Perceptual-motor processes

Anstis, et al. (1985) have reported that under certain conditions the visual system adapts and the perception of apparent motion breaks down. The present research indicates that breakdown is actually a result of same-place mechanisms successfully competing with motion-detecting mechanisms. Thus, the perception of stationarity (with flicker) can occur at the start of a trial and spontaneously switch to the perception of motion, or vice versa. The response of same-place mechanisms depends on the zero-hertz energy at each location of an apparent motion stimulus, whereas the response of motion mechanisms depends on the time-varying energy. Average luminance, luminance contrast, the temporal symmetry of the apparent motion display, and relative phase are manipulated to investigate competition between same-place and motion-detecting mechanisms.

The midline thalamus of rats is anatomically and functionally part of the "limbic" thalamus. The midline thalamic rhomboid nucleus (RH) has not been well characterized. The rhomboid nucleus is located just dorsal to the reuniens nucleus (RE), and just ventral to the central medial nucleus (CeM) of the thalamus. Using the retrograde tracer fluorogold (FG) and anti-FG antibody, we examined afferent projections to RH in the rat. Control injections were also made in CeM and the submedial nucleus of thalamus (SMT). The main sources of input to RH were from the anterior cingulate, agranular insular, orbital, and somatosensory cortices; the claustrum; the reticular nucleus of the thalamus; the posterior hypothalamus; and various brainstem structures. Based on patterns of the afferent projections, the role of RH in arousal, attention, and mnemonic functions is discussed.

Perception and behavior are mediated by a widely distributed network of brain areas. Our main concern is, how do the components of the network interact in order to give us a variety of complex coordinated behavior? We first define the nodes of the network, termed functional units, as a strongly coupled ensemble of non-identical neurons and demonstrate that the dynamics of such an ensemble may be approximated by a low dimensional set of equations. The dynamics is studied in two different contexts, sensorimotor coordination and multisensory integration. First, we treat movement coupled to the environment as a driven functional unit. Our central hypothesis is that this coupling must be minimally parametric. We demonstrate the experimental validity of this hypothesis and propose a theoretical model that explains the results of our experiment. A second example of the dynamics of functional units is evident in the domain of multisensory integration. We employ a novel rhythmic multisensory paradigm designed to capture the temporal features of multisensory integration parametrically. The relevant parameters of our experiment are the inter-onset interval between pairs of rhythmically presented stimuli and the frequency of presentation. We partition the two dimensional parameter space using subjects perception of the stimulus sequence. The general features of the partitioning are modality independent suggesting that these features depend on the coupling between the unisensory subsystems. We develop a model with coupled functional units and suggest a candidate coupling scheme. In subsequent chapters we probe the neural correlates of multisensory integration using fMRI and EEG. The results of our fMRI experiment demonstrate that multisensory integration is mediated by a network consisting of primary sensory areas, inferior parietal lobule, prefrontal areas and the posterior midbrain. Different percepts lead to the recruitment of different areas and their disengagement for other percepts. In analyzing the EEG data, we first develop a mathematical framework that allows us to differentiate between sources activated for both unisensory and multisensory stimulation from those sources activated only for multisensory stimulation. Using this methodology we show that the influences of multisensory processing may be seen at an early (40--60 ms) stage of sensory processing.

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.

It has been argued that the perception of apparent motion is based on the detection of counterchange (oppositely signed changes in luminance contrast at pairs of spatial locations) rather than motion energy (spatiotemporal changes in luminance). A constraint in furthering this distinction is that both counterchange and motion energy are present for most motion stimuli. Three experiments used illusory-contour and luminance-based stimuli to segregate (experiments 1 and 2) and combine (experiment 3) counterchange and motion energy information. Motion specified by counterchange was perceived for translating illusory squares over a wide range of frame durations, and preferentially for short motion paths. Motion specified by motion energy was diminished by relatively long frame durations, but was not affected by the length of the motion path. Results for the combined stimulus were consistent with counterchange as the basis for apparent motion perception, despite the presence of motion energy.

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.