Spatiotemporal techniques in multimodal imaging for brain mapping and epilepsy

Thesis (Ph.D.)--Boston UniversityThis thesis explored multimodal brain imaging using advanced spatiotemporal techniques. The first set of experiments were based on simulations. Much controversy exists in the literature regarding the differences between magnetoencephalography (MEG) and electroencephalography (EEG}, both practically and theoretically. The differences were explored using simulations that evaluated the expected signal-to-noise ratios from reasonable brain sources. MEG and EEG were found to be complementary, with each modality optimally suited to image activity from different areas of the cortical surface. Consequently, evaluations of epileptic patients and general neuroscience experiments will both benefit from simultaneously collected MEG/EEG. The second set of experiments represent an example of MEG combined with magnetic resonance imaging (MRI) and functional MRI (fMRI) applied to healthy subjects. The study set out to resolve two questions relating to shape perception. First, does the brain activate functional areas sequentially during shape perception, as has been suggested in recent literature? Second, which , if any, functional areas are active time-locked with reaction-time? The study found that functional areas are non-sequentially activated, and that area IT is active time-locked with reaction-time. These two points, coupled with the method for multimodal integration , can help further develop our understanding of shape perception in particular, and cortical dynamics in general for healthy subjects. Broadly, these two studies represent practical guidelines for epilepsy evaluations and brain mapping studies. For epilepsy studies, clinicians could combine MEG and EEG to maximize the probability of finding the source of seizures. For brain mapping in general, EEG, MEG, MRI and fMRI can be combined in the methods outlined here to obtain more sophisticated views of cortical dynamics

Spatiotemporal dynamics of single-letter reading: a combined ERP-FMRI study

This work investigates the neural correlates of single-letter reading by combining event-related potentials (ERPs) and functional magnetic resonance imaging (fMRI), thus exploiting their complementary spatiotemporal resolutions. Three externally-paced reading tasks were administered with an event-related design: passive observation of letters and symbols and active reading aloud of letters. ERP and fMRI data were separately recorded from 8 healthy adults during the same experimental conditions. Due to the presence of artifacts in the EEG signals, two subjects were discarded from further analysis. Independent Component Analysis was applied to ERPs, after dimensionality reduction by Principal Component Analysis: some independent components were clearly related to specific reading functions and the associated current density distributions in the brain were estimated with Low Resolution Electromagnetic Tomography Analysis method (LORETA). The impulse hemodynamic response function was modeled as a linear combination of linear B-spline functions and fMRI statistical analysis was performed by multiple linear regression. fMRI and LORETA maps were superimposed in order to identify the overlapping activations and the activated regions specifically revealed by each modality. The results showed the existence of neuronal networks functionally specific for letter processing and for explicit verbal-motor articulation, including the temporo-parietal and frontal regions. Overlap between fMRI and LORETA results was observed in the inferior temporal-middle occipital gyrus, suggesting that this area has a crucial and multifunctional role for linguistic and reading processes, likely because its spatial location and strong interconnection with the main visual and auditory sensory systems may have favored its specialization in grapheme-phoneme matching

Preparing Laboratory and Real-World EEG Data for Large-Scale Analysis: A Containerized Approach.

Large-scale analysis of EEG and other physiological measures promises new insights into brain processes and more accurate and robust brain-computer interface models. However, the absence of standardized vocabularies for annotating events in a machine understandable manner, the welter of collection-specific data organizations, the difficulty in moving data across processing platforms, and the unavailability of agreed-upon standards for preprocessing have prevented large-scale analyses of EEG. Here we describe a "containerized" approach and freely available tools we have developed to facilitate the process of annotating, packaging, and preprocessing EEG data collections to enable data sharing, archiving, large-scale machine learning/data mining and (meta-)analysis. The EEG Study Schema (ESS) comprises three data "Levels," each with its own XML-document schema and file/folder convention, plus a standardized (PREP) pipeline to move raw (Data Level 1) data to a basic preprocessed state (Data Level 2) suitable for application of a large class of EEG analysis methods. Researchers can ship a study as a single unit and operate on its data using a standardized interface. ESS does not require a central database and provides all the metadata data necessary to execute a wide variety of EEG processing pipelines. The primary focus of ESS is automated in-depth analysis and meta-analysis EEG studies. However, ESS can also encapsulate meta-information for the other modalities such as eye tracking, that are increasingly used in both laboratory and real-world neuroimaging. ESS schema and tools are freely available at www.eegstudy.org and a central catalog of over 850 GB of existing data in ESS format is available at studycatalog.org. These tools and resources are part of a larger effort to enable data sharing at sufficient scale for researchers to engage in truly large-scale EEG analysis and data mining (BigEEG.org)

Nonlinear brain dynamics as macroscopic manifestation of underlying many-body field dynamics

