Dissipation and spontaneous symmetry breaking in brain dynamics

We compare the predictions of the dissipative quantum model of brain with neurophysiological data collected from electroencephalograms resulting from high-density arrays fixed on the surfaces of primary sensory and limbic areas of trained rabbits and cats. Functional brain imaging in relation to behavior reveals the formation of coherent domains of synchronized neuronal oscillatory activity and phase transitions predicted by the dissipative model.Comment: Restyled, slight changes in title and abstract, updated bibliography, J. Phys. A: Math. Theor. Vol. 41 (2008) in prin

Binocular summation of second-order global motion signals in human vision

Although many studies have examined the principles governing first-order global motion perception, the mechanisms that mediate second-order global motion perception remain unresolved. This study investigated the existence, nature and extent of the binocular advantage for encoding second-order (contrast-defined) global motion. Motion coherence thresholds (79.4 % correct) were assessed for determining the direction of radial, rotational and translational second-order motion trajectories as a function of local element modulation depth (contrast) under monocular and binocular viewing conditions. We found a binocular advantage for second-order global motion processing for all motion types. This advantage was mainly one of enhanced modulation sensitivity, rather than of motion-integration. However, compared to findings for first-order motion where the binocular advantage was in the region of a factor of around 1.7 [Hess et al., 2007, Vision Research 47, 1682-1692 & the present study], the binocular advantage for second-order global 2 motion was marginal, being in the region of around 1.2. This weak enhancement in sensitivity with binocular viewing is considerably less than would be predicted by conventional models of either probability summation or neural summation

A Better Looking Brain: Image Pre-Processing Approaches for fMRI Data

Researchers in the field of functional neuroimaging have faced a long standing problem in pre-processing low spatial resolution data without losing meaningful details within. Commonly, the brain function is recorded by a technique known as echo-planar imaging that represents the measure of blood flow (BOLD signal) through a particular location in the brain as an array of intensity values changing over time. This approach to record a movie of blood flow in the brain is known as fMRI. The neural activity is then studied from the temporal correlation patterns existing within the fMRI time series. However, the resulting images are noisy and contain low spatial detail, thus making it imperative to pre-process them appropriately to derive meaningful activation patterns. Two of the several standard preprocessing steps employed just before the analysis stage are denoising and normalization. Fundamentally, it is difficult to perfectly remove noise from an image without making assumptions about signal and noise distributions. A convenient and commonly used alternative is to smooth the image with a Gaussian filter, but this method suffers from various obvious drawbacks, primarily loss of spatial detail. A greater challenge arises when we attempt to derive average activation patterns from fMRI images acquired from a group of individuals. The brain of one individual differs from others in a structural sense as well as in a functional sense. Commonly, the inter-individual differences in anatomical structures are compensated for by co-registering each subject\u27s data to a common normalization space, known as spatial normalization. However, there are no existing methods to compensate for the differences in functional organization of the brain. This work presents first steps towards data-driven robust algorithms for fMRI image denoising and multi-subject image normalization by utilizing inherent information within fMRI data. In addition, a new validation approach based on spatial shape of the activation regions is presented to quantify the effects of preprocessing and also as a tool to record the differences in activation patterns between individual subjects or within two groups such as healthy controls and patients with mental illness. Qualititative and quantitative results of the proposed framework compare favorably against existing and widely used model-driven approaches such as Gaussian smoothing and structure-based spatial normalization. This work is intended to provide neuroscience researchers tools to derive more meaningful activation patterns to accurately identify imaging biomarkers for various neurodevelopmental diseases and also maximize the specificity of a diagnosis