Leif H. Finkel

Ketamine modulates theta and gamma oscillations

Ketamine, an N-methyl-d-aspartate (NMDA) receptor glutamatergic antagonist, has been studied as a model of schizophrenia when applied in subanesthetic doses. In EEG studies, ketamine affects sensory gating and alters the oscillatory characteristics of neuronal signals in a complex manner. We investigated the effects of ketamine on in vivo recordings from the CA3 region of mouse hippocampus referenced to the ipsilateral frontal sinus using a paired-click auditory gating paradigm. One issue of particular interest was elucidating the effect of ketamine on background network activity, poststimulus evoked and induced activity. We find that ketamine attenuates the theta frequency band in both background activity and in poststimulus evoked activity. Ketamine also disrupts a late, poststimulus theta power reduction seen in control recordings. In the gamma frequency range, ketamine enhances both background and evoked power, but decreases relative induced power. These findings support a role for NMDA receptors in mediating the balance between theta and gamma responses to sensory stimuli, with possible implications for dysfunction in schizophrenia.

NMDA/AMPA Ratio Impacts State Transitions and Entrainment to Oscillations in a Computational Model of the Nucleus Accumbens Medium Spiny Projection Neuron

We describe a computational model of the principal cell in the nucleus accumbens (NAcb), the medium spiny projection (MSP) neuron. The model neuron, constructed in NEURON, includes all of the known ionic currents in these cells and receives synaptic input from simulated spike trains via NMDA, AMPA, and GABAA receptors. After tuning the model by adjusting maximal current conductances in each compartment, the model cell closely matched whole-cell recordings from an adult rat NAcb slice preparation. Synaptic inputs in the range of 1000-1300 Hz are required to maintain an “up” state in the model. Cell firing in the model required concurrent depolarization of several dendritic branches, which responded independently to afferent input. Depolarization from action potentials traveled to the tips of the dendritic branches and increased Ca2+ influx through voltage-gated Ca2+ channels. As NMDA/AMPA current ratios were increased, the membrane showed an increase in hysteresis of “up” and “down” state dwell times, but intrinsic bistability was not observed. The number of oscillatory inputs required to entrain the model cell was determined to be ∼20% of the “up” state inputs. Altering the NMDA/AMPA ratio had a profound effect on processing of afferent input, including the ability to entrain to oscillations in afferent input in the theta range (4-12 Hz). These results suggest that afferent information integration by the NAcb MSP cell may be compromised by pathology in which the NMDA current is altered or modulated, as has been proposed in both schizophrenia and addiction.

N-methyl-d-aspartic acid receptor antagonist-induced frequency oscillations in mice recreate pattern of electrophysiological deficits in schizophrenia.

INTRODUCTION Electrophysiological responses to auditory stimuli have provided a useful means of elucidating mechanisms and evaluating treatments in psychiatric disorders. Deficits in gating during paired-click tasks and lack of mismatch negativity following deviant stimuli have been well characterized in patients with schizophrenia. Recently, analyses of basal, induced, and evoked frequency oscillations have gained support as additional measures of cognitive processing in patients and animal models. The purpose of this study is to examine frequency oscillations in mice across the theta (4-7.5 Hz) and gamma (31-61 Hz) bands in the context of N-methyl-d-aspartic acid receptor (NMDAR) hypofunction and dopaminergic hyperactivity, both of which are thought to serve as pharmacological models of schizophrenia. EXPERIMENTAL PROCEDURES Electroencephalograms (EEG) were recorded from mice in five treatment groups that consisted of haloperidol, risperidone, amphetamine, ketamine, or ketamine plus haloperidol during an auditory task. Basal, induced and evoked powers in both frequencies were calculated. RESULTS Ketamine increased basal power in the gamma band and decreased the evoked power in the theta band. The increase in basal gamma was not blocked by treatment with a conventional antipsychotic. No other treatment group was able to fully reproduce this pattern in the mice. CONCLUSIONS Ketamine-induced alterations in EEG power spectra are consistent with abnormalities in the theta and gamma frequency ranges reported in patients with schizophrenia. Our findings support the hypothesis that NMDAR hypofunction contributes to the deficits in schizophrenia and that the dopaminergic pathways alone may not account for these changes.

