Average is optimal: An inverted-U relationship between trial-to-trial brain activity and behavioral performance

It is well known that even under identical task conditions, there is a tremendous amount of trial-to-trial variability in both brain activity and behavioral output. Thus far the vast majority of event-related potential (ERP) studies investigating the relationship between trial-to-trial fluctuations in brain activity and behavioral performance have only tested a monotonic relationship between them. However, it was recently found that across-trial variability can correlate with behavioral performance independent of trial-averaged activity. This finding predicts a U- or inverted-U- shaped relationship between trial-to-trial brain activity and behavioral output, depending on whether larger brain variability is associated with better or worse behavior, respectively. Using a visual stimulus detection task, we provide evidence from human electrocorticography (ECoG) for an inverted-U brain-behavior relationship: When the raw fluctuation in broadband ECoG activity is closer to the across-trial mean, hit rate is higher and reaction times faster. Importantly, we show that this relationship is present not only in the post-stimulus task-evoked brain activity, but also in the pre-stimulus spontaneous brain activity, suggesting anticipatory brain dynamics. Our findings are consistent with the presence of stochastic noise in the brain. They further support attractor network theories, which postulate that the brain settles into a more confined state space under task performance, and proximity to the targeted trajectory is associated with better performance

Characterization of Scale-Free Properties of Human Electrocorticography in Awake and Slow Wave Sleep States

Like many complex dynamic systems, the brain exhibits scale-free dynamics that follow power-law scaling. Broadband power spectral density (PSD) of brain electrical activity exhibits state-dependent power-law scaling with a log frequency exponent that varies across frequency ranges. Widely divergent naturally occurring neural states, awake and slow wave sleep (SWS), were used to evaluate the nature of changes in scale-free indices of brain electrical activity. We demonstrate two analytic approaches to characterizing electrocorticographic (ECoG) data obtained during awake and SWS states. A data-driven approach was used, characterizing all available frequency ranges. Using an equal error state discriminator (EESD), a single frequency range did not best characterize state across data from all six subjects, though the ability to distinguish awake and SWS ECoG data in individual subjects was excellent. Multi-segment piecewise linear fits were used to characterize scale-free slopes across the entire frequency range (0.2–200 Hz). These scale-free slopes differed between awake and SWS states across subjects, particularly at frequencies below 10 Hz and showed little difference at frequencies above 70 Hz. A multivariate maximum likelihood analysis (MMLA) method using the multi-segment slope indices successfully categorized ECoG data in most subjects, though individual variation was seen. In exploring the differences between awake and SWS ECoG data, these analytic techniques show that no change in a single frequency range best characterizes differences between these two divergent biological states. With increasing computational tractability, the use of scale-free slope values to characterize ECoG and EEG data will have practical value in clinical and research studies

A comparison of resting state functional magnetic resonance imaging to invasive electrocortical stimulation for sensorimotor mapping in pediatric patients

Localizing neurologic function within the brain remains a significant challenge in clinical neurosurgery. Invasive mapping with direct electrocortical stimulation currently is the clinical gold standard but is impractical in young or cognitively delayed patients who are unable to reliably perform tasks. Resting state functional magnetic resonance imaging non-invasively identifies resting state networks without the need for task performance, hence, is well suited to pediatric patients. We compared sensorimotor network localization by resting state fMRI to cortical stimulation sensory and motor mapping in 16 pediatric patients aged 3.1 to 18.6 years. All had medically refractory epilepsy that required invasive electrographic monitoring and stimulation mapping. The resting state fMRI data were analyzed using a previously trained machine learning classifier that has previously been evaluated in adults. We report comparable functional localization by resting state fMRI compared to stimulation mapping. These results provide strong evidence for the utility of resting state functional imaging in the localization of sensorimotor cortex across a wide range of pediatric patients

Reduction of trial-to-trial variability following stimulus onset.

<p>(<b>A</b>) Averaged ERPs (top) and trial-to-trial variability time courses (bottom) for all 24 Laplacian electrodes from Pt #4 (contralateral data). The variability time course was computed as standard deviation (s.d.) across trials, normalized to the mean of the pre-stimulus period (−500∼0 ms) and expressed in %change unit. Thick black traces denote the average across 24 electrodes. (<b>B</b>) Top: Trial-to-trial variability time course averaged across all 153 Laplacian electrodes in five subjects. Dashed lines depict mean±SEM. Bottom: Significance of the variability time course, assessed by a one-sample t-test across 153 electrodes against the null hypothesis of no change from baseline. The left column is obtained using contralateral data, and the right column using ipsilateral data. Red dashed lines indicate significance level of P = 0.001.</p

Analysis combining all electrodes (contralateral data).

