A functional model for primary visual cortex

Many neurons in mammalian primary visual cortex have properties such as sharp tuning for contour orientation, strong selectivity for motion direction, and insensitivity to stimulus polarity, that are not shared with their sub-cortical counterparts. Successful models have been developed for a number of these properties but in one case, direction selectivity, there is no consensus about underlying mechanisms. This thesis describes a model that accounts for many of the empirical observations concerning direction selectivity. The model comprises a single column of cat primary visual cortex and a series of processing stages. Each neuron in the first cortical stage receives input from a small number of on-centre and off-centre relay cells in the lateral geniculate nucleus. Consistent with recent physiological evidence, the off-centre inputs to cortex precede the on-centre inputs by a small interval (~4 ms), and it is this difference that confers direction selectivity on model neurons. I show that the resulting model successfully matches the following empirical data: the proportion of cells that are direction selective; tilted spatiotemporal receptive fields; phase advance in the response to a stationary contrast-reversing grating stepped across the receptive field. The model also accounts for several other fundamental properties. Receptive fields have elongated subregions, orientation selectivity is strong, and the distribution of orientation tuning bandwidth across neurons is similar to that seen in the laboratory. Finally, neurons in the first stage have properties corresponding to simple cells, and more complex-like cells emerge in later stages. The results therefore show that a simple feed-forward model can account for a number of the fundamental properties of primary visual cortex

A Multi-Stage Model for Fundamental Functional Properties in Primary Visual Cortex

Many neurons in mammalian primary visual cortex have properties such as sharp tuning for contour orientation, strong selectivity for motion direction, and insensitivity to stimulus polarity, that are not shared with their sub-cortical counterparts. Successful models have been developed for a number of these properties but in one case, direction selectivity, there is no consensus about underlying mechanisms. We here define a model that accounts for many of the empirical observations concerning direction selectivity. The model describes a single column of cat primary visual cortex and comprises a series of processing stages. Each neuron in the first cortical stage receives input from a small number of on-centre and off-centre relay cells in the lateral geniculate nucleus. Consistent with recent physiological evidence, the off-centre inputs to cortex precede the on-centre inputs by a small (∼4 ms) interval, and it is this difference that confers direction selectivity on model neurons. We show that the resulting model successfully matches the following empirical data: the proportion of cells that are direction selective; tilted spatiotemporal receptive fields; phase advance in the response to a stationary contrast-reversing grating stepped across the receptive field. The model also accounts for several other fundamental properties. Receptive fields have elongated subregions, orientation selectivity is strong, and the distribution of orientation tuning bandwidth across neurons is similar to that seen in the laboratory. Finally, neurons in the first stage have properties corresponding to simple cells, and more complex-like cells emerge in later stages. The results therefore show that a simple feed-forward model can account for a number of the fundamental properties of primary visual cortex

Ovarian vein thrombosis after coronavirus disease (COVID-19) infection in a pregnant woman: case report

Corona virus outbreak started in December 2019, and the disease has been defined by the World Health Organization as a public health emergency. Coronavirus is a source of deep venous thrombosis (DVT) due to complications such as over-coagulation, blood stasis, and endothelial damage. In this study, we report a 26-year-old pregnant woman with coronavirus who was hospitalized with a right ovarian vein thrombosis at Besat Hospital in Sanandaj. Risk classification for deep vein thrombosis (DVT) disease is of crucial importance for the forecast of coronavirus

Absence of Infrarenal Portion of the Inferior Vena Cava With Acute Lower Extremities Venous Thrombosis: A Case Report

A lack of congenital Inferior Vena Cava (IVC) is an uncommon malformation that has been identified in combination with idiopathic Deep Venous Thrombosis (DVT), exclusively. It may not even be revealed during the lifetime. A 63-year-old female was accepted with three months of abdominal and pelvic pain and localized edema on the right flank. During this admission, she was examined and recognized for deep vein thrombosis (DVT). Ct scan images showed a lack of the Inferior Vena Cava with enormous thrombosis collaterals of the superficial vein in the abdomen. In this case report, we report a woman with side pain who has an absence of the IVC.

Approximating cortical responses with derivatives.

