement frames were captured as still images using the Macintosh screenshot function (macOS High Sierra, Version 10.13.6, Apple Inc.© 2018). Muscle thickness measurements were performed using ImageJ software (ImageJ 1.50i, National Institutes of Health, USA). The GMAX and GMED were measured from the inferior to superior fascial borders in centimeters. The averages of the five muscle thickness measures were obtained at each 10% increments for all participants. Muscle thickness during walking was subsequently normalized to quiet stance to determine the extent of muscle activity during gait: Equation 1:21 𝐹𝑢𝑛𝑐𝑡𝑖𝑜𝑛𝑎𝑙 𝐴𝑐𝑡𝑖𝑣𝑖𝑡𝑦 𝑅𝑎𝑡𝑖𝑜 =

𝑚𝑢𝑠𝑐𝑙𝑒 𝑡ℎ𝑖𝑐𝑘𝑛𝑒𝑠𝑠𝑑𝑢𝑟𝑖𝑛𝑔 𝑎𝑐𝑡𝑖𝑣𝑖𝑡𝑦 𝑚𝑢𝑠𝑐𝑙𝑒 𝑡ℎ𝑖𝑐𝑘𝑛𝑒𝑠𝑠𝑞𝑢𝑖𝑒𝑡

USI muscle thickness measures were resampled using interpolation between the 10% interludes to generate 101 data points to prepare for cross-correlation analyses between USI and the kinematic, kinetic, and sEMG data. Based on pilot data measuring every data-point of the USI images for a subset of participants, interpolation proved to be an appropriate estimation of data points between intervals. Kinematic, kinetic, and sEMG data were analyzed from ten consecutive strides during the walking trial. Sagittal, frontal, and transverse plane hip joint angles and external joint moments were obtained from the MotionMonitorTM software and normalized to quiet standing measures. Tri-planar hip kinematics were defined as hip flexion, adduction, and internal rotation for positive values, and as extension, abduction, and external rotation for negative values, with zero values indicating a neutral resting position. The raw sEMG data were filtered using a 10500Hz bandpass filter, a 60 Hz notch filter and 50-sample window, moving average, root mean square algorithm. sEMG data were normalized to quiet standing signals. The data from each stride were reduced to 101 data points to represent 0-100% of the gait cycle. sEMG RMS

amplitudes were normalized to the mean of a 10-second data epoch during quiet standing for each variable. Data processing was performed using custom code in Matlab version R2018a (MathWorks, Inc., Natick, MA, USA). Statistical Analysis Individual cross-correlation analyses were used to assess relationships between USIderived muscle thickness measures and each of the traditional biomechanical outcomes. Cross-correlations are used to compare discrete time points across data streams to determine where and for how much of a time lag there is the strongest relationship between the measures. Cross-correlation coefficients (CCF) were interpreted as <0.39 as weak, 0.40-0.59 as moderate, and 0.60-1.0 as strong relationships. The maximum lag range was set to +/-20% (220-ms) such that data signals were compared for an epoch from 20% prior to 20% after the comparison point of the primary signal (i.e. muscle thickness for main analyses and sEMG RMS amplitude for additional analyses). Maximum correlation lag percentages were multiplied by 11 as determined by Equation 2: Equation 2: 𝑀𝑖𝑙𝑙𝑖𝑠𝑒𝑐𝑜𝑛𝑑𝑠 𝑝𝑒𝑟 1% 𝑜𝑓 𝑡ℎ𝑒 𝑔𝑎𝑖𝑡 𝑐𝑦𝑐𝑙𝑒 =

𝑐𝑙𝑖𝑝 1000𝑚𝑠 (10𝑠9 𝑈𝑆𝐼 𝑠𝑡𝑒𝑝𝑠 ∗ 1𝑠 )

101 ― 𝑝𝑜𝑖𝑛𝑡𝑠 𝑜𝑓 𝑡ℎ𝑒 𝑔𝑎𝑖𝑡 𝑐𝑦𝑐𝑙𝑒

Mean GMAX thickness was compared to mean sagittal plane kinematics and kinetics. Similarly, group mean GMED thickness was compared to mean frontal and transverse plane hip kinematics and kinetics, and mean GMED sEMG in the same increments. Individual crosscorrelations were used for additional analyses to compare GMED sEMG to hip frontal and transverse plane kinematics and kinetics. All analyses were performed using SPSS (IBM SPSS Statistics, v25), and data visualization was performed in Tableau (Tableau Software, Inc., 2019). Results will be presented as time lags both in milliseconds and percentage of the gait cycle, and strength of outcomes will be presented using the cross-correlation coefficients for all measures. Descriptive outcomes have been extensively reported

