Image Segmentation and Machine Learning for Detection of Abdominal Free Fluid in Focused Assessment With Sonography for Trauma Examinations: A Pilot Study.

The objective of this pilot study was to test the feasibility of automating the detection of abdominal free fluid in focused assessment with sonography for trauma (FAST) examinations. Perihepatic views from 10 FAST examinations with positive results and 10 FAST examinations with negative results were used. The sensitivity and specificity compared to manual classification by trained physicians was evaluated. The sensitivity and specificity (95% confidence interval) were 100% (69.2%-100%) and 90.0% (55.5%-99.8%), respectively. These findings suggest that computerized detection of free fluid on abdominal ultrasound images may be sensitive and specific enough to aid clinicians in their interpretation of a FAST examination.

Walking reduces sensorimotor network connectivity compared to standing.

BACKGROUND: Considerable effort has been devoted to mapping the functional and effective connectivity of the human brain, but these efforts have largely been limited to tasks involving stationary subjects. Recent advances with high-density electroencephalography (EEG) and Independent Components Analysis (ICA) have enabled study of electrocortical activity during human locomotion. The goal of this work was to measure the effective connectivity of cortical activity during human standing and walking. METHODS: We recorded 248-channels of EEG as eight young healthy subjects stood and walked on a treadmill both while performing a visual oddball discrimination task and not performing the task. ICA parsed underlying electrocortical, electromyographic, and artifact sources from the EEG signals. Inverse source modeling methods and clustering algorithms localized posterior, anterior, prefrontal, left sensorimotor, and right sensorimotor clusters of electrocortical sources across subjects. We applied a directional measure of connectivity, conditional Granger causality, to determine the effective connectivity between electrocortical sources. RESULTS: Connections involving sensorimotor clusters were weaker for walking than standing regardless of whether the subject was performing the simultaneous cognitive task or not. This finding supports the idea that cortical involvement during standing is greater than during walking, possibly because spinal neural networks play a greater role in locomotor control than standing control. Conversely, effective connectivity involving non-sensorimotor areas was stronger for walking than standing when subjects were engaged in the simultaneous cognitive task. CONCLUSIONS: Our results suggest that standing results in greater functional connectivity between sensorimotor cortical areas than walking does. Greater cognitive attention to standing posture than to walking control could be one interpretation of that finding. These techniques could be applied to clinical populations during gait to better investigate neural substrates involved in mobility disorders.

Loss of balance during balance beam walking elicits a multifocal theta band electrocortical response.

Determining the neural correlates of loss of balance during walking could lead to improved clinical assessment and treatment for individuals predisposed to falls. We used high-density electroencephalography (EEG) combined with independent component analysis (ICA) to study loss of balance during human walking. We examined 26 healthy young subjects performing heel-to-toe walking on a treadmill-mounted balance beam as well as walking on the treadmill belt (both at 0.22 m/s). ICA identified clusters of electrocortical EEG sources located in or near anterior cingulate, anterior parietal, superior dorsolateral-prefrontal, and medial sensorimotor cortex that exhibited significantly larger mean spectral power in the theta band (4-7 Hz) during walking on the balance beam compared with treadmill walking. Left and right sensorimotor cortex clusters produced significantly less power in the beta band (12-30 Hz) during walking on the balance beam compared with treadmill walking. For each source cluster, we also computed a normalized mean time/frequency spectrogram time locked to the gait cycle during loss of balance (i.e., when subjects stepped off the balance beam). All clusters except the medial sensorimotor cluster exhibited a transient increase in theta band power during loss of balance. Cluster spectrograms demonstrated that the first electrocortical indication of impending loss of balance occurred in the left sensorimotor cortex at the transition from single support to double support prior to stepping off the beam. These findings provide new insight into the neural correlates of walking balance control and could aid future studies on elderly individuals and others with balance impairments.

An EEG-based study of discrete isometric and isotonic human lower limb muscle contractions.

