Model cortical association fields account for the time course and dependence on target complexity of human contour perception.

Can lateral connectivity in the primary visual cortex account for the time dependence and intrinsic task difficulty of human contour detection? To answer this question, we created a synthetic image set that prevents sole reliance on either low-level visual features or high-level context for the detection of target objects. Rendered images consist of smoothly varying, globally aligned contour fragments (amoebas) distributed among groups of randomly rotated fragments (clutter). The time course and accuracy of amoeba detection by humans was measured using a two-alternative forced choice protocol with self-reported confidence and variable image presentation time (20-200 ms), followed by an image mask optimized so as to interrupt visual processing. Measured psychometric functions were well fit by sigmoidal functions with exponential time constants of 30-91 ms, depending on amoeba complexity. Key aspects of the psychophysical experiments were accounted for by a computational network model, in which simulated responses across retinotopic arrays of orientation-selective elements were modulated by cortical association fields, represented as multiplicative kernels computed from the differences in pairwise edge statistics between target and distractor images. Comparing the experimental and the computational results suggests that each iteration of the lateral interactions takes at least [Formula: see text] ms of cortical processing time. Our results provide evidence that cortical association fields between orientation selective elements in early visual areas can account for important temporal and task-dependent aspects of the psychometric curves characterizing human contour perception, with the remaining discrepancies postulated to arise from the influence of higher cortical areas.

Modeling direct effects of neural current on MRI.

We investigate the effect of the magnetic field generated by neural activity on the magnitude and phase of the MRI signal in terms of a phenomenological parameter with the dimensions of length; it involves the product of the strength and duration of these currents. We obtain an analytic approximation to the MRI signal when the neuromagnetically induced phase is small inside the MRI voxel. The phase shift is the average of the MRI phase over the voxel, and therefore first order in that phase; and the reduction in the signal magnitude is one half the square of the standard deviation of the MRI phase, which is second order. The analytic approximation is compared with numerical simulations. For weak currents the agreement is excellent, and the magnitude change is generally much smaller than the phase shift. Using MEG data as a weak constraint on the current strength we find that for a net dipole moment of 10 nAm, a typical value for an evoked response, the reduction in the magnitude of the MRI signal is two parts in 10(5), and the maximum value of the overall phase shift is approximately 4 x 10(-3), obtained when the MRI voxel is displaced 2/3 the size of the neuronal activity. We also show signal changes over a large range of values of the net dipole moment. We compare these results with others in the literature. Our model overestimates the effect on the MRI signal.

Finite difference iterative solvers for electroencephalography: serial and parallel performance analysis.

Currently the resolution of the head models used in electroencephalography (EEG) studies is limited by the speed of the forward solver. Here, we present a parallel finite difference technique that can reduce the solution time of the governing Poisson equation for a head model. Multiple processors are used to work on the problem simultaneously in order to speed up the solution and provide the memory for solving large problems. The original computational domain is divided into multiple rectangular partitions. Each partition is then assigned to a processor, which is responsible for all the computations and inter-processor communication associated with the nodes in that particular partition. Since the forward solution time is mainly spent on solving the associated matrix equation, it is desirable to find the optimum matrix solver. A detailed comparison of various iterative solvers was performed for both isotropic and anisotropic realistic head models constructed from MRI images. The conjugate gradient (CG) method preconditioned with an advanced geometric multigrid technique was found to provide the best overall performance. For an anisotropic model with 256 x 128 x 256 cells, this technique provides a speedup of 508 on 32 processors over the serial CG solution, with a speedup of 20.1 and 25.3 through multigrid preconditioning and parallelization, respectively.

Bayesian brain source imaging based on combined MEG/EEG and fMRI using MCMC.

A number of brain imaging techniques have been developed in order to investigate brain function and to develop diagnostic tools for various brain disorders. Each modality has strengths as well as weaknesses compared to the others. Recent work has explored how multiple modalities can be integrated effectively so that they complement one another while maintaining their individual strengths. Bayesian inference employing Markov Chain Monte Carlo (MCMC) techniques provides a straightforward way to combine disparate forms of information while dealing with the uncertainty in each. In this paper we introduce methods of Bayesian inference as a way to integrate different forms of brain imaging data in a probabilistic framework. We formulate Bayesian integration of magnetoencephalography (MEG) data and functional magnetic resonance imaging (fMRI) data by incorporating fMRI data into a spatial prior. The usefulness and feasibility of the method are verified through testing with both simulated and empirical data.

