A Two-Stage Cascade Model of BOLD Responses in Human Visual Cortex

<div><p>Visual neuroscientists have discovered fundamental properties of neural representation through careful analysis of responses to controlled stimuli. Typically, different properties are studied and modeled separately. To integrate our knowledge, it is necessary to build general models that begin with an input image and predict responses to a wide range of stimuli. In this study, we develop a model that accepts an arbitrary band-pass grayscale image as input and predicts blood oxygenation level dependent (BOLD) responses in early visual cortex as output. The model has a cascade architecture, consisting of two stages of linear and nonlinear operations. The first stage involves well-established computations—local oriented filters and divisive normalization—whereas the second stage involves novel computations—compressive spatial summation (a form of normalization) and a variance-like nonlinearity that generates selectivity for second-order contrast. The parameters of the model, which are estimated from BOLD data, vary systematically across visual field maps: compared to primary visual cortex, extrastriate maps generally have larger receptive field size, stronger levels of normalization, and increased selectivity for second-order contrast. Our results provide insight into how stimuli are encoded and transformed in successive stages of visual processing.</p></div

A non-invasive, quantitative study of broadband spectral responses in human visual cortex

<div><p>Currently, non-invasive methods for studying the human brain do not routinely and reliably measure spike-rate-dependent signals, independent of responses such as hemodynamic coupling (fMRI) and subthreshold neuronal synchrony (oscillations and event-related potentials). In contrast, invasive methods—microelectrode recordings and electrocorticography (ECoG)—have recently measured broadband power elevation in field potentials (~50–200 Hz) as a proxy for locally averaged spike rates. Here, we sought to detect and quantify stimulus-related broadband responses using magnetoencephalography (MEG). Extracranial measurements like MEG and EEG have multiple global noise sources and relatively low signal-to-noise ratios; moreover high frequency artifacts from eye movements can be confounded with stimulus design and mistaken for signals originating from brain activity. For these reasons, we developed an automated denoising technique that helps reveal the broadband signal of interest. Subjects viewed 12-Hz contrast-reversing patterns in the left, right, or bilateral visual field. Sensor time series were separated into evoked (12-Hz amplitude) and broadband components (60–150 Hz). In all subjects, denoised broadband responses were reliably measured in sensors over occipital cortex, even in trials without microsaccades. The broadband pattern was stimulus-dependent, with greater power contralateral to the stimulus. Because we obtain reliable broadband estimates with short experiments (~20 minutes), and with sufficient signal-to-noise to distinguish responses to different stimuli, we conclude that MEG broadband signals, denoised with our method, offer a practical, non-invasive means for characterizing spike-rate-dependent neural activity for addressing scientific questions about human brain function.</p></div

Effect of denoising on broadband response.

<p>(A) The upper panel shows the power spectra from sensor 42, subject S1, averaged across 178 epochs with the both-hemifield stimulus (blue) and blank screen (gray). The left panel is prior to denoising and is identical to the inset in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0193107#pone.0193107.g004" target="_blank">Fig 4</a>, except that harmonics of stimulus-locked frequencies have been removed. The right panel is the same as the left, except after denoising. (B) The lower panel shows the distributions of the bootstrapped broadband power for the both-hemifield (blue), blank (gray), and both-hemifield minus blank (black, inset), prior to denoising (left) and after denoising (right). The SNR is defined as the mean of the difference between both-hemifield and blank epochs divided by the standard deviation across bootstraps of the difference distribution (7.7 prior to denoising, 14.0 after). The effects of denoising are to reduce the mean power, and more importantly, reduce the standard deviation across epochs. Made with function <i>nppMakeFigure6</i>.<i>m</i>.</p

Topographic map of stimulus-locked and broadband responses.

<p>Data from subject S1 (left) and averaged across subjects S1-S8 by sensor (right). The top 3 rows show data from the 3 stimulus conditions (both-, left-, and right-hemifield) compared to blank, and the lower row shows data as the left-only minus right-only conditions. The dependent variable plotted for the single subject data is the signal-to-noise ratio at each sensor, computed as the mean of the contrast (stimulus minus blank) divided by the standard deviation across bootstraps (bootstrapped over epochs). For the group data, the signal-to-noise ratio is the mean of the subject-specific SNRs at each given sensor. The same scale bar is used for all stimulus-locked plots. For the broadband plots, one scale bar is used for the first three rows, and a different scale bar with a smaller range is used for the fourth row. Made with <i>nppMakeFigure5</i>.<i>m</i>.</p

Topographic map of broadband SNR before and after denoising.