BACKGROUND: Electroencephalography (EEG) combined with independent component analysis enables functional neuroimaging in dynamic environments including during human locomotion. This type of functional neuroimaging could be a powerful tool for neurological rehabilitation. It could enable clinicians to monitor changes in motor control related cortical dynamics associated with a therapeutic intervention, and it could facilitate noninvasive electrocortical control of devices for assisting limb movement to stimulate activity dependent plasticity. Understanding the relationship between electrocortical dynamics and muscle activity will be helpful for incorporating EEG-based functional neuroimaging into clinical practice. The goal of this study was to use independent component analysis of high-density EEG to test whether we could relate electrocortical dynamics to lower limb muscle activation in a constrained motor task. A secondary goal was to assess the trial-by-trial consistency of the electrocortical dynamics by decoding the type of muscle action. METHODS: We recorded 264-channel EEG while 8 neurologically intact subjects performed isometric and isotonic, knee and ankle exercises at two different effort levels. Adaptive mixture independent component analysis (AMICA) parsed EEG into models of underlying source signals. We generated spectrograms for all electrocortical source signals and used a naïve Bayesian classifier to decode exercise type from trial-by-trial time-frequency data. RESULTS: AMICA captured different electrocortical source distributions for ankle and knee tasks. The fit of single-trial EEG to these models distinguished knee from ankle tasks with 80% accuracy. Electrocortical spectral modulations in the supplementary motor area were significantly different for isometric and isotonic tasks (p < 0.05). Isometric contractions elicited an event related desynchronization (ERD) in the α-band (8-12 Hz) and ß-band (12-30 Hz) at joint torque onset and offset. Isotonic contractions elicited a sustained α- and ß-band ERD throughout the trial. Classifiers based on supplementary motor area sources achieved a 4-way classification accuracy of 69% while classifiers based on electrocortical sources in multiple brain regions achieved a 4-way classification accuracy of 87%. CONCLUSIONS: Independent component analysis of EEG reveals unique spatial and spectro-temporal electrocortical properties for different lower limb motor tasks. Using a broad distribution of electrocortical signals may improve classification of human lower limb movements from single-trial EEG.

Weighted phase lag index stability as an artifact resistant measure to detect cognitive EEG activity during locomotion.

BACKGROUND: High-density electroencephalography (EEG) with active electrodes allows for monitoring of electrocortical dynamics during human walking but movement artifacts have the potential to dominate the signal. One potential method for recovering cognitive brain dynamics in the presence of gait-related artifact is the Weighted Phase Lag Index. METHODS: We tested the ability of Weighted Phase Lag Index to recover event-related potentials during locomotion. Weighted Phase Lag Index is a functional connectivity measure that quantified how consistently 90° (or 270°) phase 'lagging' one EEG signal was compared to another. 248-channel EEG was recorded as eight subjects performed a visual oddball discrimination and response task during standing and walking (0.8 or 1.2 m/s) on a treadmill. RESULTS: Applying Weighted Phase Lag Index across channels we were able to recover a p300-like cognitive response during walking. This response was similar to the classic amplitude-based p300 we also recovered during standing. We also showed that the Weighted Phase Lag Index detects more complex and variable activity patterns than traditional voltage-amplitude measures. This variability makes it challenging to compare brain activity over time and across subjects. In contrast, a statistical metric of the index's variability, calculated over a moving time window, provided a more generalized measure of behavior. Weighted Phase Lag Index Stability returned a peak change of 1.8% + -0.5% from baseline for the walking case and 3.9% + -1.3% for the standing case. CONCLUSIONS: These findings suggest that both Weighted Phase Lag Index and Weighted Phase Lag Index Stability have potential for the on-line analysis of cognitive dynamics within EEG during human movement. The latter may be more useful from extracting general principles of neural behavior across subjects and conditions.

Electrocortical activity is coupled to gait cycle phase during treadmill walking.

