Object-Spatial Behavioral Results.

<p>(<b>A</b>) Patients showed an overall impairment in object-spatial integration resulting in decreased stimulus sensitivity (d’) across all trials and conditions. (<b>B</b>) Similarly, patients showed an overall response impairment resulting in increased reaction times across all trials and conditions. Error bars indicate SEM. (*), significant difference with p = 0.0032; (**), significant difference with p<0.0005.</p

Behavioral Paradigm.

<p>(<b>A</b>) In all three conditions (early mask, delayed mask, and no mask) subjects were presented with an unidentifiable, non-verbalizable, black and white object and a gray location cue (see Materials and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0034937#s4" target="_blank">Methods</a> for details). (<b>B</b>) Schematic of the main hypothesis. In the early mask condition, the mask adds noise during the processing of the visual object and spatial cue by the non-lesioned hemisphere, reducing the fidelity of the transcallosal transfer of visual information (disconnected green/red line over visual cortex). In the delayed mask condition, however, task-relevant visual information crosses the corpus callosum before the mask appears, allowing the non-lesioned hemisphere to assist in object-spatial recognition (intact green line over visual cortex). Blue shading illustrates the location of the subjects’ lesions.</p

Preferred frequency-for-amplitude as a function of Gaussian spike width.

<p>Shaded area reflects one standard deviation of the preferred frequency across 100 iterations (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0167351#sec002" target="_blank">Materials and Methods</a>).</p

Non-Sinusoidal Activity Can Produce Cross-Frequency Coupling in Cortical Signals in the Absence of Functional Interaction between Neural Sources

<div><p>The analysis of cross-frequency coupling (CFC) has become popular in studies involving intracranial and scalp EEG recordings in humans. It has been argued that some cases where CFC is mathematically present may not reflect an interaction of two distinct yet functionally coupled neural sources with different frequencies. Here we provide two empirical examples from intracranial recordings where CFC can be shown to be driven by the shape of a periodic waveform rather than by a functional interaction between distinct sources. Using simulations, we also present a generalized and realistic scenario where such coupling may arise. This scenario, which we term waveform-dependent CFC, arises when sharp waveforms (e.g., cortical potentials) occur throughout parts of the data, in particular if they occur rhythmically. Since the waveforms contain both low- and high-frequency components, these components can be inherently phase-aligned as long as the waveforms are spaced with appropriate intervals. We submit that such behavior of the data, which seems to be present in various cortical signals, cannot be interpreted as reflecting functional modulation between distinct neural sources without additional evidence. In addition, we show that even low amplitude periodic potentials that cannot be readily observed or controlled for, are sufficient for significant CFC to occur.</p></div

Examples of CFC in eight simulated signals.

<p>The plots on the left of each column depict a trace from the original (background) signal used for the simulation (black), the added Gaussian train (red), and the compound signal after adding the potentials (blue). The blue trace is therefore the sum of the black and the red ones. Note the temporal jitter in the Gaussian periodicity (i.e. variability of the inter-peak interval). The image on the right in each column shows the comodulogram for the compound signal (blue to red color scale corresponds to 0 to 1 in all panels). Clusters of significant CFC in the comodulograms are marked with a black outline (p<0.01). Note that values in the bottom triangle of each plot are not shown as CFC is invalid for frequencies-for-amplitude lower than the frequency-for-phase. The left column shows results for EEG-based simulations with a 100-ms mean inter-peak interval; the right column shows results for pink-noise-based simulations with a 166-ms mean inter-peak interval. It can be easily seen that the periodicity determines the frequency-for-phase of the CFC (10Hz and 6Hz, respectively), as well as its harmonics. A, D, the raw background traces with no added spikes. B, F, added spikes with an amplitude of 3 STDs and a width of 10 ms (full-width at half-maximum). C, G, Same as B,F but with an amplitude of 1.5 STDs. Note the diminished magnitude of the effect. D, H, Same as B,F but with a width of 20 ms. Note the lower frequency-for-amplitude range for which CFC occurs.</p

Statistical significance of phase-amplitude and phase-phase coupling for low-amplitude transients.

<p>A, Each pixel in the image represents the average p-value, across 100 repetitions, for a phase-amplitude CFC effect driven by adding a Gaussian spike train (inter-spike interval: 100 ms) of the given amplitude and width, to a background EEG signal featuring no initial CFC. The color scale corresponds not to the actual p-value, but to n = 0.05/p, i.e. the number of Bonferroni multiple-comparisons the effect “survives”, resulting in a simple linear scale of significance. The white outline indicates the cluster of significant pixels (n> = 1). Importantly, note that significant CFC occurs even for very low-amplitude transients (under 1 standard deviation). It can also be seen that the magnitude of CFC depends on the width of the transient waveform: very narrow transients do not contain enough energy to drive the phase of the slow-frequency component, while wide transients contain a weaker high-frequency component. B, Significance of phase-phase coupling between 10Hz and 20Hz is shown for the same range of transient amplitudes and widths. Since a parametric significance test was used, p-value tended to be very small and so the logarithmic scale n = log10(0.05/p) was used (such that n = 0 corresponds to p = 0.05, n = 1 to p = 0.005, etc.). White border indicates area where p<0.05. Note that the area indicated as significant in this analysis does not fully overlap that of the phase-amplitude coupling analysis (Fig 7A).</p

Patient Demographics.

<p>Note: BA, Brodmann area; HB, hypertensive bleed.</p

Effects of Early Mask on Patient Performance.

<p>(<b>A</b>) Box plot comparing control (left) and patient (right) hemispheric cost during the mask condition (contralesional minus ipsilesional). Nine out of the ten participants show this hemispheric cost whereas control subjects show no real bias. (<b>B</b>) We confirmed group differences by way of resampling statistics (see <b><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0034937#s4" target="_blank">Methods</a></b>), which confirm that the hemispheric behavioral asymmetry is greater in patients compared to controls (z = 1.66, p = 0.049). (*), significant difference with p = 0.050.</p

Summary of Reaction Times.

<p>Note: SEM, standard error of the mean; Hemispheric differences: *<i>P</i><0.0005.</p

Patient MRIs.

<p>Lesion reconstructions are show for individual patients [n = 10], and we include a group average overlay (bottom). MRI reconstructions were obtained using MRIcro <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0034937#pone.0034937-Rorden1" target="_blank">[32]</a>. For the group average, patients with right hemisphere lesions [P01 and P07] were transcribed to the left hemisphere for display purposes. The color bar indicates the percent of patients with a lesion in a specific region. The area of greatest lesion overlap across the patients occurs in Brodmann areas 9 and 46, centered in the middle frontal gyrus.</p