Brain mapping

The dissertation discusses various node identification techniques as well as their downstream effects on network characteristics using task-activated fMRI data from two working memory paradigms: a verbal n-back task and a visual n-back task. The three node identification techniques examined within this work include: a group-aggregated approach, a subject-specific approach, and a voxel wise approach. The first chapters highlight crucial differences between group-aggregated and subject-specific methods of isolating nodes prior to undirected functional connectivity analysis. Results show that the two techniques yield significantly different network interactions and local network characteristics, despite having their network nodes restricted to the same anatomical regions. Prior to the introduction of the third technique, a chapter is dedicated to explaining the differences between a priori approaches (like the previously introduced group-aggregated and subject-specific techniques) and no a priori approaches (like the voxel wise approach). The chapter also discusses two ways to aggregate signal for node representation within a network: using the signal from a single voxel or aggregating signal across a group of neighboring voxels. Subsequently, a chapter is dedicated to introducing a novel processing pipeline which uses a data driven voxel wise approach to identify network nodes. The novel pipeline defines nodes using spatial temporal features generated by a deep learning algorithm and is validated by an analysis showing that the isolated nodes are condition and subject specific. The dissertation concludes by summarizing the main takeaways from each of the three analyses as well as highlighting the advantages and disadvantages of each of the three node identification techniques.

The present study aimed at quantifying the topographic distribution of spectral power as measured with electroencephalogram (EEG) in patients with opioid use disorder (OUD) across five broad band frequencies (δ, θ, α, β, and γ). Through comparative groups of healthy controls, patients with methamphetamine use disorder, and patients with alcohol use disorder, it was determined that OUD EEG spectral power was globally increased in the δ frequency, and more region-specific in others (frontal lobes in θ and β frequencies). α frequency was reduced in occipital lobes in OUD. The observed changes are discussed in terms of the microcircuit-level changes in the cortex. Based on these findings, EEG may prove to be a valuable tool for diagnostic and prognostic evaluation of OUD.

We sought to better understand human motor control by investigating functional interactions between the Supplementary Motor Area (SMA), dorsal Anterior Cingulate Cortex (dACC), and primary motor cortex (M1) in healthy adolescent participants performing visually coordinated unimanual finger-movement and n-back working memory tasks. We discovered modulation of the SMA by the dACC by analysis of fMRI BOLD time series recorded from the three ROIs (SMA, dACC, and M1) in each participant. Two measures of functional interaction were used: undirected functional connectivity was measured using the Pearson product-moment correlation coefficient (PMCC), and directed functional connectivity was measured from linear autoregressive (AR) models. In the first project, task-specific modulation of the SMA by the dACC was discovered while subjects performed a coordinated unimanual finger-movement task, in which the finger movement was synchronized with an exogenous visual stimulus. In the second project, modulation of the SMA by the dACC was found to be significantly greater in the finger coordination task than in an n-back working memory, in which the same finger movement signified a motor response indicating a 0-back or 2-back working memory match. We thus demonstrated in the first study that the dACC sends task-specific directed signals to the supplementary motor area, suggesting a role for the dACC in top-down motor control. Finally, the second study revealed that these signals were significantly greater in the coordinated motor task than in the n-back working memory task, suggesting that the modulation of the SMA by the dACC was associated with sustained, continuous motor production and/or motor expectation, rather than with the motor movement itself.

It is well established that anticipation of the arrival of an expected stimulus is accompanied by rich ongoing oscillatory neurodynamics, which span and link large areas of cortex. An intriguing possibility is that these dynamic interactions may convey knowledge that is embodied by large-scale neurocognitive networks from higher level regions of multi-model cortex to lower level primary sensory areas. In the current study, using autoregressive spectral analysis, we establish that during the anticipatory phase of a visual discrimination task there are rich patterns of coherent interaction between various levels of the ventral visual hierarchy across the frequency spectrum of 8 - 90 Hz. Using spectral Granger causality we determined that a subset of these interactions carry beta frequency (14 - 30 Hz) top-down influences from higher level visual regions V4 and TEO to primary visual cortex. We investigated the functional significance of these top-down interactions by correlating the magnitude of the anticipatory signals with the amplitude of the visual evoked potential that was elicited by stimulus processing. We found that in one third of the extrastriate-striate pairs, tested in three monkeys, the amplitude of the visual evoked response is well predicted by the magnitude of pre-stimulus coherent top-down anticipatory influences. To investigate the dynamics of the coherent and topdown Granger causal interactions, we analyzed the relationship between coherence and top-down Granger causality with stimulus onset asynchrony. This analysis revealed that in an abundance of cases the magnitudes of the coherent interactions and top-down directional influences scaled with the length of time that had elapsed before stimulus onset.

This study investigated electroencephalographic differences related to cue (central left- or right-directed arrows) in a covert endogenous visual spatial attention task patterned after that of Hopf and Mangun (2000). This was done with the intent of defining the timing of components in relation to cognitive processes within the cue-target interval. Multiple techniques were employed to do this. Event-related potentials (ERPs) were examined using Independent Component Analysis. This revealed a significant N1, between 100:200 ms post-cue, greater contralateral to the cue. Difference wave ERPs, left minus right cue-locked data, divulged significant early directing attention negativity (EDAN) at 200:400 ms post-cue in the right posterior which reversed polarity in the left posterior. Temporal spectral evolution (TSE) analysis of the alpha band revealed three stages, (1) high bilateral alpha precue to 120 ms post-cue, (2) an event related desynchronization (ERD) from approximately 120 ms: 500 ms post-cue, and (3) an event related synchronization (ERS) rebound, 500: 900 ms post-cue, where alpha amplitude, a measure of activity, was highest contralateral to the ignored hemifield and lower contralateral to the attended hemifield. Using a combination of all of these components and scientific literature in this field, it is possible to plot out the time course of the cognitive events and their neural correlates.