Intermediate-level visual representations and the construction of surface perception

Visual processing has often been divided into three stagesearly, intermediate, and high level vision, which roughly correspond to the sensation, perception, and cognition of the visual world. In this paper, we present a network-based model of intermediate-level vision that focuses on how surfaces might be represented in visual cortex. We propose a mechanism for representing surfaces through the establishment of ownershipa selective binding of contours and regions. The representation of ownership provides a central locus for visual integration. Our simulations show the ability to segment real and illusory images in a manner consistent with human perception. In addition, through ownership, other processes such as depth, transparency, and surface completion can interact with one another to organize an image into a perceptual scene.

Ketamine Disrupts Theta Modulation of Gamma in a Computer Model of Hippocampus

Abnormalities in oscillations have been suggested to play a role in schizophrenia. We studied theta-modulated gamma oscillations in a computer model of hippocampal CA3 in vivo with and without simulated application of ketamine, an NMDA receptor antagonist and psychotomimetic. Networks of 1200 multicompartment neurons [pyramidal, basket, and oriens-lacunosum moleculare (OLM) cells] generated theta and gamma oscillations from intrinsic network dynamics: basket cells primarily generated gamma and amplified theta, while OLM cells strongly contributed to theta. Extrinsic medial septal inputs paced theta and amplified both theta and gamma oscillations. Exploration of NMDA receptor reduction across all location combinations demonstrated that the experimentally observed ketamine effect occurred only with isolated reduction of NMDA receptors on OLMs. In the ketamine simulations, lower OLM activity reduced theta power and disinhibited pyramidal cells, resulting in increased basket cell activation and gamma power. Our simulations predict the following: (1) ketamine increases firing rates; (2) oscillations can be generated by intrinsic hippocampal circuits; (3) medial-septum inputs pace and augment oscillations; (4) pyramidal cells lead basket cells at the gamma peak but lag at trough; (5) basket cells amplify theta rhythms; (6) ketamine alters oscillations due to primary blockade at OLM NMDA receptors; (7) ketamine alters phase relationships of cell firing; (8) ketamine reduces network responsivity to the environment; (9) ketamine effect could be reversed by providing a continuous inward current to OLM cells. We suggest that this last prediction has implications for a possible novel treatment for cognitive deficits of schizophrenia by targeting OLM cells.

Color-opponent receptive fields derived from independent component analysis of natural images

Independent Component Analysis (ICA) of images of natural scenes has been shown to generate basis functions, or filters, which resemble spatial [Bell & Sejnowski (1997). Vision Research, 37, 3327-3338; van Hateren & van der Schaaf (1998). Proceedings of the Royal Society of London B, 265, 359-366] and spatiotemporal [van Hateren & Ruderman (1998) Proceedings of the Royal Society of London B, 265, 2315-2320] receptive fields of simple cells of the striate cortex. ICA yields statistically independent components which provide for a redundancy-reduced representation of the data. Using one of several published algorithms [Lee (1998). Independent component analysis: theory and applications. Boston; Kluwer Academic], we applied linear ICA to color images of natural scenes. The resulting independent component filters (ICFs) separate into either luminance or color filters. The luminance filters are localized and oriented edge detectors as reported previously. The color filters resemble either blue-yellow or red-green double-opponent receptive fields with various orientations. An equal number of each type of filter (luminance, red-green, and blue-yellow) is obtained. Thus, ICA predicts that spatiochromatic information is coded in statistically independent luminance, blue-yellow, and red-green opponent pathways with a relatively equal representation and specific spatial profiles at the cortical level.

Characterization of the spatial-frequency spectrum in the perception of shape from texture

The major cue to shape from texture is the compression of texture as a function of surface curvature [J. Exp. Psychol. 13, 242 (1987); Vision Res. 33, 827 (1993)]. A number of computational models have been proposed in which compression is measured by detection of changes in the spatial-frequency spectrum [Comput. Graphics Image Process. 5, (1976)]. We propose that the visual system uses a strategy of characterizing the frequency spectrum by a simple set of measures and of tracking the changes in this characterization rather than determining changes in the shape of the actual spectra. Our evidence is based on a number of psychophysical demonstrations that use stimuli with specifically tailored frequency spectra, constructed from white noise filtered in the frequency domain. Our evidence suggests that the visual system determines the average peak frequency of the spectrum and uses this measure as its characterization. Changes in fp are strongly correlated with the degree of surface curvature, and, over a range of stimuli, fp takes account of the variance in local estimates of the frequency spectrum. One computes fp by determining the peak frequency at each spatial location and then averaging these frequency values over a local spatial region. We show that fp is related to the second-order moment but is more biologically plausible and shows superior ability to function in the presence of noise. As a test of this model, we have constructed a neural network architecture for computing shape from texture. Our model is limited to orthographically projected, homogeneous textures without in-surface rotation. The early stages of the model consist of multiple simple-cell units tuned to different orientations and spatial frequencies. We show that these simple cells are inadequate for the determination of compression but that the outputs of complex-cell-like units after normalization generate estimates of surface slant and tilt. The network shows qualitative agreement with human perception of shape from texture over a wide range of real and artificial stimuli.