elsewhere (DeJong et al., 2019, 2020; Koldenhoven et al., 2019); however, a brief overview for all measures including coefficient of variation measures can be found in the Supplementary Table. Results GMED Thickness - sEMG GMED thickness followed the GMED sEMG RMS amplitude by 9% of the gait cycle, or 99-ms (Figure 1a). As the CCF was positive (CCF=0.39), these results indicate that increases in GMED thickness followed increased GMED sEMG RMS amplitude. GMED Thickness - Kinetics For both the frontal and transverse plane kinetics, the highest correlations to GMED thickness measures were at a zero time-lag, indicating that a change in GMED activity directly corresponded to hip joint moments (Figures 1b-c). Both of these relationships were in the negative direction (frontal plane CCF= -0.47; transverse plane CCF= -0.28), indicating that increases in GMED activity corresponded to hip abduction and external rotation moments. GMED Thickness - Kinematics In the frontal plane, GMED thickness preceded changes in hip motion by 20% in the gait cycle, or 220-ms (Figure 1d). As the relationship was in the negative direction (CCF= -0.35), increased GMED thickness corresponded to increased hip abduction motion at this time lag. In the transverse plane, GMED thickness similarly preceded changes in hip rotational motion by 17%, or 187-ms, earlier in the gait cycle (Figure 1e). As the relationship was positive (CCF=0.24), increases in GMED thickness were related to increased hip internal rotation, though this relationship timing was the reverse of that in the frontal plane. GMED sEMG - Kinematics and Kinetics GMED sEMG amplitude measures were found to follow changes in hip frontal and transverse plane motion and moments within the gait cycle (Supplementary Figures 1a-d). sEMG onset occurred 5% points or 55-ms following hip abduction (CCF=-0.69), and hip external

rotation by 7% points or 77-ms (CCF=-0.67), indicative of eccentric control. sEMG onset was found to occur slightly before kinetic changes by 9% points or 99-ms for hip abduction (CCF=0.75), and 5% points or 55-ms for hip external rotation (CCF=-0.89). GMAX Thickness - Kinetics GMAX thickness was most correlated to hip kinetics 6% ahead in the gait cycle, or 66ms following kinetic changes (Figure 2a). As the largest CCF was positive, these outcomes indicate that increases in GMAX activity followed increased hip flexion moments (CCF=0.33). GMAX Thickness - Kinematics GMAX thickness was most highly correlated to hip kinematic outcomes at 14% earlier in the gait cycle, or 154-ms prior to the kinematic changes (CCF= -0.83). As the CCF was strongest in the negative direction, increased GMAX thickness most strongly corresponded to increased hip extension motion, indicative of a concentric contraction (Figure 2b). Discussion Though biomechanical measurements assess different facets of musculoskeletal properties during gait, the findings of this study confirm that hip muscle thickness, sEMG, kinematic and kinetic outcomes are related to one another across a time-lag spectrum (Figures 3a-b). These findings largely align with the hypotheses that underlying physiological properties drive the correlative properties of the signals. Muscle Thickness and sEMG: Electromechanical Delay We hypothesized that sEMG RMS amplitudes would occur prior to GMED muscle thickness changes given the principle of electromechanical delay, in which neural impulses occur first to elicit a muscle contraction (Frontera and Ochala, 2015; Go et al., 2018). The findings suggest that there is approximately a 99-ms delay in morphological USI responses. This value is slightly larger than that of previous published motor time lag of approximately 60ms, which may be attributed to the sampling rate of the USI unit (Viola and Walker, 2003). It is important to note that although this was the point of strongest