Traditionally brain function is studied through measuring physiological responses in controlled sensory, motor, and cognitive paradigms. However, even at rest, in the absence of overt goal-directed behavior, collections of cortical regions consistently show temporally coherent activity. In humans, these resting state networks have been shown to greatly overlap with functional architectures present during consciously directed activity, which motivates the interpretation of rest activity as day dreaming, free association, stream of consciousness, and inner rehearsal. In monkeys, it has been shown though that similar coherent fluctuations are present during deep anesthesia when there is no consciousness. These coherent fluctuations have also been characterized on multiple temporal scales ranging from the fast frequency regimes, 1-100 Hz, commonly observed in EEG and MEG recordings, to the ultra-slow regimes, < 0.1 Hz, observed in the Blood Oxygen Level Dependent (BOLD) signal of functi onal magnetic resonance imaging (fMRI). However, the mechanism for their genesis and the origin of the ultra-slow frequency oscillations has not been well understood. Here, we show that comparable resting state networks emerge from a stability analysis of the network dynamics using biologically realistic primate brain connectivity, although anatomical information alone does not identify the network. We specifically demonstrate that noise and time delays via propagation along connecting fibres are essential for the emergence of the coherent fluctuations of the default network. The combination of anatomical structure and time delays creates a spacetime structure in which the neural noise enables the brain to explore various functional configurations representing its dynamic repertoire.

This study examined the topography of prefrontal molds of human endocasts using three-dimensional laser scanning and geographic information systems (GIS) in order to carry out intra-species comparisons. Overall brain topography can indicate when major reorganizational shifts in brain structure happened in our evolutionalry history, and these shifts may indicate major shifts in cognition and behavior. Endocasts are one of the sole sources of information about extinct hominin brains ; they reproduce details of the brain's external morphology. Analysis of endocast morphology has never been done using GIS methodology. The use of GIS helps to overcome previous obstacles in regards to endocast analysis. Since this methodology is new, this research focuses on only one species, Homo sapiens and the area of focus is narrowed to the frontal lobe, specifically Broca's cap. This area is associated with speech in humans and is therefore of evolutionary significance. The variability in lateralization of this feature was quantified.

Research presented in this dissertation has the central aim of applying a novel method of source localization called beamforming to neuromagnetic recordings for characterizing dynamic spatiotemporal activity of sensorimotor brain processes in subjects during rhythmic auditory stimulation, self-paced movement, and two sensorimotor coordination (synchronization and syncopation) tasks known to differentiate on the basis of behavioral stability. Each experimental condition was performed at different rates resulting in 26 experimental runs per subject. Event-related neural responses were recorded with a whole-head MEG system and characterized in terms of their phase-locked (evoked) and non-phase-locked (induced) activity within the brain using both whole-brain analysis and region of interest (ROI) analysis. The analysis of the auditory conditions revealed that neural activity within extraauditory areas throughout the brain, including sensorimotor cortex, is modulated by rhythmic auditory stimulation. Additionally, the temporal profile of this activity was markedly different between sensorimotor and auditory cortex, possibly revealing different physiological processes, entrained within a common network for representing isochronic auditory events. During self-paced movements cycle-by-cycle dynamics of induced neural activity was measured and consistent neuro-modulation in the form of event-related desynchronization (ERD) and synchronization (ERS) was observed at all rates investigated (0.25 - 1.75Hz). ERD and ERS modulations exhibited dynamic scaling properties on a cycle-by-cycle basis that depended on the period of movement. Activity in the beta- and mu-bands also exhibited patterns of phase locking between sensorimotor locations. Phase locking patterns exhibited abrupt decreases with increases in movement rate.

The neurophysiological signals that are recorded in EEG (electroencephalography) and MEG (magnetoencephalography) originate from current flow perpendicular to the cortical surface due to the columnar organization of pyramidal cells in the cortical gray matter. These locations and directions have been used as anatomical constraints for dipolar sources in estimations of neural activity from MEG recordings. Here we extend anatomically constrained beamforming to EEG, which requires a more sophisticated forward model than MEG due to the blurring of the electric potential at tissue boundaries, but in contrast to MEG, EEG can account for both tangential and radial sources. Using computed tomography (CT) scans we create a realistic three-layer head model consisting of tessellated surfaces representing the tissue boundaries cerebrospinal fluid-skull, skull-scalp and scalp-air. The cortical gray matter surface, the anatomical constraint for the source dipoles, is extracted from magnetic resonance imaging (MRI) scans. EEG beamforming is implemented in a set of simulated data and compared for three different head models: single sphere, multi-shell sphere and realistic geometry multi-shell model that employs a boundary element method. Beamformer performance is also analyzed and evaluated for multiple dipoles and extended sources (patches). We show that using anatomical constraints with the beamforming algorithm greatly reduces computation time while increasing the spatial accuracy of the reconstructed sources of neural activity. Using the spatial Laplacian instead of the electric potential in combination with beamforming further improves the spatial resolution and allows for the detection of highly correlated sources.