Recent findings suggest that human cortex is more active during steady-speed unperturbed locomotion than previously thought. However, techniques that have been used to image the brain during locomotion lack the temporal resolution necessary to assess intra-stride cortical dynamics. Our aim was to determine if electrocortical activity is coupled to gait cycle phase during steady-speed human walking. We used electroencephalography (EEG), motion capture, and a force-measuring treadmill to record brain and body dynamics while eight healthy young adult subjects walked on a treadmill. Infomax independent component analysis (ICA) parsed EEG signals into maximally independent component (IC) processes representing electrocortical sources, muscle sources, and artifacts. We calculated a spatially fixed equivalent current dipole for each IC using an inverse modeling approach, and clustered electrocortical sources across subjects by similarities in dipole locations and power spectra. We then computed spectrograms for each electrocortical source that were time-locked to the gait cycle. Electrocortical sources in the anterior cingulate, posterior parietal, and sensorimotor cortex exhibited significant (p<0.05) intra-stride changes in spectral power. During the end of stance, as the leading foot was contacting the ground and the trailing foot was pushing off, alpha- and beta-band spectral power increased in or near the left/right sensorimotor and dorsal anterior cingulate cortex. Power increases in the left/right sensorimotor cortex were more pronounced for contralateral limb push-off (ipsilateral heel-strike) than for ipsilateral limb push-off (contralateral heel-strike). Intra-stride high-gamma spectral power changes were evident in anterior cingulate, posterior parietal, and sensorimotor cortex. These data confirm cortical involvement in steady-speed human locomotion. Future applications of these techniques could provide critical insight into the neural mechanisms of movement disorders and gait rehabilitation.

Cognition in action: imaging brain/body dynamics in mobile humans.

We have recently developed a mobile brain imaging method (MoBI), that allows for simultaneous recording of brain and body dynamics of humans actively behaving in and interacting with their environment. A mobile imaging approach was needed to study cognitive processes that are inherently based on the use of human physical structure to obtain behavioral goals. This review gives examples of the tight coupling between human physical structure with cognitive processing and the role of supraspinal activity during control of human stance and locomotion. Existing brain imaging methods for actively behaving participants are described and new sensor technology allowing for mobile recordings of different behavioral states in humans is introduced. Finally, we review recent work demonstrating the feasibility of a MoBI system that was developed at the Swartz Center for Computational Neuroscience at the University of California, San Diego, demonstrating the range of behavior that can be investigated with this method.

Removal of movement artifact from high-density EEG recorded during walking and running.

Although human cognition often occurs during dynamic motor actions, most studies of human brain dynamics examine subjects in static seated or prone conditions. EEG signals have historically been considered to be too noise prone to allow recording of brain dynamics during human locomotion. Here we applied a channel-based artifact template regression procedure and a subsequent spatial filtering approach to remove gait-related movement artifact from EEG signals recorded during walking and running. We first used stride time warping to remove gait artifact from high-density EEG recorded during a visual oddball discrimination task performed while walking and running. Next, we applied infomax independent component analysis (ICA) to parse the channel-based noise reduced EEG signals into maximally independent components (ICs) and then performed component-based template regression. Applying channel-based or channel-based plus component-based artifact rejection significantly reduced EEG spectral power in the 1.5- to 8.5-Hz frequency range during walking and running. In walking conditions, gait-related artifact was insubstantial: event-related potentials (ERPs), which were nearly identical to visual oddball discrimination events while standing, were visible before and after applying noise reduction. In the running condition, gait-related artifact severely compromised the EEG signals: stable average ERP time-courses of IC processes were only detectable after artifact removal. These findings show that high-density EEG can be used to study brain dynamics during whole body movements and that mechanical artifact from rhythmic gait events may be minimized using a template regression procedure.

An investigation of the NOCSAE linear impactor test method based on in vivo measures of head impact acceleration in American football.

The performance characteristics of football helmets are currently evaluated by simulating head impacts in the laboratory using a linear drop test method. To encourage development of helmets designed to protect against concussion, the National Operating Committee for Standards in Athletic Equipment recently proposed a new headgear testing methodology with the goal of more closely simulating in vivo head impacts. This proposed test methodology involves an impactor striking a helmeted headform, which is attached to a nonrigid neck. The purpose of the present study was to compare headform accelerations recorded according to the current (n=30) and proposed (n=54) laboratory test methodologies to head accelerations recorded in the field during play. In-helmet systems of six single-axis accelerometers were worn by the Dartmouth College men's football team during the 2005 and 2006 seasons (n=20,733 impacts; 40 players). The impulse response characteristics of a subset of laboratory test impacts (n=27) were compared with the impulse response characteristics of a matched sample of in vivo head accelerations (n=24). Second- and third-order underdamped, conventional, continuous-time process models were developed for each impact. These models were used to characterize the linear head/headform accelerations for each impact based on frequency domain parameters. Headform linear accelerations generated according to the proposed test method were less similar to in vivo head accelerations than headform accelerations generated by the current linear drop test method. The nonrigid neck currently utilized was not developed to simulate sport-related direct head impacts and appears to be a source of the discrepancy between frequency characteristics of in vivo and laboratory head/headform accelerations. In vivo impacts occurred 37% more frequently on helmet regions, which are tested in the proposed standard than on helmet regions tested currently. This increase was largely due to the addition of the facemask test location. For the proposed standard, impactor velocities as high as 10.5 m/s were needed to simulate the highest energy impacts recorded in vivo. The knowledge gained from this study may provide the basis for improving sports headgear test apparatuses with regard to mimicking in vivo linear head accelerations. Specifically, increasing the stiffness of the neck is recommended. In addition, this study may provide a basis for selecting appropriate test impact energies for the standard performance specification to accompany the proposed standard linear impactor test method.

