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

Abstract 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 β-band (12–30 Hz) at joint torque onset and offset. Isotonic contractions elicited a sustained α- and β-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.http://deepblue.lib.umich.edu/bitstream/2027.42/112617/1/12984_2011_Article_362.pd

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

Walking reduces sensorimotor network connectivity compared to standing

Abstract 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.http://deepblue.lib.umich.edu/bitstream/2027.42/134578/1/12984_2013_Article_546.pd

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

Development and validation of quantitative PCR assays for HIV-associated cryptococcal meningitis in sub-Saharan Africa: a diagnostic accuracy study

Background: HIV-associated cryptococcal meningitis is the second leading cause of AIDS-related deaths, with a 10-week mortality rate of 25–30%. Fungal load assessed by colony-forming unit (CFU) counts is used as a prognostic marker and to monitor response to treatment in research studies. PCR-based assessment of fungal load could be quicker and less labour-intensive. We sought to design, optimise, and validate quantitative PCR (qPCR) assays for the detection, identification, and quantification of Cryptococcus infections in patients with cryptococcal meningitis in sub-Saharan Africa. Methods: We developed and validated species-specific qPCR assays based on DNA amplification of QSP1 (QSP1A specific to Cryptococcus neoformans, QSP1B/C specific to Cryptococcus deneoformans, and QSP1D specific to Cryptococcus gattii species) and a pan-Cryptococcus assay based on a multicopy 28S rRNA gene. This was a longitudinal study that validated the designed assays on cerebrospinal fluid (CSF) of 209 patients with cryptococcal meningitis at baseline (day 0) and during anti-fungal therapy (day 7 and day 14), from the AMBITION-cm trial in Botswana and Malawi (2018–21). Eligible patients were aged 18 years or older and presenting with a first case of cryptococcal meningitis. Findings: When compared with quantitative cryptococcal culture as the reference, the sensitivity of the 28S rRNA was 98·2% (95% CI 95·1–99·5) and of the QSP1 assay was 90·4% (85·2–94·0) in CSF at day 0. Quantification of the fungal load with QSP1 and 28S rRNA qPCR correlated with quantitative cryptococcal culture (R2=0·73 and R2=0·78, respectively). Both Botswana and Malawi had a predominant C neoformans prevalence of 67% (95% CI 55–75) and 68% (57–73), respectively, and lower C gattii rates of 21% (14–31) and 8% (4–14), respectively. We identified ten patients that, after 14 days of treatment, harboured viable but non-culturable yeasts based on QSP1 RNA detection (without any positive CFU in CSF culture). Interpretation: QSP1 and 28S rRNA assays are useful in identifying Cryptococcus species. qPCR results correlate well with baseline quantitative cryptococcal culture and show a similar decline in fungal load during induction therapy. These assays could be a faster alternative to quantitative cryptococcal culture to determine fungal load clearance. The clinical implications of the possible detection of viable but non-culturable cells in CSF during induction therapy remain unclear. Funding: European and Developing Countries Clinical Trials Partnership; Swedish International Development Cooperation Agency; Wellcome Trust/UK Medical Research Council/UKAID Joint Global Health Trials; and UK National Institute for Health Research

Noninvasive Electrical Neuroimaging of the Human Brain during Mobile Tasks including Walking and Running.

Noninvasive brain imaging during mobile activities could have far reaching scientific, clinical, and technological benefits. Electroencephalography (EEG) is the only mobile noninvasive sensing modality with sufficient temporal resolution to record brain activity on the time scale of natural motor behavior. In the past, EEG has been limited to stationary settings to prevent contamination by electromyographic and movement artifacts. I overcame this limitation by using Independent Component Analysis (ICA) to parse electrocortical processes from artifact contaminated EEG. Chapters 2 through 4 of this dissertation demonstrate the feasibility of measuring electrocortical activity during human locomotion. In Chapter 2, subjects performed a visual target discrimination and response task while standing, walking, and running. Cognitive event-related cortical potentials during walking and running were nearly identical to those during standing. Chapter 3 provided the first intra-stride measurements of brain activity during walking. Electrocortical sources in the anterior cingulate, posterior parietal, and sensorimotor cortex exhibited significant intra-stride changes in spectral power. A substantive scientific contribution of this study is the observation that synchronous neural firing in the anterior cingulate and posterior parietal cortex, not just the sensorimotor cortex, is modulated within the stride cycle during repetitive, steady-state locomotion. A 264-channel electrode array was used in Chapters 2 and 3. By systematically reducing the number of channels used, Chapter 4 demonstrated that 35 channels were sufficient to record the most dominate electrocortical sources. In Chapters 5 and 6, I studied healthy subjects performing isometric and isotonic lower-limb muscle contractions while seated to better understand the relationship between electrocortical dynamics and lower limb muscle activity. Isometric contractions elicited motor cortex event related desynchronization at joint torque onset and offset, while isotonic contractions elicited sustained cortical desynchronization throughout the movement. There was significant coherence between contralateral motor cortex signals and lower-limb electromyographic signals. The frequency of this coherence shifted from the beta-range for isometric contractions to the gamma-range for isotonic contractions. This dissertation demonstrated that EEG-based brain imaging in dynamic environments is possible and expanded our understanding of cortical involvement in voluntary lower limb movement. It also provided direction for future developments of clinical neuro-monitoring, neuro-assessment, and neuro-rehabilitation technologies.PHDIndependent Interdepartmental Degree ProgramUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/93901/1/jgwin_1.pd

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

Abstract 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.http://deepblue.lib.umich.edu/bitstream/2027.42/112517/1/12984_2011_Article_388.pd

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