<p><b>a.</b> The receptive field spatial profiles for the two sub-cortical channels in the basic model are shown on the left. The distance between peaks is set equal to the distance between neighbouring on- and off-centre X-type ganglion cells, and the off-centre signal is inverted. The graph on the right shows the sum of the two sub-cortical profiles and the spatial derivative of one of them (shifted so that the zero-crossing is centred). The sum and derivative are indistinguishable. The response of the centrally located neuron in cortical stage 1 of the basic model is also shown. It was calculated with the same bar stimulus used in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0034466#pone-0034466-g005" target="_blank">Figure 5<i>a</i></a>, and the response is the generator potential 70 ms after bar onset. There is a good match between all three curves. <b>b.</b> The time courses on the left are impulse responses for the on- and off-centre geniculate cells in the basic model, with the off-centre curve inverted for ease of comparison. The sum of the on- and off-centre responses is shown in black on the right, along with the derivative of one of the responses (computed with the mean of the on- and off-centre time constants); the sum and derivative are indistinguishable. Also shown, in blue, is the time course of the membrane potential in the first-stage cortical cell at the middle of the receptive field patch. Its impulse response was calculated by delivering a very brief bar of light (width = 0.25°) at the middle of the patch. The black lines give the synaptic drive to the cortical cell and the blue line is relatively delayed because the cortical cell acts as a low-pass filter.</p

Direction selectivity: drifting gratings.

<p><b>a.</b> Gratings with optimal orientation and spatial frequency were drifted across the visual field in both the preferred and anti-preferred directions. Geniculate generator potential in the on- and off-centre neurons is shown, along with their sum. Only time varying signals are shown and the off-centre signal is inverted, for ease of comparison. The on- and off-signals are closer in the anti-preferred case, resulting in a smaller sum. <b>b.</b> Generator potential and impulse rate are shown for the centrally located neuron in cortical stage 1 on the left and right, respectively. After thresholding, the anti-preferred response is much smaller than the preferred. <b>c.</b> Population responses in the model were compared with empirical responses by computing the direction selectivity index. Indices obtained from the generator potential are shown on the left, and are compared with the empirical data in Figure 9 of Jagadeesh et al. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0034466#pone.0034466-Jagadeesh1" target="_blank">[32]</a>. These authors calculated the index as where <i>a_pref</i> and <i>a_anti</i> are the fundamental Fourier amplitudes for the preferred and anti-preferred directions, respectively; we follow suit. Indices obtained from impulse rate are shown on the right, and compared with those of Peterson et al. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0034466#pone.0034466-Peterson1" target="_blank">[3]</a>, who used . The basic model is represented by the blue vertical arrows: all neurons fall into the same histogram bin, to the right of the empirical data. To improve the match, the model was rerun with a range of delays between the on- and off-channels. The resulting histograms, shown in green, are closer to their empirical counterparts.</p

Complex-like responses.

<p><b>a.</b> A grating of optimal orientation and spatial frequency, and a contrast of 0.25, was drifted across the receptive field patch. Impulse rate was computed for the centrally located neuron in cortical stages 1 and 3. Response measures were chosen to match those of Dean and Tolhurst <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0034466#pone.0034466-Dean1" target="_blank">[4]</a> whose measurements from a simple cell and complex cell are shown at right (reprinted by permission from John Wiley and Sons Ltd.). The reduced impulse rate modulation in the stage 3 cell is due to rectification in previous stages, and static depolarisation. <b>b.</b> For each cell in their sample, Dean and Tolhurst calculated a modulation ratio equal to the Fourier fundamental amplitude of impulse rate divided by the mean rate. Their frequency histogram is shown in black. We have calculated the same ratio across all active cells in all three cortical stages of the basic model, and the resulting histogram is shown in blue. Stage 1 contributes the peak on the right and stages 2 and 3 together give the central peak. As in the laboratory, complex-like cells have a modulation rate close to or less than 1. A closer match between model and laboratory was obtained by allowing rectified geniculate impulse rates, as shown by the green histogram.</p

Responses to spots, and receptive field maps.