Enabling Stroke Rehabilitation in Home and Community Settings: A Wearable Sensor-Based Approach for Upper-Limb Motor Training.

High-dosage motor practice can significantly contribute to achieving functional recovery after a stroke. Performing rehabilitation exercises at home and using, or attempting to use, the stroke-affected upper limb during Activities of Daily Living (ADL) are effective ways to achieve high-dosage motor practice in stroke survivors. This paper presents a novel technological approach that enables 1) detecting goal-directed upper limb movements during the performance of ADL, so that timely feedback can be provided to encourage the use of the affected limb, and 2) assessing the quality of motor performance during in-home rehabilitation exercises so that appropriate feedback can be generated to promote high-quality exercise. The results herein presented show that it is possible to detect 1) goal-directed movements during the performance of ADL with a [Formula: see text]-statistic of 87.0% and 2) poorly performed movements in selected rehabilitation exercises with an [Formula: see text]-score of 84.3%, thus enabling the generation of appropriate feedback. In a survey to gather preliminary data concerning the clinical adequacy of the proposed approach, 91.7% of occupational therapists demonstrated willingness to use it in their practice, and 88.2% of stroke survivors indicated that they would use it if recommended by their therapist.

Telehealth monitor to measure physical activity and pressure relief maneuver performance in wheelchair users.

This study demonstrated the feasibility of a device for monitoring pressure relief maneuvers and physical activity for wheelchair users. The device counts the number of wheel pushes based on wheelchair acceleration and measures pressure relief maneuvers using a seat sensor consisting of three force sensing resistors (FSRs). To establish the feasibility of the seat sensor for the detection of pressure relief maneuvers, 10 wheelchair users and 10 non-disabled controls completed a series of wheelchair depression raises, forward trunk leans, and lateral trunk leans. The seat sensor was placed underneath the user's seat cushion. To establish the feasibility of wheel push counting, 10 full-time wheelchair users navigated a flat 50-m outdoor track and a 100-m outdoor obstacle course during self-propulsion (e.g., wheel pushes) and during assisted-propulsion (e.g., no wheel pushes). Of the 240 performed pressure relief, 225 were properly classified by the seat sensor (accuracy: 94%, sensitivity: 96%, specificity: 80%). Sensitivity was highest for depression raises (98%) and lowest for front lean maneuvers (80%). The wheelchair activity monitor measured 2,112 pushes during the self-propulsion trials compared to 2,162 pushes measured with the instrumented push-rim (97.7%). During assisted-propulsion trials, there were 477 incorrectly identified pushes (8.0 per trial).

Wearable Sensors in Huntington Disease: A Pilot Study.

BACKGROUND: The Unified Huntington's Disease Rating Scale (UHDRS) is the principal means of assessing motor impairment in Huntington disease but is subjective and generally limited to in-clinic assessments. OBJECTIVE: To evaluate the feasibility and ability of wearable sensors to measure motor impairment in individuals with Huntington disease in the clinic and at home. METHODS: Participants with Huntington disease and controls were asked to wear five accelerometer-based sensors attached to the chest and each limb for standardized, in-clinic assessments and for one day at home. A second chest sensor was worn for six additional days at home. Gait measures were compared between controls, participants with Huntington disease, and participants with Huntington disease grouped by UHDRS total motor score using Cohen's d values. RESULTS: Fifteen individuals with Huntington disease and five controls completed the study. Sensor data were successfully captured from 18 of the 20 participants at home. In the clinic, the standard deviation of step time (time between consecutive steps) was increased in Huntington disease (p < 0.0001; Cohen's d = 2.61) compared to controls. At home with additional observations, significant differences were observed in seven additional gait measures. The gait of individuals with higher total motor scores (50 or more) differed significantly from those with lower total motor scores (below 50) on multiple measures at home. CONCLUSIONS: In this pilot study, the use of wearable sensors in clinic and at home was feasible and demonstrated gait differences between controls, participants with Huntington disease, and participants with Huntington disease grouped by motor impairment.