relationship between the measures, the cross-correlation was weak at CCF = 0.39, or approximately 19.5% of shared variance across measures. sEMG presents inherent limitations of measurement fidelity due to cross-talk from neighboring musculature which we postulate influenced the strength of the relationship (A. V. Dieterich et al., 2015). One previous investigation has looked at the association of intra-muscular EMG and USI motion-mode (or muscle onset determined through muscle thickness change) during basic isometric gluteal contractions that reported much stronger correlations (r=0.90) (A. V. Dieterich et al., 2015). However, intra-muscular EMG only measures electrical activity of a finite number of motor units and may not reasonably provide an overall estimate of a muscle’s electrical activity hindering generalizability of global skeletal muscle function. Our findings support that USI-derived muscle thickness measures provides valuable information on muscle characteristics that can expand beyond simple tabletop measures of muscular properties. Although muscle thickness measures present a different insight into muscle activity than sEMG RMS amplitude measures, it is important to consider that muscle thickness measures have less measurement error than sEMG, and have high inter- and intra-rater reliability (DeJong et al., 2018; A. V. Dieterich et al., 2015; Mangum et al., 2016). The key clinical conclusion from this analysis is that muscle thickness measures provide an estimate of increased muscle activity weakly associated with increased neural muscle impulses at a slight delay. This notion is solidified by the noted delay between sEMG RMS amplitude and kinematic and kinetic changes, likely due to the eccentric nature of the contractions that occur during walking gait (Cavanagh and Komi, 1979). Clinicians and researchers should be aware of these distinctions when utilizing USI measurements of muscle thickness. Muscle Thickness and Kinematics GMAX and GMED muscle thickness measures were found to precede joint motion changes for the muscles’ primary actions by approximately 15-20% of the gait cycle, or 154220-ms. Although these ranges are longer than most reported electromechanical delay

outcomes, previous evaluations have focused on isolated motions, whereas other muscles may be contributing to the joint movements during gait (Cavanagh and Komi, 1979; Smith et al., 2018, 2017). Further, past reports of delay have been isolated to EMG comparisons hindering a direct comparison to the current investigation (Cavanagh and Komi, 1979). Our outcomes support that morphological muscle changes occur prior to eliciting hip joint excursions in the sagittal plane, or when preparing to control against frontal and transverse plane motions by a greater lag than sEMG (Baldon et al., 2011; Cavanagh and Komi, 1979; Go et al., 2018). The strongest noted association was GMAX thickness with increased hip extension with a CCF of -0.83. The GMAX has the primary concentric role of producing hip extension during gait, particularly during propulsion (Neptune et al., 2004; Neumann, 2010). Conversely, the GMED controls abduction and internal rotation which may explain why each of the associations to hip kinematics were weaker than that of the GMAX (Neptune and McGowan, 2016; Neumann, 2010). This also helps to explain why the GMED association to transverse plane motion contradicted the anticipated outcomes since this is a secondary role of the GMED during gait (Neumann, 2010; Nguyen et al., 2017); instead, the GMED thickness changes observed are postulated to control internal rotation. USI-derived measures of GMED and GMAX thickness changes should be interpreted with kinematics in the context of the muscles’ respective eccentric and concentric actions, and clinicians and researchers should be aware that muscle thickness changes occur prior to motion. This information is important to consider for neuromuscular feedback. If clinicians have a gait-training goal to increase hip extension or abduction motion, muscle activity prompting to thicken the GMAX or GMED respectively should occur prior to expected timing of kinematic changes. For example, if patients are excessively adducting the hip during mid-stance, cueing GMED thickening should be targeted closer to initial contact to optimize the extent of muscle fiber overlap to facilitate eccentric control based on our results. Muscle Thickness and Kinetics

GMED muscle thickness changes directly corresponded to hip frontal and transverse plane moments, which was an expected outcome. Muscle thickness changes indicate that the muscle has already contracted, and thus corresponding muscle forces are acting on the hip joint simultaneously. This change would logically increase the hip abduction and external rotation moments with instantaneous changes in GMED thickness (Go et al., 2018; Neptune and McGowan, 2016). The relationship of GMED muscle thickness and hip frontal plane moments was considerably stronger than hip transverse moments (frontal CCF: -0.47 vs. transverse CCF: -0.28) which is likely explained by the primary GMED action (Neumann, 2010). It was somewhat surprising that there was a considerable lag between GMAX thickness and sagittal plane kinetics. It is important to consider other muscle groups contribute to hip frontal and transverse plane motions and may have influenced this association, resulting in the GMED and GMAX timing discrepancies. This shift may be further explained by the fact that hip extension occurs considerably later and in a larger excursion than frontal plane movements during gait. Referencing Figures 3a-b, the maximum hip extension moment occurred at ~50% of the gait cycle during the transition from stance to swing while the maximum abduction moment occurred ~35% during midstance. The potential shift for the sagittal plane appears to be largely driven by the transition from stance to swing and thus created a greater lag in GMAX thickness to joint motion. Joint moments relationships to muscle thickness appear to be driven by the type of motion about the joint. Future research should aim to determine how these relationships might be upheld with different types of joints to test this theory. Implications for Research and Clinical Practice This report serves as a basis for understanding how USI-derived measures of muscle thickness align with traditional biomechanical measurements to aid in the interpretation of gluteal muscle function during walking. USI provides a unique depiction of muscle function through muscle thickness measures which we found to be most highly correlated with an inciting neural activation event, and followed by joint motion changes. This sort of “cascade” of