Neural activity patterns related to behavior occur at many scales in time and space from the atomic and molecular to the whole brain. Here we explore the feasibility of interpreting neurophysiological data in the context of many-body physics by using tools that physicists have devised to analyze comparable hierarchies in other fields of science. We focus on a mesoscopic level that offers a multi-step pathway between the microscopic functions of neurons and the macroscopic functions of brain systems revealed by hemodynamic imaging. We use electroencephalographic (EEG) records collected from high-density electrode arrays fixed on the epidural surfaces of primary sensory and limbic areas in rabbits and cats trained to discriminate conditioned stimuli (CS) in the various modalities. High temporal resolution of EEG signals with the Hilbert transform gives evidence for diverse intermittent spatial patterns of amplitude (AM) and phase modulations (PM) of carrier waves that repeatedly re-synchronize in the beta and gamma ranges at near zero time lags over long distances. The dominant mechanism for neural interactions by axodendritic synaptic transmission should impose distance-dependent delays on the EEG oscillations owing to finite propagation velocities. It does not. EEGs instead show evidence for anomalous dispersion: the existence in neural populations of a low velocity range of information and energy transfers, and a high velocity range of the spread of phase transitions. This distinction labels the phenomenon but does not explain it. In this report we explore the analysis of these phenomena using concepts of energy dissipation, the maintenance by cortex of multiple ground states corresponding to AM patterns, and the exclusive selection by spontaneous breakdown of symmetry (SBS) of single states in sequences.Comment: 31 page

Mapping Sensorimotor Function and Controlling Upper Limb Neuroprosthetics with Electrocorticography

Electrocorticography (ECoG) occupies a unique intermediate niche between microelectrode recordings of single neurons and recordings of whole brain activity via functional magnetic resonance imaging (fMRI). ECoG’s combination of high temporal resolution and wide area coverage make it an ideal modality for both functional brain mapping and brain-machine interface (BMI) for control of prosthetic devices. This thesis demonstrates the utility of ECoG, particularly in high gamma frequencies (70-120 Hz), for passive online mapping of language and motor behaviors, online control of reaching and grasping of an advanced robotic upper limb, and mapping somatosensory digit representations in the postcentral gyrus. The dissertation begins with a brief discussion of the framework for neuroprosthetic control developed by the collaboration between Johns Hopkins and JHU Applied Physics Laboratory (JHU/APL). Second, the methodology behind an online spatial-temporal functional mapping (STFM) system is described. Trial-averaged spatiotemporal maps of high gamma activity were computed during a visual naming and a word reading task. The system output is subsequently shown and compared to stimulation mapping. Third, simultaneous and independent ECoG-based control of reaching and grasping is demonstrated with the Modular Prosthetic Limb (MPL). The STFM system was used to identify channels whose high gamma power significantly and selectively increases during either reaching or grasping. Using this technique, two patients were able to rapidly achieve naturalistic control over simple movements by the MPL. Next, high-density ECoG (hdECoG) was used to map the cortical responses to mechanical vibration of the fingertips. High gamma responses exhibited a strong yet overlapping somatotopy that was not well replicated in other frequency bands. These responses are strong enough to be detected in single trials and used to classify the finger being stimulated with over 98% accuracy. Finally, the role of ECoG is discussed for functional mapping and BMI applications. ECoG occupies a unique role among neural recording modalities as a tool for functional mapping, but must prove its value relative to stimulation mapping. For BMI, ECoG lags microelectrode arrays but hdECoG may provide a more robust long-term interface with optimal spacing for sampling relevant cortical representations

A hybrid brain-computer interface based on motor intention and visual working memory

Non-invasive electroencephalography (EEG) based brain-computer interface (BCI) is able to provide alternative means for people with disabilities to communicate with and control over external assistive devices. A hybrid BCI is designed and developed for following two types of system (control and monitor). Our first goal is to create a signal decoding strategy that allows people with limited motor control to have more command over potential prosthetic devices. Eight healthy subjects were recruited to perform visual cues directed reaching tasks. Eye and motion artifacts were identified and removed to ensure that the subjects\u27 visual fixation to the target locations would have little or no impact on the final result. We applied a Fisher Linear Discriminate (FLD) analysis for single-trial classification of the EEG to decode the intended arm movement in the left, right, and forward directions (before the onsets of actual movements). The mean EEG signal amplitude near the PPC region 271-310 ms after visual stimulation was found to be the dominant feature for best classification results. A signal scaling factor developed was found to improve the classification accuracy from 60.11% to 93.91% in the two-class (left versus right) scenario. This result demonstrated great promises for BCI neuroprosthetics applications, as motor intention decoding can be served as a prelude to the classification of imagined motor movement to assist in motor disable rehabilitation, such as prosthetic limb or wheelchair control. The second goal is to develop the adaptive training for patients with low visual working memory (VWM) capacity to improve cognitive abilities and healthy individuals who seek to enhance their intellectual performance. VWM plays a critical role in preserving and processing information. It is associated with attention, perception and reasoning, and its capacity can be used as a predictor of cognitive abilities. Recent evidence has suggested that with training, one can enhance the VWM capacity and attention over time. Not only can these studies reveal the characteristics of VWM load and the influences of training, they may also provide effective rehabilitative means for patients with low VWM capacity. However, few studies have investigated VWM over a long period of time, beyond 5-weeks. In this study, a combined behavioral approach and EEG was used to investigate VWM load, gain, and transfer. The results reveal that VWM capacity is directly correlated to the reaction time and contralateral delay amplitude (CDA). The approximate magic number 4 was observed through the event-related potentials (ERPs) waveforms, where the average capacity is 2.8-item from 15 participants. In addition, the findings indicate that VWM capacity can be improved through adaptive training. Furthermore, after training exercises, participants from the training group are able to improve their performance accuracies dramatically compared to the control group. Adaptive training gains on non-trained tasks can also be observed at 12 weeks after training. Therefore, we conclude that all participants can benefit from training gains, and augmented VWM capacity can be sustained over a long period of time. Our results suggest that this form of training can significantly improve cognitive function and may be useful for enhancing the user performance on neuroprosthetics device