Hyperspectral image processing using locally linear embedding

We describe a method of processing hyperspectral images of natural scenes that uses a combination of k-means clustering and locally linear embedding (LLE). The primary goal is to assist anomaly detection by preserving spectral uniqueness among the pixels. In order to reduce redundancy among the pixels, adjacent pixels which are spectrally similar are grouped using the k-means clustering algorithm. Representative pixels from each cluster are chosen and passed to the LLE algorithm, where the high dimensional spectral vectors are encoded by a low dimensional mapping. Finally, monochromatic and tri-chromatic images are constructed from the k-means cluster assignments and LLE vector mappings. The method generates images where differences in the original spectra are reflected in differences in the output vector assignments. An additional benefit of mapping to a lower dimensional space is reduced data size. When spectral irregularities are added to a patch of the hyperspectral images, again the method successfully generated color assignments that detected the changes in the spectra.

Network simulations of retinal and cortical contributions to color constancy

A biologically-based neural network simulation is used to analyze the contributions to color perception of each of several processing steps in the visual system from the retina to cortical area V4. We consider the effects on color constancy and color induction of adaptation, spectral opponency, non-linearities including saturation and rectification, and spectrally-specific long-range inhibition. This last stage is a novel mechanism based on cells which have been described in V4. The model has been tested with simulations of several well known psychophysical color constancy and color induction experiments. We conclude from these simulations the following: (1) a simple push-pull spectrally specific contrast mechanism, using large surrounds analogous to those found in V4, is very effective in producing general color constancy and color induction behavior; (2) given some spatio-temporal averaging, receptor adaptation can also produce a degree of color constancy; (3) spectrally opponent processes have spatial frequency dependent responses to color and brightness contrast which affect the contribution of the V4 mechanism to color constancy in images with nonuniform backgrounds; and (4) the effect of the V4 mechanism depends on the difference between center and surround while the effect of adaptation depends on the total sum of inputs from both center and surround and therefore the two stages cooperate to increase the range of stimulus conditions under which color constancy can be achieved.

Conventional MRI Is Inadequate to Delineate the Relationship between the Red Nucleus and Subthalamic Nucleus in Parkinson’s Disease

Background: An understanding of the relationships between the anterior commissure-posterior commissure line (AC-PC), the subthalamic nucleus (STN), and red nucleus (RN) is imperative if these structures are to be used for targeting in deep brain stimulation. Currently, these relationships are incompletely understood and difficult to assess using conventional MRI. We examined the location and relationships of the STN and the RN to the AC-PC line and to each other in order to provide a greater understanding of their utility when targeting the STN, and the consistency of these anatomic relationships when examined using conventional MRI. Methods: A total of 52 STN and RN in 26 patients with Parkinson’s disease were evaluated on T2-weighted MR images. The anterior and posterior commissures and the border coordinates of the STN and RN were derived using frame coordinates. The distances from the midcommissural point (mcp) to the centers of the STN and RN, the diameters for each nucleus, and the distances between the nuclei were calculated in the x-, y-, and z-axes. Results: The mean AC-PC length was 26.1 ± 1.3 mm. The distance from the mcp to the center of the STN was 10 ± 0.7 mm in the x-axis, 0.2 ± 0.7 mm in the y-axis, and 3.3 ± 0.9 mm in the z-axis. The distance from the mcp to the center of the RN was 4.7 ± 0.6 mm in the x-axis, –5.9 ± 1.0 mm in the y-axis, and 6.1 ± 1.3 mm in the z-axis. The distance between the STN and RN was 2.3 ± 0.7 mm in the x-axis, 2.1 ± 1.0 mm in the y-axis, and –0.2 ± 1.3 mm in the z-axis. Conclusions: Although recent studies imply that the RN can be used as a relatively consistent marker for the position of the STN, the present data suggest otherwise. These data indicate that a single targeting method may be inadequate given the resolution of conventional MRI, and that it is imperative to use multiple anatomical measurements when targeting the STN for deep brain stimulation in Parkinson’s disease.