<p><b>a.</b> The grey square represents the simulated (2°×2°) patch of visual field, and the <i>plus</i> and <i>minus</i> signs indicate the receptive field centres of the on- and off-centre channels, respectively (though not to scale). The white spot in the visual field represents a light square with a side length of 0.38° and with its centre 0.2° from the middle of the visual field patch, and the rectangular waveform at left indicates its time course. The graphs on the left and right show responses to this spot for on- and off-centre cells, respectively. All eight sub-cortical neurons are represented; only time-varying responses are shown, and the photoreceptor response on the left is inverted for easy comparison with the other traces. Time courses in the later sub-cortical stages are delayed relative to earlier stages because of the low-pass filtering action of all neurons. <b>b.</b> The resulting generator potential and impulse rate in the centrally located neuron of cortical stage 1 are shown on the left and right, respectively. <b>c.</b> This shows the receptive field of the model neuron centrally located in the first cortical stage. To produce it we followed the methods of Martinez et al. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0034466#pone.0034466-Martinez1" target="_blank">[29]</a>. Light squares with a side of 0.38° and a duration of 40 ms were presented at the nodes of a grid spanning the visual field patch, and impulse rate was calculated at 85 ms after stimulus onset. Red contours connect these responses, and blue contours connect the responses to dark spots. The colour bar at the right of the visual field gives the peak responses to the two spot polarities. <b>d.</b> The receptive field of the centrally located neuron in cortical stage 3 computed by the same method as for the stage 1 cell. <b>e.</b> Simple and complex cell receptive fields measured by Martinez et al., and reprinted by permission from Macmillan Publishers Ltd. The on- and off-subfields for the complex cell are spatially coincident (they are separated here for ease of comparison). <b>f.</b> Unlike the simple cell, the receptive field shown in part <i>c</i> shows little elongation. We rectified this fault by adding four more sub-cortical channels, as shown in the accompanying visual field map. Spot width here is 0.8°.</p

Direction selectivity: stationary stimuli.

<p><b>a.</b> The spatiotemporal receptive field was calculated for the centrally located neuron in cortical stage 1 by presenting narrow bars of light and dark at a variety of locations, as illustrated in the visual field maps. Bars were 0.25° wide and were presented at 16 locations evenly distributed across the visual field patch. Bar duration was 40 ms. Contours connect responses to stimuli of the same polarity. The methods were chosen to match those used by DeAngelis et al. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0034466#pone.0034466-DeAngelis1" target="_blank">[33]</a>, whose results are shown at right (reprinted by permission from The American Physiological Society). The model produces slanted contours, as in the empirical data; the six-channel model was used because it yields elongated contours. <b>b.</b> The horizontal axis shows the spatial phase of a stationary grating whose contrast was varied sinusoidally in time; orientation and spatial frequency were optimal. The fundamental Fourier component in the resulting impulse rate was calculated, and its amplitude and temporal phase are shown on the left and right, respectively. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0034466#s2" target="_blank">Results</a> from the basic model, shown in blue, are compared with those from the cell in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0034466#pone-0034466-g004" target="_blank">Figure 4A and B</a> of Murthy et al. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0034466#pone.0034466-Murthy1" target="_blank">[37]</a>. Grating contrast in the model was set at 1 to obtain the best match in amplitude data. <b>c.</b> As shown in panel <i>b</i>, the model's response phase advances as the grating is shifted away from the off-centre input and towards the on-centre input. The vector diagram explains this finding. Vector length and angle give response amplitude and phase, respectively. Shifting the grating has opposite effects on the amplitude of the off- and on-centre inputs, advancing the phase of their sum. The sum represents the synaptic drive to the first-stage cortical cell at the middle of the receptive field patch, and the phase of this cell's impulse rate therefore advances as the grating shifts.</p

Vertebral Fractures Due to Metastatic Tumors: A Case Report

Metastasis is responsible for most cancer-related morbidity and mortality. In principle, metastasis is the spread of cancer from the primary site to distant tissues. Pathological consistency may be compromised during metastasis. We report the case of a 55-year-old man with MRI images of the dorsal spine showing the effects of a fifth dorsal collapse. He was referred to our hospital because of back pain, imbalance, inability to walk, and weakness of the lower limb. The histological features of bone tumors were corresponding with cell carcinoma, and bone damage was considered metastatic from a site in the lung. His general situation gently diminished, and He died during radiotherapy