A. James Clark School of Engineering

Permanent URI for this communityhttp://hdl.handle.net/1903/1654

The collections in this community comprise faculty research works, as well as graduate theses and dissertations.

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Subject: search.filters.subject.magnetoencephalography

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    Bilaterally Reduced Rolandic Beta Band Activity in Minor Stroke Patients - Dataset
    (2022) Kulasingham, Joshua; Brodbeck, Christian; Khan, Sheena; Simon, Jonathan; Marsh, Elisabeth
    Stroke patients with hemiparesis display decreased beta band (13–25Hz) rolandic activity, correlating to impaired motor function. However, clinically, patients without significant weakness, with small lesions far from sensorimotor cortex, exhibit bilateral decreased motor dexterity and slowed reaction times. We investigate whether these minor stroke patients also display abnormal beta band activity. Magnetoencephalographic (MEG) data were collected from nine minor stroke patients (NIHSS < 4) without significant hemiparesis, at ~1 and ~6 months postinfarct, and eight age-similar controls. Rolandic relative beta power during matching tasks and resting state, and Beta Event Related (De)Synchronization (ERD/ERS) during button press responses were analyzed. Regardless of lesion location, patients had significantly reduced relative beta power and ERS compared to controls. abnormalities persisted over visits, and were present in both ipsi- and contra-lesional hemispheres, consistent with bilateral impairments in motor dexterity and speed. Minor stroke patients without severe weakness display reduced rolandic beta band activity in both hemispheres, which may be linked to bilaterally impaired dexterity and processing speed, implicating global connectivity dysfunction affecting sensorimotor cortex independent of lesion location. Findings not only illustrate global network disruption after minor stroke, but suggest rolandic beta band activity may be a potential biomarker and treatment target, even for minor stroke patients with small lesions far from sensorimotor areas.
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    Cortical Processing of Arithmetic and Simple Sentences in an Auditory Attention Task - Dataset
    (2021) Kulasingham, Joshua P.; Joshi, Neha H.; Rezaeizadeh, Mohsen; Simon, Jonathan Z.
    Cortical processing of arithmetic and of language rely on both shared and task-specific neural mechanisms, which should also be dissociable from the particular sensory modality used to probe them. Here, spoken arithmetical and non-mathematical statements were employed to investigate neural processing of arithmetic, compared to general language processing, in an attention-modulated cocktail party paradigm. Magnetoencephalography (MEG) data were recorded from 22 human subjects listening to audio mixtures of spoken sentences and arithmetic equations while selectively attending to one of the two speech streams. Short sentences and simple equations were presented diotically at fixed and distinct word/symbol and sentence/equation rates. Critically, this allowed neural responses to acoustics, words, and symbols to be dissociated from responses to sentences and equations. Indeed, the simultaneous neural processing of the acoustics of words and symbols were observed in auditory cortex for both streams. Neural responses to sentences and equations, however, were predominantly to the attended stream, originating primarily from left temporal, and parietal areas, respectively. Additionally, these neural responses were correlated with behavioral performance in a deviant detection task. Source-localized Temporal Response Functions revealed distinct cortical dynamics of responses to sentences in left temporal areas and equations in bilateral temporal, parietal, and motor areas. Finally, the target of attention could be decoded from MEG responses, especially in left superior parietal areas. In short, the neural responses to arithmetic and language are especially well segregated during the cocktail party paradigm, and the correlation with behavior suggests that they may be linked to successful comprehension or calculation.
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    Noninvasive neural decoding of overt and covert hand movement
    (2010) Bradberry, Trent Jason; Contreras-Vidal, José L.; Bioengineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    It is generally assumed that the signal-to-noise ratio and information content of neural data acquired noninvasively via magnetoencephalography (MEG) or scalp electroencephalography (EEG) are insufficient to extract detailed information about natural, multi-joint movements of the upper limb. If valid, this assumption could severely limit the practical usage of noninvasive signals in brain-computer interface (BCI) systems aimed at continuous complex control of arm-like prostheses for movement impaired persons. Fortunately this dissertation research casts doubt on the veracity of this assumption by extracting continuous hand kinematics from MEG signals collected during a 2D center-out drawing task (Bradberry et al. 2009, NeuroImage, 47:1691-700) and from EEG signals collected during a 3D center-out reaching task (Bradberry et al. 2010, Journal of Neuroscience, 30:3432-7). In both studies, multiple regression was performed to find a matrix that mapped past and current neural data from multiple sensors to current hand kinematic data (velocity). A novel method was subsequently devised that incorporated the weights of the mapping matrix and the standardized low resolution electromagnetic tomography (sLORETA) software to reveal that the brain sources that encoded hand kinematics in the MEG and EEG studies were corroborated by more traditional studies that required averaging across trials and/or subjects. Encouraged by the favorable results of these off-line decoding studies, a BCI system was developed for on-line decoding of covert movement intentions that provided users with real-time visual feedback of the decoder output. Users were asked to use only their thoughts to move a cursor to acquire one of four targets on a computer screen. With only one training session, subjects were able to accomplish this task. The promising results of this dissertation research significantly advance the state-of-the-art in noninvasive BCI systems.