Front Hum Neurosci
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Chronic pain in Sickle Cell Disease (SCD) is probably related to maladaptive plasticity of brain areas involved in nociceptive processing. Transcranial Direct Current Stimulation (tDCS) and Peripheral Electrical Stimulation (PES) can modulate cortical excitability and help to control chronic pain. Studies have shown that combined use of tDCS and PES has additive effects. ⋯ The main study outcome will be pain intensity, measured by a Visual Analogue Scale. In addition, electroencephalographic power density, cortical maps of the gluteus maximus muscle elicited by Transcranial Magnetic Stimulation (TMS), serum levels of Brain-derived Neurotrophic Factor (BDNF), and Tumor Necrosis Factor (TNF) will be assessed as secondary outcomes. Data will be analyzed using ANOVA of repeated measures, controlling for confounding variables.
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Working memory (WM) is crucial for intelligent cognitive functioning, and synchronization phenomena in the fronto-parietal network have been suggested as an underlying neural mechanism. In an attempt to provide causal evidence for this assumption, we applied transcranial alternating current stimulation (tACS) at theta frequency over fronto-parietal sites during a visuospatial match-to-sample (MtS) task. Depending on the stimulation protocol, i.e., in-phase, anti-phase or sham, we anticipated a differential impact of tACS on behavioral WM performance as well as on the EEG (electroencephalography) during resting state before and after stimulation. ⋯ However, the closer participants' individual theta peak frequencies were to the stimulation frequency of 5 Hz after anti-phase tACS, the faster they responded in the MtS task. This effect did not reach statistical significance during in-phase tACS and was not present during sham. A lack of statistically significant behavioral results in the MtS task and frequency-unspecific effects on the electrophysiological level question the effectiveness of tACS in modulating cortical oscillations in a frequency-specific manner.
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Recent studies have investigated local oscillations, long-range connectivity, and global network patterns to identify neural changes associated with anesthetic-induced unconsciousness. These studies typically employ anesthetic protocols that either just cross the threshold of unconsciousness, or induce deep unconsciousness for a brief period of time-neither of which models general anesthesia for major surgery. To study neural patterns of unconsciousness and recovery in a clinically-relevant context, we used a realistic anesthetic regimen to induce and maintain unconsciousness in eight healthy participants for 3 h. ⋯ Phase-lag index did not distinguish between states of consciousness or stages of recovery. Network efficiency was significantly depressed and network clustering coefficient was significantly increased during unconsciousness; these graph theoretical measures returned to baseline during the 3 h recovery period. Posterior alpha power may be a potential biomarker for normal recovery of functional brain networks after general anesthesia.
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The aim of this study was to explore possible changes in whole brain gray matter volume (GMV) after spinal cord injury (SCI) using voxel-based morphometry (VBM), and to study their associations with the injury duration, severity, and clinical variables. In total, 21 patients with SCI (10 with complete and 11 with incomplete SCI) and 21 age- and sex-matched healthy controls (HCs) were recruited. The 3D high-resolution T1-weighted structural images of all subjects were obtained using a 3.0 Tesla MRI system. ⋯ In the sub-acute subgroup, we found a significant positive correlation between the dACC GMV and the total clinical motor scores, and a significant negative correlation between right OFC GMV and the injury duration. These findings indicate that SCI can cause remote atrophy of brain gray matter, especially in the salient network. In general, the duration and severity of SCI may be not associated with the degree of brain atrophy in total SCI patients, but there may be associations between them in subgroups.
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There is evidence that neural patterns are predictive of voluntary decisions, but findings come from paradigms that have typically required participants to make arbitrary choices decisions in highly abstract experimental tasks. It remains to be seen whether proactive neural activity reflects upcoming choices for individuals performing decisions in more complex, dynamic, scenarios. In this functional Magnetic Resonance Imaging (fMRI) study, we investigated proactive neural activity for voluntary decisions compared with instructed decisions in a virtual environment, which more closely mimicked a real-world decision. ⋯ Using multi-voxel pattern analysis (MVPA), we showed that participants' choices, which were decodable from motor and visual cortices, could be predicted with lower accuracy for voluntary decisions than for instructed decisions. This corresponded to eye-tracking data showing that participants made a greater number of fixations to alternative options during voluntary choices, which might have resulted in less stable choice representations. These findings suggest that voluntary decisions engage proactive choice selection, and that upcoming choices are encoded in neural representations even while individuals continue to consider their options in the environment.