95 research outputs found
Mobile Brain and Body Imaging during Walking Motor Tasks
Mobile brain and body imaging (MoBI) presents new and promising methods for moving traditional research studies out of a controlled laboratory and into the real world. Most current neuroimaging techniques require subjects to be stationary in laboratory settings because of both hardware and software limitations. Recent developments in mobile brain imaging have utilized Electroencephalography (EEG) in conjunction with advanced signal processing techniques such as Independent Component Analysis (ICA) to overcome these obstacles and study humans doing complex tasks in non-traditional environments. In my first study, I used high density EEG to examine the cortical dynamics of subjects walking on a split-belt treadmill with legs moving independently of each other at different speeds to investigate how humans adapt to novel perturbations. I found significantly increased low and high frequency spectral power across all sensorimotor and parietal neural sources during split-belt adaptation compared to normal walking, which provides insight into the brain areas and patterns used to accommodate locomotor adaptation. In my second study I combined multi-modal sensing and biometric devices including EEG, eye tracking, heart rate, accelerometers, and salivary cortisol into a portable setup that subjects wore indoors on a treadmill using virtual reality as well as outdoors in a public arboretum. Subjects walked for 1 hour each indoors and outdoors while completing a free viewing visual search oddball task in virtual reality and in real life. I reported on the methods for how to set this experiment up, synchronize all data, and standardize the data in order to make it usable as an open access dataset that has been made available to the public online. My third study used this data set to examine the P300 event-related potential response during both indoors in virtual reality and outdoors in the arboretum. I found a significantly increased amplitude response between 250 to 400 ms across the centro-parietal electrodes that distinguished target flags from distractor flags during visual search for both indoor and outdoor environments. And finally, for my fourth study I used the same data set to look at the behavioral and neural correlates associated with gait dynamics when subjects walked indoors on a treadmill vs outdoors in variable terrain while also doing the visual search task. I found significant EEG power differences across multiple neural sources that showed increased spectral fluctuations throughout the gait cycle when subjects walked outdoors compared to indoors on a treadmill.
The collective studies in this dissertation present new ways of using mobile brain and body imaging devices to expand our knowledge of the neural dynamics involved in humans moving in complex ways and in variable environments outside of traditional laboratories.PHDBiomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147691/1/ghanada_1.pd
Decoding Working-Memory Load During n-Back Task Performance from High Channel NIRS Data
Near-infrared spectroscopy (NIRS) can measure neural activity through blood
oxygenation changes in the brain in a wearable form factor, enabling unique
applications for research in and outside the lab. NIRS has proven capable of
measuring cognitive states such as mental workload, often using machine
learning (ML) based brain-computer interfaces (BCIs). To date, NIRS research
has largely relied on probes with under ten to several hundred channels,
although recently a new class of wearable NIRS devices with thousands of
channels has emerged. This poses unique challenges for ML classification, as
NIRS is typically limited by few training trials which results in severely
under-determined estimation problems. So far, it is not well understood how
such high-resolution data is best leveraged in practical BCIs and whether
state-of-the-art (SotA) or better performance can be achieved. To address these
questions, we propose an ML strategy to classify working-memory load that
relies on spatio-temporal regularization and transfer learning from other
subjects in a combination that has not been used in previous NIRS BCIs. The
approach can be interpreted as an end-to-end generalized linear model and
allows for a high degree of interpretability using channel-level or cortical
imaging approaches. We show that using the proposed methodology, it is possible
to achieve SotA decoding performance with high-resolution NIRS data. We also
replicated several SotA approaches on our dataset of 43 participants wearing a
3198 dual-channel NIRS device while performing the n-Back task and show that
these existing methods struggle in the high-channel regime and are largely
outperformed by the proposed method. Our approach helps establish high-channel
NIRS devices as a viable platform for SotA BCI and opens new applications using
this class of headset while also enabling high-resolution model imaging and
interpretation.Comment: 29 pages, 9 figures. Under revie
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Decoding Depth of Meditation: Electroencephalography Insights From Expert Vipassana Practitioners.