Beta- and gamma-range human lower limb corticomuscular coherence.

Coherence between electroencephalography (EEG) recorded on the scalp above the motor cortex and electromyography (EMG) recorded on the skin of the limbs is thought to reflect corticospinal coupling between motor cortex and muscle motor units. Beta-range (13-30 Hz) corticomuscular coherence has been extensively documented during static force output while gamma-range (31-45 Hz) coherence has been linked to dynamic force output. However, the explanation for this beta-to-gamma coherence shift remains unclear. We recorded 264-channel EEG and 8-channel lower limb EMG while eight healthy subjects performed isometric and isotonic, knee, and ankle exercises. Adaptive mixture independent component analysis (AMICA) parsed EEG into models of underlying source signals. We computed magnitude squared coherence between electrocortical source signals and EMG. Significant coherence between contralateral motor cortex electrocortical signals and lower limb EMG was observed in the beta- and gamma-range for all exercise types. Gamma-range coherence was significantly greater for isotonic exercises than for isometric exercises. We conclude that active muscle movement modulates the speed of corticospinal oscillations. Specifically, isotonic contractions shift corticospinal oscillations toward the gamma-range while isometric contractions favor beta-range oscillations. Prior research has suggested that tasks requiring increased integration of visual and somatosensory information may shift corticomuscular coherence to the gamma-range. The isometric and isotonic tasks studied here likely required similar amounts of visual and somatosensory integration. This suggests that muscle dynamics, including the amount and type of proprioception, may play a role in the beta-to-gamma shift.

High-density EEG and independent component analysis mixture models distinguish knee contractions from ankle contractions.

Decoding human motor tasks from single trial electroencephalography (EEG) signals can help scientists better understand cortical neurophysiology and may lead to brain computer interfaces (BCI) for motor augmentation. Spatial characteristics of EEG have been used to distinguish left from right hand motor imagery and motor action. We used independent component analysis (ICA) of EEG to distinguish right knee action from right ankle action. We recorded 264-channel EEG while 5 subjects performed a variety of knee and ankle exercises. An adaptive mixture independent component analysis (ICA) algorithm generated two distinct mixture models from a merged set of EEG signals (including both knee and ankle actions) without prior knowledge of the underlying exercise. The ICA mixture models parsed EEG signals into maximally independent component (IC) processes representing electrocortical sources, muscle sources, and artifacts. We calculated a spatially fixed equivalent current dipole for each IC using an inverse modeling approach. The fit of the models to the single trial EEG signals distinguished knee exercises from ankle exercise with 90% accuracy. For 3 of 5 subjects, accuracy was 100%. Electrocortical current dipole locations revealed significant differences in the knee and ankle mixture models that were consistent with the somatotopy of the tasks. These data demonstrate that EEG mixture models can distinguish motor tasks that have different somatotopic arrangements, even within the same brain hemisphere.

Visual evoked responses during standing and walking.

Human cognition has been shaped both by our body structure and by its complex interactions with its environment. Our cognition is thus inextricably linked to our own and others' motor behavior. To model brain activity associated with natural cognition, we propose recording the concurrent brain dynamics and body movements of human subjects performing normal actions. Here we tested the feasibility of such a mobile brain/body (MoBI) imaging approach by recording high-density electroencephalographic (EEG) activity and body movements of subjects standing or walking on a treadmill while performing a visual oddball response task. Independent component analysis of the EEG data revealed visual event-related potentials that during standing, slow walking, and fast walking did not differ across movement conditions, demonstrating the viability of recording brain activity accompanying cognitive processes during whole body movement. Non-invasive and relatively low-cost MoBI studies of normal, motivated actions might improve understanding of interactions between brain and body dynamics leading to more complete biological models of cognition.

