NBT: NBT v0.5.0-alpha Integrating Biomarkers.

<p>Changelog (In total 45 commits and changes to 368 files since NBT v0.4.2-alpha):</p> <ul> <li>New tools for integrating biomarker.</li> <li>Correcting critical bug in eeg_eegrej (eeg_eegrej.m did not combine regions correctly - see commit log)</li> <li>New biomarker: Phase Lag Index</li> <li>New biomarker: Hjorth's parameters.</li> <li>New option to plot a grand average power spectrum</li> <li>upgraded EEGLAB to 13.3.2</li> <li>Multiple minor bug fixes.</li> </ul

The Continuous Temporal Expectancy Task results in large variation in reaction times.

<p>(A) Illustration of the CTET paradigm (adapted from [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0196907#pone.0196907.ref044" target="_blank">44</a>]) with stimuli presented for either 600 ms (900 ms experiment 2) if they were standard stimuli or for 1200 ms (1600 ms experiment 2) if they were targets. (B) Example sequence of reaction times exhibiting large variation to the 100 target images shown.</p

Weak LRTC of reaction-time series are associated with fast reaction times.

<p>The observed correlation (R² = .52, <i>p</i> = .00002), shows that better performance is associated with less variability.</p

Results PANAS (negative affect).

<p>Results PANAS (negative affect).</p

Results PANAS (positive affect).

<p>Results PANAS (positive affect).</p

More mind wandering episodes are associated with increased LRTC of response-time series.

<p>The Correlation between DFA the of the subjective rating of attention, or mind wandering episodes shows that variability increases with more mind wandering (R² = .43, <i>p</i> = .01).</p

Mood has an effect on average reaction time and reaction-time temporal structure.

<p>Participants in the negative mood condition showed worse performnce than particiapnts in the neutral (t65 = 2.07, <i>p</i> = .042) and positve mood condition (t40 = 2.39, <i>p</i> = .022). Additionally, the temporal strucutre of reaction time series differed between positive—negative (t40 = 2.53, <i>p</i> = .016), Error bars represent 95% confidence intervals.</p

A model of attention fluctuations to explain non-random fluctuations in reaction times.

<p>(A) Our model is based on the hypothesis that attention fluctuates on a spectrum from highly external to highly internal with a non-random temporal structure, shown here for a DFA exponent of 0.8. The <i>black dots</i> indicate moments that target stimuli appear in the CTET experiment, which results in (B) a reaction-time series with a similar temporal structure under the assumption that reaction times are shorter when attention is strongly focused on external as opposed to internal sources of information. (C) 1/f signal produced with simulated sampling, showed a robust estimation of underlying temporal correlation with infrequent, semi-random sampling (<i>p</i> <.00001).</p

Catecholamines alter the intrinsic variability of cortical population activity and perception

<div><p>The ascending modulatory systems of the brain stem are powerful regulators of global brain state. Disturbances of these systems are implicated in several major neuropsychiatric disorders. Yet, how these systems interact with specific neural computations in the cerebral cortex to shape perception, cognition, and behavior remains poorly understood. Here, we probed into the effect of two such systems, the catecholaminergic (dopaminergic and noradrenergic) and cholinergic systems, on an important aspect of cortical computation: its intrinsic variability. To this end, we combined placebo-controlled pharmacological intervention in humans, recordings of cortical population activity using magnetoencephalography (MEG), and psychophysical measurements of the perception of ambiguous visual input. A low-dose catecholaminergic, but not cholinergic, manipulation altered the rate of spontaneous perceptual fluctuations as well as the temporal structure of “scale-free” population activity of large swaths of the visual and parietal cortices. Computational analyses indicate that both effects were consistent with an increase in excitatory relative to inhibitory activity in the cortical areas underlying visual perceptual inference. We propose that catecholamines regulate the variability of perception and cognition through dynamically changing the cortical excitation–inhibition ratio. The combined readout of fluctuations in perception and cortical activity we established here may prove useful as an efficient and easily accessible marker of altered cortical computation in neuropsychiatric disorders.</p></div

Effects of task and pharmacological conditions on long-range temporal correlations of the amplitude envelope of 8–12-Hz MEG activity.

<p><b>(A)</b> Spatial distribution of significant differences in scaling exponent α between Task-counting and Fixation during the Placebo (left), Atomoxetine (middle), and Donepezil condition (right). <b>(B)</b> Comparison between mean scaling exponents α averaged across the entire brain (see <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2003453#sec017" target="_blank">Materials and methods</a>) during the different pharmacological conditions. <b>(C)</b> Individual subject differences in scaling exponent α between all drug conditions. <b>(D, E)</b> Spatial distribution of drug-induced changes in scaling exponents. <b>(D)</b> Atomoxetine versus placebo. <b>(E)</b> Donepezil versus placebo. Two-sided permutation tests (<i>N</i> = 28); all statistical maps: threshold at <i>p</i> = 0.05, cluster based. All drug comparisons are averaged across behavioral conditions, i.e., Fixation and Task-counting. The data can be found at <a href="https://figshare.com/articles/DFA_source_level_/5755311" target="_blank">https://figshare.com/articles/DFA_source_level_/5755311</a>. MEG, magnetoencephalographic; n.s., not significant.</p