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

Regional Differences in the Sensitivity of MEG for Interictal Spikes in Epilepsy

MEG interictal spikes as recorded in epilepsy patients are a reflection of intracranial interictal activity. This study investigates the relationship between the estimated sources of MEG spikes and the location, distribution and size of interictal spikes in the invasive ECoG of a group of 38 epilepsy patients that are monitored for pre-surgical evaluation. An amplitude/surface area measure is defined to quantify and rank ECoG spikes. It is found that all MEG spikes are associated with an ECoG spike that is among the three highest ranked in a patient. Among the different brain regions considered, the fronto-orbital, inter-hemispheric, tempero-lateral and central regions stand out. In an accompanying simulation study it is shown that for hypothesized extended sources of larger sizes, as suggested by the data, source location, orientation and curvature can partly explain the observed sensitivity of MEG for interictal spikes

Gamma and Beta Oscillations in Human MEG Encode the Contents of Vibrotactile Working Memory

Ample evidence suggests that oscillations in the beta band represent quantitative information about somatosensory features during stimulus retention. Visual and auditory working memory (WM) research, on the other hand, has indicated a predominant role of gamma oscillations for active WM processing. Here we reconciled these findings by recording whole-head magnetoencephalography during a vibrotactile frequency comparison task. A Braille stimulator presented healthy subjects with a vibration to the left fingertip that was retained in WM for comparison with a second stimulus presented after a short delay. During this retention interval spectral power in the beta band from the right intraparietal sulcus and inferior frontal gyrus (IFG) monotonically increased with the to-be-remembered vibrotactile frequency. In contrast, induced gamma power showed the inverse of this pattern and decreased with higher stimulus frequency in the right IFG. Together, these results expand the previously established role of beta oscillations for somatosensory WM to the gamma band and give further evidence that quantitative information may be processed in a fronto-parietal network

Measuring Directed Functional Connectivity Using Non-Parametric Directionality Analysis : Validation and Comparison with Non-Parametric Granger Causality

BACKGROUND: 'Non-parametric directionality' (NPD) is a novel method for estimation of directed functional connectivity (dFC) in neural data. The method has previously been verified in its ability to recover causal interactions in simulated spiking networks in Halliday et al. (2015). METHODS: This work presents a validation of NPD in continuous neural recordings (e.g. local field potentials). Specifically, we use autoregressive models to simulate time delayed correlations between neural signals. We then test for the accurate recovery of networks in the face of several confounds typically encountered in empirical data. We examine the effects of NPD under varying: a) signal-to-noise ratios, b) asymmetries in signal strength, c) instantaneous mixing, d) common drive, e) data length, and f) parallel/convergent signal routing. We also apply NPD to data from a patient who underwent simultaneous magnetoencephalography and deep brain recording. RESULTS: We demonstrate that NPD can accurately recover directed functional connectivity from simulations with known patterns of connectivity. The performance of the NPD measure is compared with non-parametric estimators of Granger causality (NPG), a well-established methodology for model-free estimation of dFC. A series of simulations investigating synthetically imposed confounds demonstrate that NPD provides estimates of connectivity that are equivalent to NPG, albeit with an increased sensitivity to data length. However, we provide evidence that: i) NPD is less sensitive than NPG to degradation by noise; ii) NPD is more robust to the generation of false positive identification of connectivity resulting from SNR asymmetries; iii) NPD is more robust to corruption via moderate amounts of instantaneous signal mixing. CONCLUSIONS: The results in this paper highlight that to be practically applied to neural data, connectivity metrics should not only be accurate in their recovery of causal networks but also resistant to the confounding effects often encountered in experimental recordings of multimodal data. Taken together, these findings position NPD at the state-of-the-art with respect to the estimation of directed functional connectivity in neuroimaging

Language Mapping With Magnetoencephalography: An Update on the Current State of Clinical Research and Practice With Considerations for Clinical Practice Guidelines

Numerous studies have shown that language processing is not limited to a few brain areas. Visual or auditory stimuli activate corresponding cortical areas, then memory identifies the word or image, Wernicke\u27s and Broca\u27s areas support the processing for either reading/listening or speaking and many areas of the brain are recruited. Determining how a normal person processes language helps clinicians and scientist to understand how brain pathologies such as tumor or stroke can affect changes in language processing. Patients with epilepsy may develop atypical language organization. Over time, the chronic nature of epileptic activity, or changes from a tumor or stroke, can result in a shift of language processing area from the left to the right hemisphere, or re-routing of language pathways from traditional to non-traditional areas within the dominant left hemisphere. It is important to determine where these language areas are prior to brain surgery. MEG evoked responses reflecting cerebral activation of receptive and expressive language processing can be localized using several different techniques: Single equivalent current dipole, current distribution techniques or beamformer techniques. Over the past 20 years there have been at least 25 validated MEG studies that indicate MEG can be used to determine the dominant hemisphere for language processing. The use of MEG neuroimaging techniques is needed to reliably predict altered language networks in patients and to provide identification of language eloquent cortices for localization and lateralization necessary for clinical care

Sensitivity of MEG and EEG to Source Orientation

An important difference between magnetoencephalography (MEG) and electroencephalography (EEG) is that MEG is insensitive to radially oriented sources. We quantified computationally the dependency of MEG and EEG on the source orientation using a forward model with realistic tissue boundaries. Similar to the simpler case of a spherical head model, in which MEG cannot see radial sources at all, for most cortical locations there was a source orientation to which MEG was insensitive. The median value for the ratio of the signal magnitude for the source orientation of the lowest and the highest sensitivity was 0.06 for MEG and 0.63 for EEG. The difference in the sensitivity to the source orientation is expected to contribute to systematic differences in the signal-to-noise ratio between MEG and EEG.National Institutes of Health (U.S.) (Grant NS057500)National Institutes of Health (U.S.) (Grant NS037462)National Institutes of Health (U.S.) (Grant HD040712)National Center for Research Resources (U.S.) (P41RR14075)Mind Research Networ

The detection of phase amplitude coupling during sensory processing

There is increasing interest in understanding how the phase and amplitude of distinct neural oscillations might interact to support dynamic communication within the brain. In particular, previous work has demonstrated a coupling between the phase of low frequency oscillations and the amplitude (or power) of high frequency oscillations during certain tasks, termed phase amplitude coupling (PAC). For instance, during visual processing in humans, PAC has been reliably observed between ongoing alpha (8-13 Hz) and gamma-band (>40 Hz) activity. However, the application of PAC metrics to electrophysiological data can be challenging due to numerous methodological issues and lack of coherent approaches within the field. Therefore, in this article we outline the various analysis steps involved in detecting PAC, using an openly available MEG dataset from 16 participants performing an interactive visual task. Firstly, we localized gamma and alpha-band power using the Fieldtrip toolbox, and extracted time courses from area V1, defined using a multimodal parcelation scheme. These V1 responses were analyzed for changes in alpha-gamma PAC, using four common algorithms. Results showed an increase in alpha (7-13 Hz)-gamma (40-100 Hz) PAC in response to the visual grating stimulus, though specific patterns of coupling were somewhat dependent upon the algorithm employed. Additionally, post-hoc analyses showed that these results were not driven by the presence of non-sinusoidal oscillations, and that trial length was sufficient to obtain reliable PAC estimates. Finally, throughout the article, methodological issues and practical guidelines for ongoing PAC research will be discussed