Probabilistic forward model for electroencephalography source analysis.

Source localization by electroencephalography (EEG) requires an accurate model of head geometry and tissue conductivity. The estimation of source time courses from EEG or from EEG in conjunction with magnetoencephalography (MEG) requires a forward model consistent with true activity for the best outcome. Although MRI provides an excellent description of soft tissue anatomy, a high resolution model of the skull (the dominant resistive component of the head) requires CT, which is not justified for routine physiological studies. Although a number of techniques have been employed to estimate tissue conductivity, no present techniques provide the noninvasive 3D tomographic mapping of conductivity that would be desirable. We introduce a formalism for probabilistic forward modeling that allows the propagation of uncertainties in model parameters into possible errors in source localization. We consider uncertainties in the conductivity profile of the skull, but the approach is general and can be extended to other kinds of uncertainties in the forward model. We and others have previously suggested the possibility of extracting conductivity of the skull from measured electroencephalography data by simultaneously optimizing over dipole parameters and the conductivity values required by the forward model. Using Cramer-Rao bounds, we demonstrate that this approach does not improve localization results nor does it produce reliable conductivity estimates. We conclude that the conductivity of the skull has to be either accurately measured by an independent technique, or that the uncertainties in the conductivity values should be reflected in uncertainty in the source location estimates.

Dynamic neuroimaging of retinal light responses using fast intrinsic optical signals.

Transient intrinsic optical responses associated with neural activation offer an attractive strategy for dynamic imaging of neural activity, and may provide a noninvasive methodology for imaging of retinal function. Here we demonstrate the feasibility of near infrared imaging of fast intrinsic optical changes in isolated frog retina activated by visible light. Using a photodiode detector in a transmitted light geometry, we routinely measured dynamic transmitted optical responses in single passes, at the level of one part in 10(4) of background light. Rapid CCD image sequences acquired with transmitted light (bright field) illumination disclosed larger fractional responses and showed evidence of multiple response components with both negative- and positive-going signals with different timecourses. Dark field imaging further enhanced the contrast and sensitivity of optical measures of neural activation. High-resolution imaging disclosed optical responses in single pixels often exceeding 5%, of background light, allowing dynamic imaging at the resolution of single cells, in single passes. Fast optical signals are closely related to identified response components of the electroretinogram. Optical responses showed complex but consistent spatial organization from frame to frame. Our experimental results and theoretical analysis suggest that the optical responses may result from dynamic volume changes corresponding to ion and water flow across the cell membrane, directly associated with the electrophysiological response.

See globally, spike locally: oscillations in a retinal model encode large visual features.

We show that coherent oscillations among neighboring ganglion cells in a retinal model encode global topological properties, such as size, that cannot be deduced unambiguously from their local, time-averaged firing rates. Whereas ganglion cells may fire similar numbers of spikes in response to both small and large spots, only large spots evoke coherent high frequency oscillations, potentially allowing downstream neurons to infer global stimulus properties from their local afferents. To determine whether such information might be extracted over physiologically realistic spatial and temporal scales, we analyzed artificial spike trains whose oscillatory correlations were similar to those measured experimentally. Oscillatory power in the upper gamma band, extracted on single-trials from multi-unit spike trains, supported good to excellent size discrimination between small and large spots, with performance improving as the number of cells and/or duration of the analysis window was increased. By using Poisson distributed spikes to normalize the firing rate across stimulus conditions, we further found that coincidence detection, or synchrony, yielded substantially poorer performance on identical size discrimination tasks. To determine whether size encoding depended on contiguity independent of object shape, we examined the total oscillatory activity across the entire model retina in response to random binary images. As the ON-pixel probability crossed the percolation threshold, which marks the sudden emergence of large connected clusters, the total gamma-band activity exhibited a sharp transition, a phenomena that may be experimentally observable. Finally, a reanalysis of previously published oscillatory responses from cat ganglion cells revealed size encoding consistent with that predicted by the retinal model.

Improving source detection and separation in a spatiotemporal Bayesian inference dipole analysis.

