A Systematic approach to visual system rehabilitation: population receptive field analysis and real-time functional magnetic resonance imaging neurofeedback methods

Visual information transmission flows from the retinal ganglion cells to the lateral geniculate nucleus and then to the primary visual cortex (V1), the chief cortical relay of visual information and in turn, to “higher” extrastriate areas. Beyond area V1, visual processing is distributed across multiple interconnected brain areas, the precise role of which and their interactions are not yet, completely understood. To add to the dynamic complexity of the system, feedback from higher areas and modulation by top-down processes, such as attention are often critical in the formation of visual percepts

A Systematic Approach to Visual System Rehabilitation — Population Receptive Field Analysis and Real-time Functional Magnetic Resonance Imaging Neurofeedback Methods

Visual information transmission flows from the retinal ganglion cells to the lateral geniculate nucleus and then to the primary visual cortex (V1), the chief cortical relay of visual information and in turn, to “higher” extrastriate areas. Beyond area V1, visual processing is distributed across multiple interconnected brain areas, the precise role of which and their interactions are not yet, completely understood. To add to the dynamic complexity of the system, feedback from higher areas and modulation by top-down processes, such as attention are often critical in the formation of visual percepts

Asynchronous suppression of visual cortex during absence seizures in stargazer mice

Absence epilepsy is a common childhood disorder featuring frequent cortical spike-wave seizures with a loss of awareness and behavior. Using the calcium indicator GCaMP6 with in vivo 2-photon cellular microscopy and simultaneous electrocorticography, we examined the collective activity profiles of individual neurons and surrounding neuropil across all layers in V1 during spike-wave seizure activity over prolonged periods in stargazer mice. We show that most (~80%) neurons in all cortical layers reduce their activity during seizures, whereas a smaller pool activates or remains neutral. Unexpectedly, ictal participation of identified single-unit activity is not fixed, but fluctuates on a flexible time scale from seizure to seizure. Pairwise correlation analysis of calcium activity reveals a surprising lack of synchrony among neurons and neuropil patches in all layers during seizures. Our results demonstrate asynchronous suppression of visual cortex during absence seizures, with potential implications for understanding cortical network function during EEG states of reduced awareness

Estimating average single-neuron visual receptive field sizes by fMRI

The noninvasive estimation of neuronal receptive field (RF) properties in vivo allows a detailed understanding of brain organization as well as its plasticity by longitudinal following of potential changes. Visual RFs measured invasively by electrophysiology in animal models have traditionally provided a great extent of our current knowledge about the visual brain and its disorders. Voxel-based estimates of population RF (pRF) by functional magnetic resonance imaging (fMRI) in humans revolutionized the field and have been used extensively in numerous studies. However, current methods cannot estimate single-neuron RF sizes as they reflect large populations of neurons with individual RF scatter. Here, we introduce an approach to estimate RF size using spatial frequency selectivity to checkerboard patterns. This method allowed us to obtain noninvasive, average single-neuron RF estimates over a large portion of human early visual cortex. These estimates were significantly smaller compared with prior pRF methods. Furthermore, fMRI and electrophysiology experiments in nonhuman primates demonstrated an exceptionally good match, validating the approach

Lower cerebrospinal fluid/plasma fibroblast growth factor 21 (FGF21) ratios and placental FGF21 production in gestational diabetes

Objectives: Circulating Fibroblast Growth Factor 21 (FGF21) levels are increased in insulin resistant states such as obesity, type 2 diabetes mellitus and gestational diabetes mellitus (GDM). In addition, GDM is associated with serious maternal and fetal complications. We sought to study human cerebrospinal fluid (CSF) and corresponding circulating FGF21 levels in women with gestational diabetes mellitus (GDM) and in age and BMI matched control subjects. We also assessed FGF21 secretion from GDM and control human placental explants. Design: CSF and corresponding plasma FGF21 levels of 24 women were measured by ELISA [12 GDM (age: 26–47 years, BMI: 24.3–36.3 kg/m2) and 12 controls (age: 22–40 years, BMI: 30.1–37.0 kg/m2)]. FGF21 levels in conditioned media were secretion from GDM and control human placental explants were also measured by ELISA. Results: Glucose, HOMA-IR and circulating NEFA levels were significantly higher in women with GDM compared to control subjects. Plasma FGF21 levels were significantly higher in women with GDM compared to control subjects [234.3 (150.2–352.7) vs. 115.5 (60.5–188.7) pg/ml; P<0.05]. However, there was no significant difference in CSF FGF21 levels in women with GDM compared to control subjects. Interestingly, CSF/Plasma FGF21 ratio was significantly lower in women with GDM compared to control subjects [0.4 (0.3–0.6) vs. 0.8 (0.5–1.6); P<0.05]. FGF21 secretion into conditioned media was significantly lower in human placental explants from women with GDM compared to control subjects (P<0.05). Conclusions: The central actions of FGF21 in GDM subjects maybe pivotal in the pathogenesis of insulin resistance in GDM subjects. The significance of FGF21 produced by the placenta remains uncharted and maybe crucial in our understanding of the patho-physiology of GDM and its associated maternal and fetal complications. Future research should seek to elucidate these points

Invariant template matching in systems with spatiotemporal coding: a vote for instability

We consider the design of a pattern recognition that matches templates to images, both of which are spatially sampled and encoded as temporal sequences. The image is subject to a combination of various perturbations. These include ones that can be modeled as parameterized uncertainties such as image blur, luminance, translation, and rotation as well as unmodeled ones. Biological and neural systems require that these perturbations be processed through a minimal number of channels by simple adaptation mechanisms. We found that the most suitable mathematical framework to meet this requirement is that of weakly attracting sets. This framework provides us with a normative and unifying solution to the pattern recognition problem. We analyze the consequences of its explicit implementation in neural systems. Several properties inherent to the systems designed in accordance with our normative mathematical argument coincide with known empirical facts. This is illustrated in mental rotation, visual search and blur/intensity adaptation. We demonstrate how our results can be applied to a range of practical problems in template matching and pattern recognition.Comment: 52 pages, 12 figure

Neural Decision Boundaries for Maximal Information Transmission

We consider here how to separate multidimensional signals into two categories, such that the binary decision transmits the maximum possible information transmitted about those signals. Our motivation comes from the nervous system, where neurons process multidimensional signals into a binary sequence of responses (spikes). In a small noise limit, we derive a general equation for the decision boundary that locally relates its curvature to the probability distribution of inputs. We show that for Gaussian inputs the optimal boundaries are planar, but for non-Gaussian inputs the curvature is nonzero. As an example, we consider exponentially distributed inputs, which are known to approximate a variety of signals from natural environment.Comment: 5 pages, 3 figure

Weak pairwise correlations imply strongly correlated network states in a neural population

Biological networks have so many possible states that exhaustive sampling is impossible. Successful analysis thus depends on simplifying hypotheses, but experiments on many systems hint that complicated, higher order interactions among large groups of elements play an important role. In the vertebrate retina, we show that weak correlations between pairs of neurons coexist with strongly collective behavior in the responses of ten or more neurons. Surprisingly, we find that this collective behavior is described quantitatively by models that capture the observed pairwise correlations but assume no higher order interactions. These maximum entropy models are equivalent to Ising models, and predict that larger networks are completely dominated by correlation effects. This suggests that the neural code has associative or error-correcting properties, and we provide preliminary evidence for such behavior. As a first test for the generality of these ideas, we show that similar results are obtained from networks of cultured cortical neurons.Comment: Full account of work presented at the conference on Computational and Systems Neuroscience (COSYNE), 17-20 March 2005, in Salt Lake City, Utah (http://cosyne.org