A Novel Synergistic Model Fusing Electroencephalography and Functional Magnetic Resonance Imaging for Modeling Brain Activities

Study of the human brain is an important and very active area of research. Unraveling the way the human brain works would allow us to better understand, predict and prevent brain related diseases that affect a significant part of the population. Studying the brain response to certain input stimuli can help us determine the involved brain areas and understand the mechanisms that characterize behavioral and psychological traits. In this research work two methods used for the monitoring of brain activities, Electroencephalography (EEG) and functional Magnetic Resonance (fMRI) have been studied for their fusion, in an attempt to bridge together the advantages of each one. In particular, this work has focused in the analysis of a specific type of EEG and fMRI recordings that are related to certain events and capture the brain response under specific experimental conditions. Using spatial features of the EEG we can describe the temporal evolution of the electrical field recorded in the scalp of the head. This work introduces the use of Hidden Markov Models (HMM) for modeling the EEG dynamics. This novel approach is applied for the discrimination of normal and progressive Mild Cognitive Impairment patients with significant results. EEG alone is not able to provide the spatial localization needed to uncover and understand the neural mechanisms and processes of the human brain. Functional Magnetic Resonance imaging (fMRI) provides the means of localizing functional activity, without though, providing the timing details of these activations. Although, at first glance it is apparent that the strengths of these two modalities, EEG and fMRI, complement each other, the fusion of information provided from each one is a challenging task. A novel methodology for fusing EEG spatiotemporal features and fMRI features, based on Canonical Partial Least Squares (CPLS) is presented in this work. A HMM modeling approach is used in order to derive a novel feature-based representation of the EEG signal that characterizes the topographic information of the EEG. We use the HMM model in order to project the EEG data in the Fisher score space and use the Fisher score to describe the dynamics of the EEG topography sequence. The correspondence between this new feature and the fMRI is studied using CPLS. This methodology is applied for extracting features for the classification of a visual task. The results indicate that the proposed methodology is able to capture task related activations that can be used for the classification of mental tasks. Extensions on the proposed models are examined along with future research directions and applications

A Quest for Meaning in Spontaneous Brain Activity - From fMRI to Electrophysiology to Complexity Science

The brain is not a silent, complex input/output system waiting to be driven by external stimuli; instead, it is a closed, self-referential system operating on its own with sensory information modulating rather than determining its activity. Ongoing spontaneous brain activity costs the majority of the brain\u27s energy budget, maintains the brain\u27s functional architecture, and makes predictions about the environment and the future. I have completed three separate studies on the functional significance and the organization of spontaneous brain activity. The first study showed that strokes disrupt large-scale network coherence in the spontaneous functional magnetic resonance imaging: fMRI) signals, and that the degree of such disruption predicts the behavioral impairment of the patient. This study established the functional significance of coherent patterns in the spontaneous fMRI signals. In the second study, by combining fMRI and electrophysiology in neurosurgical patients, I identified the neurophysiological signal underlying the coherent patterns in the spontaneous fMRI signal, the slow cortical potential: SCP). The SCP is a novel neural correlate of the fMRI signal, most likely underlying both spontaneous fMRI signal fluctuations and task-evoked fMRI responses. Some theoretical considerations have led me to propose a hypothesis on the involvement of the neural activity indexed by the SCP in the emergence of consciousness. In the last study I investigated the temporal organization across a wide range of frequencies in the spontaneous electrical field potentials recorded from the human brain. This study demonstrated that the arrhythmic, scale-free brain activity often discarded in human and animal electrophysiology studies in fact contains rich, complex structures, and further provided evidence supporting the functional significance of such activity