Most existing spatiotemporal multi-dipole approaches for MEG/EEG source localization assume that the dipoles are active for the full time range being analysed. If the actual time range of activity of sources is significantly shorter than the time range being analysed, the detectability, localization and time-course determination of such sources may be adversely affected, especially for weak sources. In order to improve detectability and reconstruction of such sources, it is natural to add active time range information (starting time point and ending time point of source activation) for each candidate source as unknown parameters in the analysis. However, this adds additional nonlinear free parameters that could burden the analysis and could be unfeasible for some methods. Recently, we described a spatiotemporal Bayesian inference multi-dipole analysis for the MEG/EEG inverse problem. This approach treated the number of dipoles as a free parameter, produced realistic uncertainty estimates using a Markov chain Monte Carlo numerical sampling of the posterior distribution and included a method to reduce the unwanted effects of local minima. In this paper, our spatiotemporal Bayesian inference multi-dipole analysis is extended to incorporate active time range parameters of starting and stopping time points. The properties of this analysis in comparison to the previous one without active time range parameters are demonstrated through extensive studies using both simulated and empirical MEG data.

Near-infrared imaging of fast intrinsic optical responses in visible light-activated amphibian retina.

High performance functional imaging is needed for dynamic measurements of neural processing in retina. Emerging techniques for visual prosthesis also require advanced methodology for reliable validation of electromagnetic stimulation of the retina. Imaging of fast intrinsic optical responses associated with neural activation promises a variety of technical advantages over traditional single and multichannel electrophysiological techniques for these purposes, but the application of fast optical signals for neural imaging has been limited by low signal-to-noise ratio and high background light intensity. However, by using an optimized near-infrared probe light and improved optical system, we improve the optical signals substantially, allowing single pass measurements with approximately micron resolution. We image fast intrinsic optical responses with different optical modalities, i.e., bright field, dark field, and cross-polarization, from isolated retina activated by visible light stimulation. At single cell resolution, bright-field imaging discloses the maxima of optical responses approximately 5% dI/I, where dI is the dynamic optical change and I is the baseline light intensity. Dark-field imaging techniques further enhance the sensitivity of optical measurements, and show the maxima of optical responses exceeding 10% dI/I. Cross-polarized imaging provides optical sensitivity similar to dark-field imaging, but different patterns of neural activation are observed.

Spatiotemporal Bayesian inference dipole analysis for MEG neuroimaging data.

Recently, we described a Bayesian inference approach to the MEG/EEG inverse problem that used numerical techniques to estimate the full posterior probability distributions of likely solutions upon which all inferences were based [Schmidt, D.M., George, J.S., Wood, C.C., 1999. Bayesian inference applied to the electromagnetic inverse problem. Human Brain Mapping 7, 195; Schmidt, D.M., George, J.S., Ranken, D.M., Wood, C.C., 2001. Spatial-temporal bayesian inference for MEG/EEG. In: Nenonen, J., Ilmoniemi, R. J., Katila, T. (Eds.), Biomag 2000: 12th International Conference on Biomagnetism. Espoo, Norway, p. 671]. Schmidt et al. (1999) focused on the analysis of data at a single point in time employing an extended region source model. They subsequently extended their work to a spatiotemporal Bayesian inference analysis of the full spatiotemporal MEG/EEG data set. Here, we formulate spatiotemporal Bayesian inference analysis using a multi-dipole model of neural activity. This approach is faster than the extended region model, does not require use of the subject's anatomical information, does not require prior determination of the number of dipoles, and yields quantitative probabilistic inferences. In addition, we have incorporated the ability to handle much more complex and realistic estimates of the background noise, which may be represented as a sum of Kronecker products of temporal and spatial noise covariance components. This reduces the effects of undermodeling noise. In order to reduce the rigidity of the multi-dipole formulation which commonly causes problems due to multiple local minima, we treat the given covariance of the background as uncertain and marginalize over it in the analysis. Markov Chain Monte Carlo (MCMC) was used to sample the many possible likely solutions. The spatiotemporal Bayesian dipole analysis is demonstrated using simulated and empirical whole-head MEG data.

Spatio-temporal mapping of rat whisker barrels with fast scattered light signals.

Optical techniques offer a number of potential advantages for imaging dynamic spatio-temporal patterns of activity in neural tissue. The methods provide the wide field of view required to image population activation across networks, while allowing resolution of the detailed structure of individual cells. Optical probes can provide high temporal resolution without penetrating the tissue surface. However, functional optical imaging has been constrained by the small size of the signals and the sluggish nature of the metabolic and hemodynamic responses that are the basis of most existing methods. Here, we employ both high-speed CCD cameras and high-sensitivity photodiodes to optimize resolution in both space and time, together with dark-field illumination in the near-infrared, to record fast intrinsic scattering signals from rat somatosensory cortex in vivo. Optical responses tracked the physiological activation of cortical columns elicited by single whisker twitches. High-speed imaging produced maps that were initially restricted in space to individual barrels, and then spread over time. Photodiode recordings disclosed 400-600 Hz oscillatory responses, tightly correlated in frequency and phase to those seen in simultaneous electrical recordings. Imaging based on fast intrinsic light scattering signals eventually could provide high resolution dynamic movies of neural networks in action.

