Reduction in Inter-Hemispheric Connectivity in Disorders of Consciousness

Clinical diagnosis of disorders of consciousness (DOC) caused by brain injury poses great challenges since patients are often behaviorally unresponsive. A promising new approach towards objective DOC diagnosis may be offered by the analysis of ultra-slow (<0.1 Hz) spontaneous brain activity fluctuations measured with functional magnetic resonance imaging (fMRI) during the resting-state. Previous work has shown reduced functional connectivity within the “default network”, a subset of regions known to be deactivated during engaging tasks, which correlated with the degree of consciousness impairment. However, it remains unclear whether the breakdown of connectivity is restricted to the “default network”, and to what degree changes in functional connectivity can be observed at the single subject level. Here, we analyzed resting-state inter-hemispheric connectivity in three homotopic regions of interest, which could reliably be identified based on distinct anatomical landmarks, and were part of the “Extrinsic” (externally oriented, task positive) network (pre- and postcentral gyrus, and intraparietal sulcus). Resting-state fMRI data were acquired for a group of 11 healthy subjects and 8 DOC patients. At the group level, our results indicate decreased inter-hemispheric functional connectivity in subjects with impaired awareness as compared to subjects with intact awareness. Individual connectivity scores significantly correlated with the degree of consciousness. Furthermore, a single-case statistic indicated a significant deviation from the healthy sample in 5/8 patients. Importantly, of the three patients whose connectivity indices were comparable to the healthy sample, one was diagnosed as locked-in. Taken together, our results further highlight the clinical potential of resting-state connectivity analysis and might guide the way towards a connectivity measure complementing existing DOC diagnosis

Putting cognitive tasks on trial: A measure of reliability convergence

Data for the following article: A measure of reliability convergence to select and optimize cognitive tasks for individual differences research With the following associated bioarxiv paper: Putting cognitive tasks on trial: A measure of reliability convergence Jan Kadlec, Catherine Walsh, Uri Sadé, Ariel Amir, Jesse Rissman, Michal Ramot, bioRxiv 2023.07.03.547563; doi: https://doi.org/10.1101/2023.07.03.547563

A measure of reliability convergence to select and optimize cognitive tasks for individual differences research

Abstract: Surging interest in individual differences has faced setbacks in light of recent replication crises in psychology, for example in brain-wide association studies exploring brain-behavior correlations. A crucial component of replicability for individual differences studies, which is often assumed but not directly tested, is the reliability of the measures we use. Here, we evaluate the reliability of different cognitive tasks on a dataset with over 250 participants, who each completed a multi-day task battery. We show how reliability improves as a function of number of trials, and describe the convergence of the reliability curves for the different tasks, allowing us to score tasks according to their suitability for studies of individual differences. We further show the effect on reliability of measuring over multiple time points, with tasks assessing different cognitive domains being differentially affected. Data collected over more than one session may be required to achieve trait-like stability

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

Regions activated by the task during wakefulness and sleep.

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

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