<p>Data from subject S1 (left) and averaged across subjects S1-S8 by sensor (right). The top 3 rows show data from the 3 stimulus conditions (both-, left-, and right-hemifield) and the fourth row shows the difference between the left-only and right-only conditions. The fourth row uses a different scale bar from the other 3 rows. The columns show data before and after denoising. Made with function <i>nppMakeFigure9</i>.<i>m</i>.</p

Effect of denoising on the broadband signal and noise.

<p>Noise (upper) and signal (lower) before and after denoising in each of three stimulus conditions. Plotting conventions as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0193107#pone.0193107.g007" target="_blank">Fig 7c</a>. Made with function <i>nppMakeFigure8</i>.<i>m</i>.</p

Effect of denoising on broadband SNR.

<p>(A) SNR as a function of the number of PCs projected out in subject S1 for the both-hemifield stimulus. Each line is one sensor. The solid blue line is the mean of the 10 sensors of interest, chosen as those sensors with the highest SNR, as measured either before or after denoising (see ‘Statistical comparisons’ in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0193107#sec002" target="_blank">Material and methods</a>). The dotted blue line is the mean of the 75 sensors in the noise pool, as measured either before or after denoising. (B) SNR as a function of PCs projected out in each of 8 subjects for the both-hemifield stimulus. Each line is the mean across the 10 sensors with the highest SNR in one subject. The rightmost points indicate the effect of projecting out all 75 PCs. (C) SNR before denoising (0 PCs projected out) and after denoising (10 PCs projected out) for each stimulus condition. Each line is the mean of the 10 sensors with the highest SNR for one subject in one stimulus condition. Color saturation corresponds to the subject number (highest to lowest saturation, subjects 1–8, respectively). Made with function <i>nppMakeFigure7</i>.<i>m</i>.</p

Example response to flickering both-hemifield stimulus.

<p>(A) The main panel plots the spectral power, averaged across 148 1-s epochs, during which the subject viewed either the both-hemifield stimulus (blue line) or a blank screen at mean luminance (gray line). The black dot on the schematic head indicates the location of the sensor. The peak at 12 Hz corresponds to the frequency of dartboard contrast reversals, and is a measure of the stimulus-locked component (orange arrow). (B) The lower inset zooms in on higher frequencies to emphasize the broadband component, most evident in this example data set as a spectral power elevation spanning 60 to 150 Hz. The increase in the broadband response of the stimulus condition relative to the blank condition is shown by the orange arrow. The histograms on the right show the broadband level separately for the stimulus condition (blue) and the blank condition (gray), and the difference between them (black inset), computed 1000 times by bootstrapping over epochs in the experiment. Data from subject S1. Made with function <i>nppMakeFigure4</i>.<i>m</i>.</p

Overview of experimental design.

<p>Large-field on-off stimuli were presented in 6-s blocks consisting of either both-, left-, or right-hemifield flicker, alternating with 6-s blocks of blanks (mean luminance). A run consisted of six stimulus and six baseline blocks, after which the subject had a short break. The figure shows the first half of one run. Within a run, the order of both-, left-, and right-field flickering periods was randomized. Fifteen runs were obtained per subject, so that there were 30 repetitions of each stimulus type across the 15 runs. The fixation dot is increased in size for visibility, and shown in gray scale. Actual fixation dot was 0.17 degrees in radius (6 pixels).</p

Microsaccades during experimental conditions.

<p>(A) The circular histograms show the frequency of microsaccades per 1-s epoch, binned by direction, for each of the 4 stimulus conditions (columns 1–4). The rows show data for 3 subjects. The last column shows the rate of microsaccades (per 1-s epoch) irrespective of direction, for each of the 4 stimulus conditions, bootstrapped 100 times over epochs. Arrows indicate the median rate for each condition. (B) Both-hemifield minus blank broadband SNR meshes limited to only those epochs with microsaccades (top row) or without microsaccades (bottom row). Made with function <i>nppMakeFigure14</i>.<i>m</i>.</p