<p>(<b>A</b>) Top: For each subject, <i>D(t)</i> was computed by combining across all significant electrodes in the electrode-based analysis (orange and white electrodes in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003348#pcbi-1003348-g005" target="_blank">Fig. 5A</a>), and then averaged for hit and miss trials separately. Flanking dashed lines depict mean±SEM. Red dots: P<0.005 for hit vs. miss trials, two-sample t-test. Bottom: the P-value time course of the two-sample t-test comparing <i>D(t)</i> between hit and miss trials. Dashed red line indicates significance level of P = 0.005. (<b>B</b>) As in (A), except that <i>D(t)</i> was computed by combining across all remaining non-significant electrodes not included in (A). (<b>C</b>) As in (A), except that <i>D(t)</i> was computed by combining across all electrodes. For (A–C), all subjects except Pt #3 were included, because Pt #3 did not have any electrode showing a significant quadratic ECoG-hit rate relationship (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003348#pcbi-1003348-g005" target="_blank">Fig. 5A</a>). (<b>D</b>) Pearson correlation coefficient between <i>D(t)</i> and RT across all hit trials (pooled across all five subjects). <i>D(t)</i> was combined across all significant electrodes from the electrode-based analysis (orange/white/yellow electrodes in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003348#pcbi-1003348-g006" target="_blank">Fig. 6B</a>) (red line), all remaining non-significant electrodes (blue line) and all (black line) electrodes. Dots at the bottom: P<0.005 for significant <i>D(t)</i>-RT correlation, with <i>D(t)</i> computed using all significant (red), all non-significant (blue) or all (black) electrodes. Vertical dashed line indicates the time of median RT across all subjects.</p

Inverted-U relationship between ECoG activity and response speed in an example electrode (from Pt #3, contralateral data).

<p>(<b>A</b>) Averaged ERP (green) and across-trial variability (blue) time courses. Green and blue dots indicate P<0.005 compared to the pre-stimulus period for ERP and variability respectively. (<b>B</b>) Across-trial variability time courses for fast and slow trials separately. Fast and slow trials were defined by a median-split on RT across all hit trials (N = 195). Red circles: P<0.005 for variability between fast and slow trials (two-sample F-test for variance). (<b>C</b>) Scatter plot of ECoG signal value at 447 ms against RT across all hit trials. Blue line indicates the best-fit quadratic function (P = 0.0001).</p

PCA test of the Inverted-U hypothesis using contralateral data.

<p>(<b>A</b>) <i>D(t)</i> combined across the first one (top), three (middle) and five (bottom) PCs in each subject, averaged for hit and miss trials separately (data from all subjects were included). Flanking dashed lines depict mean±SEM. Red dots: P<0.005 for hit vs. miss trials, two-sample t-test. Vertical dashed line indicates stimulus onset. (<b>B</b>) Time courses of Pearson correlation coefficient between <i>D(t)</i> and RT across all hit trials (including data from all subjects). <i>D(t)</i> was combined across the first one (top), three (middle) and five (bottom) PCs in each subject. Red dots: P<0.005 for significant <i>D(t)</i>-RT correlation. Vertical dashed line indicates the time of median RT across all subjects.</p

Inverted-U relationship between ECoG activity (the first PC) and hit rate.

<p>(<b>A–C</b>) Results from the first PC in Pt #1 (contralateral data). (<b>A</b>) Left: Averaged ERP across all trials. Right: Averaged ERP for hit and miss trials separately. (<b>B</b>) Left: Across-trial variability time course; red dots: P<0.005 (F-test, compared against pre-stimulus period). Right: Across-trial variability time course (normalized to the pre-stimulus mean computed across all trials) for hit (N = 83) and miss (N = 66) trials separately; red dots: P<0.005 (F-test, hit vs. miss trials). (<b>C</b>) Left: Hit rate as a function of raw ECoG activity at stimulus onset. Middle: Hit rate as a function of rectified ECoG signal amplitude at stimulus onset. Right: ECoG signal amplitude at stimulus onset for hit vs. miss trials (P = 0.007, Wilcoxon rank-sum test). Red line and the edges of the box denote median, 25^th and 75^th percentiles respectively. The whiskers extend to the range for data not considered outliers and the crosses indicate the outliers. (<b>D</b>) Hit rate as a function of ECoG activity (from the first PC) at stimulus onset, averaged across Patients #2–5 (contralateral data). (<b>E</b>) ECoG signal amplitude (from the first PC in each subject, contralateral data) at stimulus onset for hit vs. miss trials (P = 0.008, Wilcoxon rank-sum test). Data were pooled across all five subjects. (<b>F</b>) Same as <b>E</b>, except using ipsilateral data across 5 subjects. Hit vs. miss: P = 0.062 (Wilcoxon rank-sum test).</p

PCA test of the Inverted-U hypothesis using ipsilateral data.

<p>(<b>A</b> and <b>B</b>) same as in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003348#pcbi-1003348-g004" target="_blank">Figure 4</a>, except results were obtained using ipsilateral data.</p

Task design, behavioral data and electrode coverage.

<p>(<b>A</b>) The distribution of inter-trial intervals (ITIs) in one task block containing 50 trials. This distribution is identical across blocks. (<b>B</b>) A scatter plot of reaction times (RT) against ITI across all hit trials in all subjects over contralateral blocks. There was no dependence of RT on ITI (P>0.1, Spearman rank correlation). The red line indicates the best linear regression fit. (<b>C</b>) Electrode locations in each subject overlaid on the pial surface reconstructed from the subject's own anatomical MRI. All intracranial electrodes are shown, including electrodes excluded due to signal quality issues or from the Laplacian montage derivation (those on the electrode strips or on the edge of the grid). For Pt #3, the clinical CT scan was not acquired, thus electrode locations could not be determined in relation to the MRI and the presurgical planning diagram is shown instead.</p