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

Volition and Action in the Human Brain: Processes, Pathologies, and Reasons

Humans seem to decide for themselves what to do, and when to do it. This distinctive capacity may emerge from an ability, shared with other animals, to make decisions for action that are related to future goals, or at least free from the constraints of immediate environmental inputs. Studying such volitional acts proves a major challenge for neuroscience. This review highlights key mechanisms in the generation of voluntary, as opposed to stimulus-driven actions, and highlights three issues. The first part focuses on the apparent spontaneity of voluntary action. The second part focuses on one of the most distinctive, but elusive, features of volition, namely, its link to conscious experience, and reviews stimulation and patient studies of the cortical basis of conscious volition down to the single-neuron level. Finally, we consider the goal-directedness of voluntary action, and discuss how internal generation of action can be linked to goals and reasons

A Behavioral Analysis of Spatial Neglect and its Recovery After Stroke

In a longitudinal study of recovery of left neglect following stroke using reaction time computerized assessment, we find that lateralized spatial deficits of attention and perception to be more severe than disturbance of action. Perceptual-attention deficits also show the most variability in the course of recovery, making them prime candidates for intervention. In an anatomical analysis of MRI findings, ventral frontal cortex damage was correlated with the most severe neglect, reflecting impaired fronto-parietal communication

A gradient of sharpening effects by perceptual prior across the human cortical hierarchy

Prior knowledge profoundly influences perceptual processing. Previous studies have revealed consistent suppression of predicted stimulus information in sensory areas, but how prior knowledge modulates processing higher up in the cortical hierarchy remains poorly understood. In addition, the mechanism leading to suppression of predicted sensory information remains unclear, and studies thus far have revealed a mixed pattern of results in support of either the "sharpening" or "dampening" model. Here, using 7T fMRI in humans (both sexes), we observed that prior knowledge acquired from fast, one-shot perceptual learning sharpens neural representation throughout the ventral visual stream, generating suppressed sensory responses. In contrast, the frontoparietal and default mode networks exhibit similar sharpening of content-specific neural representation, but in the context of unchanged and enhanced activity magnitudes, respectively: a pattern we refer to as "selective enhancement." Together, these results reveal a heretofore unknown macroscopic gradient of prior knowledge's sharpening effect on neural representations across the cortical hierarchy

lfp_dimReduce

evoked responses and pre-stimulus activity across all channels (LFP

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

csd_allchannels

evoked responses and pre-stimulus activity across all channels (CSD

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