Prism Adaptation Resting State

Code sharing for Wilf et al., Prism adaptation enhances decoupling between the default mode network and the attentional network

Higher cognitive load interferes with head-hand coordination: virtual reality-based study

Abstract Daily life activities often involve decision-based reaching movements in different contexts and circumstances. These activities span a wide array of cognitive load types we face while executing motor functions. Here we use a virtual reality-based neurocognitive testing platform to assess cognitive-induced changes in motor behavior as reflected by modulations in head-hand coordination. Our paradigm is based on the Color Trails Test (CTT), which is designed to assess two types of cognitive functions: Trails A—sustained visual attention (SVA), and Trails B—divided attention (DA). The virtual reality CTT adaptation (VR-CTT) requires execution of large multi-directional hand movements and head rotations. We employed a cross-correlation analysis on hand and head kinematics data collected from 122 healthy participants (ages: 20–90 years; divided as follows: young, middle-aged, and older adults) who completed the VR-CTT. The level of spatial coherence of head-hand movements was found to be high (R ≥ 0.76) in both Trails A and B, in all age groups. However, assessing head-hand phase shifts revealed longer time lags (i.e., in which head leads hand) in Trails B versus Trails A, in all age groups. We conclude that allocating cognitive resources to DA task reduces head-hand synchrony as compared to SVA conditions

Diminished Auditory Responses during NREM Sleep Correlate with the Hierarchy of Language Processing.

Natural sleep provides a powerful model system for studying the neuronal correlates of awareness and state changes in the human brain. To quantitatively map the nature of sleep-induced modulations in sensory responses we presented participants with auditory stimuli possessing different levels of linguistic complexity. Ten participants were scanned using functional magnetic resonance imaging (fMRI) during the waking state and after falling asleep. Sleep staging was based on heart rate measures validated independently on 20 participants using concurrent EEG and heart rate measurements and the results were confirmed using permutation analysis. Participants were exposed to three types of auditory stimuli: scrambled sounds, meaningless word sentences and comprehensible sentences. During non-rapid eye movement (NREM) sleep, we found diminishing brain activation along the hierarchy of language processing, more pronounced in higher processing regions. Specifically, the auditory thalamus showed similar activation levels during sleep and waking states, primary auditory cortex remained activated but showed a significant reduction in auditory responses during sleep, and the high order language-related representation in inferior frontal gyrus (IFG) cortex showed a complete abolishment of responses during NREM sleep. In addition to an overall activation decrease in language processing regions in superior temporal gyrus and IFG, those areas manifested a loss of semantic selectivity during NREM sleep. Our results suggest that the decreased awareness to linguistic auditory stimuli during NREM sleep is linked to diminished activity in high order processing stations

Combining Classification with fMRI-Derived Complex Network Measures for Potential Neurodiagnostics

<div><p>Complex network analysis (CNA), a subset of graph theory, is an emerging approach to the analysis of functional connectivity in the brain, allowing quantitative assessment of network properties such as functional segregation, integration, resilience, and centrality. Here, we show how a classification framework complements complex network analysis by providing an efficient and objective means of selecting the best network model characterizing given functional connectivity data. We describe a novel kernel-sum learning approach, block diagonal optimization (BDopt), which can be applied to CNA features to single out graph-theoretic characteristics and/or anatomical regions of interest underlying discrimination, while mitigating problems of multiple comparisons. As a proof of concept for the method’s applicability to future neurodiagnostics, we apply BDopt classification to two resting state fMRI data sets: a trait (between-subjects) classification of patients with schizophrenia vs. controls, and a state (within-subjects) classification of wake vs. sleep, demonstrating powerful discriminant accuracy for the proposed framework.</p></div

Cortical responses to auditory stimuli during wakefulness.

<p>Statistical parametric maps of GLM multi participant (n = 10) random effect analysis. Color coding denotes t values. <i>(a)</i> Response to scrambled sentences versus rest during wakefulness in the night session (blue shades), and in auditory localizer scans (orange shades). Note the high proportion of overlap (purple shades) <i>(b)</i> Regions showing preferred activation for comprehensible sentences over scrambled sentences in awake periods during the night session and in auditory localizer scans. HG = Heschl’s gyrus, IFG = inferior frontal gyrus, STG = superior temporal gyrus. Color bars denote the maps t values during the night (left scale) and daytime localizer (right scale) sessions.</p

Indices quantifying the effect of sleep on the different ROIs.

<p>Beta values were used to calculate the indices (n = 10). <i>(a)</i> Mean index assessing semantic selectivity during wakefulness (purple) and sleep (green), calculated by subtracting betas of scrambled from comprehensible sentences in each of the ROIs. <i>(b)</i> Median index measuring the effect of sleep on the response to comprehensible sentences, calculated by subtracting awake betas from sleep betas and dividing by awake betas for the comprehensible sentences category. Note the graded decrease in the index values moving along the hierarchy of semantic processing ROIs. sen = comprehensible sentences. Statistical specifications are the same as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0157143#pone.0157143.g004" target="_blank">Fig 4</a>.</p

ROI analysis of all three types of auditory stimuli in wakefulness and sleep during night sessions.

<p><i>(a-d left)</i> Mean beta values (n = 10) after GLM analysis for each stimulus category in wakefulness (purple) and sleep (green) in <i>(a)</i> thalamus <i>(b)</i> left Heschl’s gyrus (primary auditory cortices) <i>(c)</i> left superior-temporal gyrus (Wernicke), <i>(d</i>, <i>left 3 bars)</i> left inferior frontal gyrus (Broca), and <i>(d</i>, <i>right bar)</i> comprehensible sentences category for only low activated voxels in HG. <i>(a-d right)</i> Averaged hemodynamic response curves of percent signal change for the auditory stimuli in sleep and wakefulness across participants (awake: purple shades, sleep: green shades; see legend). Errorbars represent standard error of mean (SEM). Dashed lines denote the onset of a stimulus event. sen = comprehensible sentences, pse = pseudoword, scr = scrambled. Significance values of Tukey tests or one-sampled t-tests (corrected) are shown by asterisks, above the bars to denote differences between categories / states, and at the bottom of the bars to denote significance above baseline: * p < 0.05 ** p < 0.005 *** p < 0.0005.</p

Regions activated by the task during wakefulness and sleep.

<p>Regions activated by the task during wakefulness and sleep.</p

Subcortical responses to all auditory stimuli versus rest during wakefulness (both awake during the night and the localizer session).

<p>Statistical parametric maps of GLM multi participant (n = 10) random effect analysis. Color coding denotes t values. The map shows response to all three types of auditory stimuli during all wakefulness segments in coronal (top) and axial (bottom) slices. HG = Heschl’s gyrus.</p