Cross-polarized reflected light measurement of fast optical responses associated with neural activation.

We developed an optical probe for cross-polarized reflected light measurements and investigated optical signals associated with electrophysiological activation in isolated lobster nerves. The cross-polarized baseline light intensity (structural signal) and the amplitude of the transient response to stimulation (functional signal) measured in reflected mode were dependent on the orientation of the nerve axis relative to the polarization plane of incident light. The maximum structural signal and functional response amplitude were observed at 45 degrees , and the ratio of functional to structural signals was approximately constant across orientations. Functional responses were measured in single trials in both transmitted and reflected geometries and responses had similar waveforms. Both structural and functional signals were an order of magnitude smaller in reflected than in transmitted light measurements, but functional responses had similar signal/noise ratios. We propose a theoretical model based on geometrical optics that is consistent with experimental results. In the model, the cross-polarized structural signal results from light reflection from axonal fibers and the transient functional response arises from axonal swelling associated with neural activation. Polarization-sensitive reflected light measurements could greatly enhance in vivo imaging of neural activation since cross-polarized responses are much larger than scattering signals now employed for dynamic functional neuroimaging.

Rapid optical coherence tomography and recording functional scattering changes from activated frog retina.

Optical coherence tomography (OCT) has important potential advantages for fast functional neuroimaging. However, dynamic neuroimaging poses demanding requirements for fast and stable acquisition of optical scans. Optical phase modulators based on the electro-optic effect allow rapid phase modulation; however, applications to low-coherence tomography are limited by the optical dispersion of a broadband light source by the electro-optic crystal. We show that the optical dispersion can be theoretically estimated and experimentally compensated. With an electro-optic phase modulator-based, no-moving-parts OCT system, near-infrared scattering changes associated with neural activation were recorded from isolated frog retinas activated by visible light.

Correlated firing improves stimulus discrimination in a retinal model.

Synchronous firing limits the amount of information that can be extracted by averaging the firing rates of similarly tuned neurons. Here, we show that the loss of such rate-coded information due to synchronous oscillations between retinal ganglion cells can be overcome by exploiting the information encoded by the correlations themselves. Two very different models, one based on axon-mediated inhibitory feedback and the other on oscillatory common input, were used to generate artificial spike trains whose synchronous oscillations were similar to those measured experimentally. Pooled spike trains were summed into a threshold detector whose output was classified using Bayesian discrimination. For a threshold detector with short summation times, realistic oscillatory input yielded superior discrimination of stimulus intensity compared to rate-matched Poisson controls. Even for summation times too long to resolve synchronous inputs, gamma band oscillations still contributed to improved discrimination by reducing the total spike count variability, or Fano factor. In separate experiments in which neurons were synchronized in a stimulus-dependent manner without attendant oscillations, the Fano factor increased markedly with stimulus intensity, implying that stimulus-dependent oscillations can offset the increased variability due to synchrony alone.

Stimulus-specific oscillations in a retinal model.

High-frequency oscillatory potentials (HFOPs) in the vertebrate retina are stimulus specific. The phases of HFOPs recorded at any given retinal location drift randomly over time, but regions activated by the same stimulus tend to remain phase locked with approximately zero lag, whereas regions activated by spatially separate stimuli are typically uncorrelated. Based on retinal anatomy, we previously postulated that HFOPs are mediated by feedback from a class of axon-bearing amacrine cells that receive excitation from neighboring ganglion cells-via gap junctions-and make inhibitory synapses back onto the surrounding ganglion cells. Using a computer model, we show here that such circuitry can account for the stimulus specificity of HFOPs in response to both high- and low-contrast features. Phase locking between pairs of model ganglion cells did not depend critically on their separation distance, but on whether the applied stimulus created a continuous path between them. The degree of phase locking between spatially separate stimuli was reduced by lateral inhibition, which created a buffer zone around strongly activated regions. Stimulating the inhibited region between spatially separate stimuli increased their degree of phase locking proportionately. Our results suggest several experimental strategies for testing the hypothesis that stimulus-specific HFOPs arise from axon-mediated feedback in the inner retina.

A theory of the Benham Top based on center-surround interactions in the parvocellular pathway.

