Emergent effects of synaptic connectivity on the dynamics of global and local slow waves in a large-scale thalamocortical network model of the human brain.

Slow-wave sleep (SWS), characterized by slow oscillations (SOs, <1Hz) of alternating active and silent states in the thalamocortical network, is a primary brain state during Non-Rapid Eye Movement (NREM) sleep. In the last two decades, the traditional view of SWS as a global and uniform whole-brain state has been challenged by a growing body of evidence indicating that SO can be local and can coexist with wake-like activity. However, the mechanisms by which global and local SOs arise from micro-scale neuronal dynamics and network connectivity remain poorly understood. We developed a multi-scale, biophysically realistic human whole-brain thalamocortical network model capable of transitioning between the awake state and SWS, and we investigated the role of connectivity in the spatio-temporal dynamics of sleep SO. We found that the overall strength and a relative balance between long and short-range synaptic connections determined the network state. Importantly, for a range of synaptic strengths, the model demonstrated complex mixed SO states, where periods of synchronized global slow-wave activity were intermittent with the periods of asynchronous local slow-waves. An increase in the overall synaptic strength led to synchronized global SO, while a decrease in synaptic connectivity produced only local slow-waves that would not propagate beyond local areas. These results were compared to human data to validate probable models of biophysically realistic SO. The model producing mixed states provided the best match to the spatial coherence profile and the functional connectivity estimated from human subjects. These findings shed light on how the spatio-temporal properties of SO emerge from local and global cortical connectivity and provide a framework for further exploring the mechanisms and functions of SWS in health and disease.

Emergent effects of synaptic connectivity on the dynamics of global and local slow waves in a large-scale thalamocortical network model of the human brain.

Slow-wave sleep (SWS), characterized by slow oscillations (SO, <1Hz) of alternating active and silent states in the thalamocortical network, is a primary brain state during Non-Rapid Eye Movement (NREM) sleep. In the last two decades, the traditional view of SWS as a global and uniform whole-brain state has been challenged by a growing body of evidence indicating that SO can be local and can coexist with wake-like activity. However, the understanding of how global and local SO emerges from micro-scale neuron dynamics and network connectivity remains unclear. We developed a multi-scale, biophysically realistic human whole-brain thalamocortical network model capable of transitioning between the awake state and slow-wave sleep, and we investigated the role of connectivity in the spatio-temporal dynamics of sleep SO. We found that the overall strength and a relative balance between long and short-range synaptic connections determined the network state. Importantly, for a range of synaptic strengths, the model demonstrated complex mixed SO states, where periods of synchronized global slow-wave activity were intermittent with the periods of asynchronous local slow-waves. Increase of the overall synaptic strength led to synchronized global SO, while decrease of synaptic connectivity produced only local slow-waves that would not propagate beyond local area. These results were compared to human data to validate probable models of biophysically realistic SO. The model producing mixed states provided the best match to the spatial coherence profile and the functional connectivity estimated from human subjects. These findings shed light on how the spatio-temporal properties of SO emerge from local and global cortical connectivity and provide a framework for further exploring the mechanisms and functions of SWS in health and disease.

A Neural Marker of Speech Intention: Evidence From Contingent Negative Variation.

Purpose This study investigated whether changes in brain activity preceding spoken words can be used as a neural marker of speech intention. Specifically, changes in the contingent negative variation (CNV) were examined prior to speech production in three different study designs to determine a method that maximizes signal detection in a speaking task. Method Electroencephalography data were collected in three different protocols to elicit the CNV in a spoken word task that varied the timing and type of linguistic information. The first protocol provided participants with the word to be spoken before the instruction of whether or not to speak, the second provided both the word and the instruction to speak, and the third provided the instruction to speak before the word. Participants (N = 18) were split into three groups (one for each protocol) and were instructed to either speak (Go) or refrain from speaking (NoGo) each word according to task instructions. The CNV was measured by analyzing the difference in slope between Go and NoGo trials. Results Statistically significant effects of hemispheric laterality on the CNV slope confirm the third protocol where the participants know they will speak in advance of the word, as the paradigm that reliably elicits a CNV response related to speech intention. Conclusions The maximal CNV response when the instruction is known before the word indicates the neural processing measured in this protocol may reflect a generalized speech intention process in which the speech-language systems become prepared to speak and then execute production once the word information is provided. Further analysis of the optimal protocol identified in this study requires additional experimental investigation to confirm its role in eliciting an objective marker of speech intention. Supplemental Material https://doi.org/10.23641/asha.14111468.

Increased Levels of Protein-methionine Sulfoxide in Plasma Correlate with a Shift from a Mild Cognitive Impairment to an Alzheimer's Disease Stage.

Objective: Sporadic Alzheimer's disease (AD) is an oxidative, stress-dependent neurodegenerative disease. We investigated whether the levels of protein-methionine sulfoxide (MetO) in plasma could be a possible marker for AD in individuals with mild cognitive impariment (MCI). Design: We evaluated blood samples from patients with AD or MCI, as well as from normal controls, testing their MetO levels and superoxide dismutase (SOD) specific activity. Results: An increase of MetO levels of a particular protein of human plasma and a decrease of SOD activity were observed only in AD plasma. Conclusion: Monitoring the patterns of these plasma markers in patients with MCI could provide a warning sign for disease progression into AD.