BACKGROUND: Meditation practices have demonstrated numerous psychological and physiological benefits, but capturing the neural correlates of varying meditative depths remains challenging. In this study, we aimed to decode self-reported time-varying meditative depth in expert practitioners using electroencephalography (EEG). METHODS: Expert Vipassana meditators (n = 34) participated in 2 separate sessions. Participants reported their meditative depth on a personally defined 1 to 5 scale using both traditional probing and a novel spontaneous emergence method. EEG activity and effective connectivity in theta, alpha, and gamma bands were used to predict meditative depth using machine/deep learning, including a novel method that fused source activity and connectivity information. RESULTS: We achieved significant accuracy in decoding self-reported meditative depth across unseen sessions. The spontaneous emergence method yielded improved decoding performance compared with traditional probing and correlated more strongly with postsession outcome measures. Best performance was achieved by a novel machine learning method that fused spatial, spectral, and connectivity information. Conventional EEG channel-level methods and preselected default mode network regions fell short in capturing the complex neural dynamics associated with varying meditation depths. CONCLUSIONS: This study demonstrates the feasibility of decoding personally defined meditative depth using EEG. The findings highlight the complex, multivariate nature of neural activity during meditation and introduce spontaneous emergence as an ecologically valid and less obtrusive experiential sampling method. These results have implications for advancing neurofeedback techniques and enhancing our understanding of meditative practices
Dynamic changes in cell-surface expression of mannose in the oral epithelium during the development of graft-versus-host disease of the oral mucosa in rats
Fibrin-glue assisted multilayered amniotic membrane transplantation in surgical management of pediatric corneal limbal dermoid: a novel approach
Clustered Gene Expression Changes Flank Targeted Gene Loci in Knockout Mice
Gene expression profiling using microarrays is a powerful technology widely used to study regulatory networks. Profiling of mRNA levels in mutant organisms has the potential to identify genes regulated by the mutated protein.Using tissues from multiple lines of knockout mice we have examined genome-wide changes in gene expression. We report that a significant proportion of changed genes were found near the targeted gene.The apparent clustering of these genes was explained by the presence of flanking DNA from the parental ES cell. We provide recommendations for the analysis and reporting of microarray data from knockout mice
A Neuromedin U Receptor Acts with the Sensory System to Modulate Food Type-Dependent Effects on C. elegans Lifespan
Different food types modulate worm lifespan and involve the neuropeptide receptor NMUR-1, which acts with the sensory neurons in a bacterial lipopolysaccaharide structure-dependent manner
Cross-modal cue effects in psychophysics, fMRI, and MEG in motion perception
Thesis (M.Sc.Eng.) PLEASE NOTE: Boston University Libraries did not receive an Authorization To Manage form for this thesis or dissertation. It is therefore not openly accessible, though it may be available by request. If you are the author or principal advisor of this work and would like to request open access for it, please contact us at [email protected]. Thank you.Motion perception is critical to navigation within the environment and has been studied primarily in the unisensory visual domain. However, the real world is not unisensory, but contains motion information from several modalities. With the billions of sensory stimuli our brains receive every second, many complex processes must be executed in order to properly filter relevant motion related information. In transparent motion, when there are more than one velocity fields within the same visual space, our brains must be able to separate out conflicting forms of motion utilizing environmental cues. But even in unimodal visual situations, one often uses information from other modalities for guidance. We studied this phenomenon in psychophysics with cross-modal (visual and auditory) cues and their role in detecting transparent motion. To further examine these ideas, using a single subject we explored the spatiotemporal characteristics of the neural substrates involved in utilizing these different cues in motion detection during magnetoencephalography (MEG).
Another dimension of motion perception is involved when the observer is moving and, therefore, must deal with self-motion and changing environmental cues. To better understand this idea we used a visual search psychophysical task that has been well studied in our lab to determine whether subjects use a simple relative-motion computation to detect moving objects during self-motion or whether they utilize scene context when detecting object motion and how this might change when given a cross-modal auditory cue. To find the spatiotemporal neural characteristics involved in this process, functional magnetic resonance imaging (fMRI) and MEG were performed separately in elderly subjects (healthy and a stroke patient) and compared with previous studies of young healthy subjects doing the same task.2031-01-0
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