scholarly journals Swallowing‐related neural oscillation: an intracranial EEG study

Author(s):  
Hiroaki Hashimoto ◽  
Kazutaka Takahashi ◽  
Seiji Kameda ◽  
Fumiaki Yoshida ◽  
Hitoshi Maezawa ◽  
...  
2020 ◽  
Author(s):  
Hiroaki Hashimoto ◽  
Kazutaka Takahashi ◽  
Seiji Kameda ◽  
Fumiaki Yoshida ◽  
Hitoshi Maezawa ◽  
...  

AbstractSwallowing is a unique movement due to the indispensable orchestration of voluntary and involuntary movement. The transition from voluntary to involuntary swallowing is executed on the order of milliseconds. We hypothesized that its neural mechanism is revealed by high frequency cortical activities. Eight epileptic participants fitted with intracranial electrodes over the orofacial cortex were asked to swallow a water bolus, and cortical oscillatory changes, including high γ band (75–150 Hz) and β band (13–30 Hz) were investigated at the time of mouth-opening, water-injection, and swallowing. High γ power increases associated with mouth-opening were observed in the ventrolateral prefrontal cortex with water-injection in the lateral central sulcus and with swallowing in the region along the Sylvian fissure. Mouth-opening induced a β power decrease, which continued until the completion of swallowing. The high γ burst activity was focal and specific to swallowing, however, the β activities were extensive and not specific to swallowing. At the boundary time between voluntary and involuntary swallowing, swallowing-related high γ power achieved the peak, and subsequently, the power decreased. We demonstrated three distinct activities related to mouth-opening, water-injection, and swallowing induced at different timings, using high γ activities. The peak of high γ power related to swallowing suggests that during voluntary swallowing phases, the cortex is the main driving force for swallowing rather than the brain stem.


2008 ◽  
Vol 25 (6) ◽  
pp. 331-339 ◽  
Author(s):  
Marta Santiuste ◽  
Rafal Nowak ◽  
Antonio Russi ◽  
Thais Tarancon ◽  
Bartolome Oliver ◽  
...  

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Camille Fauchon ◽  
David Meunier ◽  
Isabelle Faillenot ◽  
Florence B Pomares ◽  
Hélène Bastuji ◽  
...  

Abstract Intracranial EEG (iEEG) studies have suggested that the conscious perception of pain builds up from successive contributions of brain networks in less than 1 s. However, the functional organization of cortico-subcortical connections at the multisecond time scale, and its accordance with iEEG models, remains unknown. Here, we used graph theory with modular analysis of fMRI data from 60 healthy participants experiencing noxious heat stimuli, of whom 36 also received audio stimulation. Brain connectivity during pain was organized in four modules matching those identified through iEEG, namely: 1) sensorimotor (SM), 2) medial fronto-cingulo-parietal (default mode-like), 3) posterior parietal-latero-frontal (central executive-like), and 4) amygdalo-hippocampal (limbic). Intrinsic overlaps existed between the pain and audio conditions in high-order areas, but also pain-specific higher small-worldness and connectivity within the sensorimotor module. Neocortical modules were interrelated via “connector hubs” in dorsolateral frontal, posterior parietal, and anterior insular cortices, the antero-insular connector being most predominant during pain. These findings provide a mechanistic picture of the brain networks architecture and support fractal-like similarities between the micro-and macrotemporal dynamics associated with pain. The anterior insula appears to play an essential role in information integration, possibly by determining priorities for the processing of information and subsequent entrance into other points of the brain connectome.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jan Pyrzowski ◽  
Jean- Eudes Le Douget ◽  
Amal Fouad ◽  
Mariusz Siemiński ◽  
Joanna Jędrzejczak ◽  
...  

AbstractClinical diagnosis of epilepsy depends heavily on the detection of interictal epileptiform discharges (IEDs) from scalp electroencephalographic (EEG) signals, which by purely visual means is far from straightforward. Here, we introduce a simple signal analysis procedure based on scalp EEG zero-crossing patterns which can extract the spatiotemporal structure of scalp voltage fluctuations. We analyzed simultaneous scalp and intracranial EEG recordings from patients with pharmacoresistant temporal lobe epilepsy. Our data show that a large proportion of intracranial IEDs manifest only as subtle, low-amplitude waveforms below scalp EEG background and could, therefore, not be detected visually. We found that scalp zero-crossing patterns allow detection of these intracranial IEDs on a single-trial level with millisecond temporal precision and including some mesial temporal discharges that do not propagate to the neocortex. Applied to an independent dataset, our method discriminated accurately between patients with epilepsy and normal subjects, confirming its practical applicability.


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