A model color-opponent neuron was used to investigate the subjective colors evoked by the Benham Top (BT). Color-opponent inputs from cone-selective parvocellular (P) pathway neurons with center-surround receptive fields were subtracted with a short relative delay, yielding a small transient input in response to a white spot. This transient input was amplified by BT-like stimuli, modeled as a thin dark bar followed by full-field illumination. The narrow bar produced maximal activation of the P-pathway surrounds but only partial activation of the P-pathway centers. Due to saturation, subsequent removal of the bar had little effect on the P-pathway surrounds, whereas the transient input from the P-pathway centers was amplified via disinhibition. Responses to BT-like stimuli became weaker as surround sensitivity recovered, producing an effect analogous to the progression of perceived BT colors. Our results suggest that the BT-illusion arises because cone-selective neurons convey information about both color and luminance contrast, allowing the two signals become confounded.

Simultaneous magnetoencephalography and SQUID detected nuclear MR in microtesla magnetic fields.

A system that simultaneously measures magnetoencephalography (MEG) and nuclear magnetic resonance (NMR) signals from the human brain was designed and fabricated. A superconducting quantum interference device (SQUID) sensor coupled to a gradiometer pickup coil was used to measure the NMR and MEG signals. 1H NMR spectra with typical Larmor frequencies from 100-1000 Hz acquired simultaneously with the evoked MEG response from a stimulus to the median nerve are reported. The single SQUID gradiometer was placed approximately over the somatosensory cortex of a human subject to noninvasively record the signals. These measurements demonstrate, for the first time, the feasibility of simultaneous MRI and MEG. NMR in the microtesla regime provides narrow linewidths and the potential for high spatial resolution imaging, while SQUID sensors enable direct measurement of neuronal activity with high temporal resolution via MEG.

SQUID detected NMR in microtesla magnetic fields.

We have built an NMR system that employs a superconducting quantum interference device (SQUID) detector and operates in measurement fields of 2-25 microT. The system uses a pre-polarizing field from 4 to 30 mT generated by simple room-temperature wire-wound coils that are turned off during measurements. The instrument has an open geometry with samples located outside the cryostat at room-temperature. This removes constraints on sample size and allows us to obtain signals from living tissue. We have obtained 1H NMR spectra from a variety of samples including water, mineral oil, and a live frog. We also acquired gradient encoded free induction decay (FID) data from a water-plastic phantom in the microT regime, from which simple projection images were reconstructed. NMR signals from samples inside metallic containers have also been acquired. This is possible because the penetration skin depth is much greater at the low operating frequencies of this system than for conventional systems. Advantages to ultra-low field NMR measurements include lower susceptibility artifacts caused by high strength polarizing and measurement fields, and negligible line width broadening due to measurement field inhomogeneity, reducing the burden of producing highly homogeneous fields.

Simultaneous birefringence and scattered light measurements reveal anatomical features in isolated crustacean nerve.

Simultaneous fast birefringence and scattered light changes associated with crustacean nerve activation have different time courses and are produced by separate biophysical mechanisms. Technological advances in illumination, photodiodes and amplification circuitry achieved better signal-to-noise than earlier studies revealing optical signals in axonal nerve bundles as small as crayfish ventral cord and claw. The birefringence measurements yielded signals that could be observed in single trials, with temporally separated peaks associated with axonal populations of different diameters. A slit aperture placed perpendicular to the nerve reduced the spatial-temporal integration and enhanced the temporal structure of the separate peaks in the birefringence signal. Moving the slit aperture farther from the stimulation point delayed the signal in time, and also enhanced the separation between peaks. Different propagation velocities of the separate peaks provided evidence for at least three neuronal populations in the bundle. These studies underscore the advantages of birefringence over scattering measurements. Application of birefringence methods can optimize non-invasive imaging techniques being developed to detect fast optical responses associated with electrical neural activity in humans.

Optical lever recording of displacements from activated lobster nerve bundles and Nitella internodes.

An optical lever was designed for studying physical displacements associated with electrophysiological activation of lobster nerve bundles. Stimulation current pulses generated a compound action potential volley, and upward physical displacements of <1 nm were recorded. The swelling displacement propagated in the same direction as the action potential volley, occurred simultaneously with the action potentials, and required 10 ms to relax after the electrical potential was restored. For comparison with previous reports, we also recorded the displacement of Nitella internodes associated with electrical stimulation. We found that a rapid swelling displacement (approximately 10 nm) was followed by a larger, slow-shrinking displacement (approximately 100 nm).