outcomes across these signals serves to provide a broader scope of hip function during gait (Figures 3a-b). We propose that the current cross-correlation results may be used as a basis to understand relationships across measures specific to the hip. Future research should aim to compare the current findings to lower extremity injury populations to elucidate proximal neuromuscular control deficits, and help formulate gait-training programs. We believe that USI-derived measures of muscle thickness can be used to increase sEMG signals, and alter joint angles and moments due to the signal cross-correlations determined in the present study. USI may be more clinically useful than traditional laboratory techniques, and may be considered as a useful component of assessing muscle activity in patient evaluations. Limitations Only fourteen females were included in this study as a sample of convenience, and results may not be generalizable to a broader population, nor to males (Baldon et al., 2011). All walking trials were performed at a standard treadmill speed for reporting consistency across gait analysis studies. The extent of lag may alter with different speeds, however the reference values may be most useful for reproducibility and clinical interpretations across different populations. We only investigated outcomes specific to the hip during gait, and should not be extrapolated to other muscles groups and joints. Finally, we did not collect GMAX sEMG as these outcomes were a part of a larger investigation that did not incorporate these measures. Future studies should aim to determine the time-lags for GMAX sEMG outcomes. Conclusions GMED sEMG RMS amplitude signals were determined to precede USI-derived GMED muscle thickness changes, and in turn thickness changes were found to occur prior to joint excursions for the muscles’ primary actions during walking. GMED thickness changes occurred in tandem with frontal and transverse hip joint moments, while GMAX thickness changes occurred prior to hip extension moments. These results should be considered as references for

interpreting muscle thickness changes, and inform neuromuscular deficits in pathological patient populations during gait analyses.

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Figure 1. Gluteus medius ultrasound imaging thickness measure cross-correlations with hip (A) electromyography, (B) frontal plane kinetics, (C), transverse plane kinetics, (D) frontal plane kinematics, and (E) transverse plane kinematics. GMED EMG VS. GMED USI 1.0

Cross Correlation -1.000

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Abbreviations: GMED, gluteus medius; EMG, electromyography; USI, ultrasound imaging; CCF, cross-correlation coefficient

Figure 2. Gluteus maximus ultrasound imaging thickness measure cross-correlations with hip (A) sagittal plane kinetics and (B) sagittal plane kinematics. HIP SAGITTAL PLANE KINETICS VS. GMAX USI 1.0 -1.000 0.8

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Abbreviations: GMAX, gluteus maximus; USI, ultrasound imaging; CCF, cross-correlation coefficient

Figure 3. Cross-correlation outcome summaries for (A) gluteus medius ultrasound-derived thickness measure comparisons, and (B) gluteus maximus ultrasound-derived thickness measure comparisons.

Hip Frontal Moment

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Abbreviations: GMED, gluteus medius; USI, ultrasound imaging; EMG, electromyography; GMAX, gluteus maximus

Alexandra DeJong received her Bachelor’s degree in Athletic Training from the University of Pittsburgh in 2016, and completed her Master’s in Education in Kinesiology with a focus in Athletic Training from the University of Virginia in 2017. Alexandra is currently pursuing her doctorate in the Department of Kinesiology in Sports Medicine with the goal of continuing in academia. Her current research interests include quantifying gait mechanics using wearable technology, and looking at the effects of gait-training interventions on individuals with running-related injuries. Additionally, she has utilized musculoskeletal ultrasound imaging to investigate proximal muscle adaptations in lower extremity injury populations. Alexandra is a board certified and licensed Athletic Trainer in the state of Virginia, and is currently a member of the National Athletic Trainers’ Association. She currently serves as a research assistant in the Exercise and Sports Injury Laboratory, and is a teaching assistant in the Department of Kinesiology for undergraduate courses.

Conflicts of Interest: None Disclosures: This work was performed in partial fulfillment of the requirement for the doctor of philosophy degree and is archived in the Online Archive of University of Virginia Scholarship (DOI: 10.18130/v3-zv0r-mj47).