Motor control and aging: links to age-related brain structural, functional, and biochemical effects.

Although connections between cognitive deficits and age-associated brain differences have been elucidated, relationships with motor performance are less well understood. Here, we broadly review age-related brain differences and motor deficits in older adults in addition to cognition-action theories. Age-related atrophy of the motor cortical regions and corpus callosum may precipitate or coincide with motor declines such as balance and gait deficits, coordination deficits, and movement slowing. Correspondingly, degeneration of neurotransmitter systems-primarily the dopaminergic system-may contribute to age-related gross and fine motor declines, as well as to higher cognitive deficits. In general, older adults exhibit involvement of more widespread brain regions for motor control than young adults, particularly the prefrontal cortex and basal ganglia networks. Unfortunately these same regions are the most vulnerable to age-related effects, resulting in an imbalance of "supply and demand". Existing exercise, pharmaceutical, and motor training interventions may ameliorate motor deficits in older adults.

Head impact severity measures for evaluating mild traumatic brain injury risk exposure.

OBJECTIVE: The aims of this study were to quantify the sensitivity of various biomechanical measures (linear acceleration, rotational acceleration, impact duration, and impact location) of head impact to the clinical diagnosis of concussion in United States football players and to develop a novel measure of head impact severity combining these measures into a single score that better predicts the incidence of concussion. METHODS: On-field head impact data were collected from 449 football players at 13 organizations (n = 289,916) using in-helmet systems of six single-axis accelerometers. Concussions were diagnosed by medical staff and later associated with impact data. Principal component analysis and a weighting coefficient based on impact location were used to transform correlated head impact measures into a new composite variable, weighted principal component score (wPCS). The predictive power of linear acceleration, rotational acceleration, head injury criterion, and wPCS was quantified using receiver operating characteristic curves. The null hypothesis, that a measure was no more predictive than guessing, was tested (alpha = 0.05). In addition, receiver operating characteristic curves for wPCS and classical measures were directly compared to test the hypothesis that wPCS was more predictive of concussion than were classic measures (alpha = 0.05). RESULTS: When all of the impacts were considered, every biomechanical measure evaluated was statistically more predictive of concussion than guessing (P < 0.005). However, for the top 1 and 2% of impacts based on linear acceleration, a subset that consisted of 82% of all diagnosed concussions, only wPCS was significantly more predictive of concussion than guessing (P < 0.03); when compared with each other, wPCS was more predictive of concussion than were classical measures for the top 1 and 2% of all of the data (P < 0.04). CONCLUSION: A weighted combination of several biomechanical inputs, including impact location, is more predictive of concussion than a single biomechanical measure. This study is the first to the authors' knowledge to quantify improvements in the sensitivity of a biomechanical measure to incidence of concussion when impact location is considered.

In vivo study of head impacts in football: a comparison of National Collegiate Athletic Association Division I versus high school impacts.

OBJECTIVE: To compare the frequency and magnitude of head impacts between National Collegiate Athletic Association Division I and American high school football players. The long-term goal is to correlate impact forces with injury patterns, leading to improvements in protective headgear. METHODS: The helmets of football players at the University of Oklahoma (n = 40) and Casady High School (n = 16) were instrumented with the Head Impact Telemetry System (Simbex, Lebanon, NH). Data were collected for practices and games for the 2005 football season and were analyzed by player position and school. Player positions were separated into two groups (skill and line) for analysis. Two case studies of athletes who sustained a concussion are also presented. RESULTS: A total of 54,154 impacts were recorded at the University of Oklahoma and 8326 at Casady High School. College players sustained high-level impacts greater than 98 g more frequently than high school players. The mean linear accelerations for the top 1, 2, and 5% of all impacts were also higher for college players (P < 0.02). Skill position players received 24.6% of all impacts and sustained an impact greater than 98 g once every 70 impacts. In contrast, linemen sustained the highest number of impacts, but most were relatively low-magnitude (20-30 g). Linemen sustained an impact greater than 98 g once every 125 impacts. CONCLUSION: Differences in the frequency and magnitude of head acceleration after impact exist between a Division I college team and a high school team. Compared with linemen, skill position players typically sustain the highest-level impacts. Additional data collection and analysis are required to correlate concussion diagnosis with acceleration magnitude and impact location.