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Volitional respiratory manoeuvres such as sniffing and apnoea play a key role in the active olfactory exploration of the environment. Their impairment by neurodegenerative processes could thus impair olfactory abilities with the ensuing impact on quality of life. Functional brain imaging studies have identified brain networks engaged in sniffing and voluntary apnoea, comprising the primary motor and somatosensory cortices, the insula, the anterior cingulate cortex and the amygdala. The temporal organization and the oscillatory activities of these networks are not known. To elucidate these aspects, we recorded intracranial electroencephalograms in six patients during voluntary sniffs and short apnoeas (12 s). The preparation phase of both manoeuvres involved increased alpha and theta activity in the posterior insula, amygdala and temporal regions, with a specific preparatory activity in the parahippocampus for the short apnoeas and the hippocampus for sniff. Subsequently, it narrowed to the superior and median temporal areas, immediately after the manoeuvres. During short apnoeas, a particular dynamic was observed, consisting of a rapid decline in alpha and theta activity followed by a slow recovery and increase. Volitional respiratory manoeuvres involved in olfactory control involve corticolimbic structures in both a preparatory and executive manner. Further studies are needed to determine whether diseases altering deep brain structures can disrupt these mechanisms and if such disruption contributes to the corresponding olfactory deficits. KEY POINTS: Both sniff manoeuvres and short apnoeas are associated with oscillatory activity predominantly in low-frequency bands (alpha and theta). Preparation of sniff manoeuvres and short apnoeas involve activities in low-frequency bands in the posterior insula and temporal regions that extend to amygdala during the execution of both manoeuvres. During short apnoeas, activities in low-frequency bands initially decline before continuously increasing until the apnoeas end.
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http://dx.doi.org/10.1113/JP287045 | DOI Listing |
Am J Hum Genet
September 2025
Department of Medicine, Harvard Medical School, Boston, MA, USA; CardioVascular Institute (CVI), Beth Israel Deaconess Medical Center, Boston, MA, USA; Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Bosto
Strong sex differences exist in sleep phenotypes and also cardiovascular diseases (CVDs). However, sex-specific causal effects of sleep phenotypes on CVD-related outcomes have not been thoroughly examined. Mendelian randomization (MR) analysis is a useful approach for estimating the causal effect of a risk factor on an outcome of interest when interventional studies are not available.
View Article and Find Full Text PDFZhonghua Er Bi Yan Hou Tou Jing Wai Ke Za Zhi
August 2025
Otorhinolaryngology Hospital, The First Affiliated H00ospital, Sun Yat-sen University, Guangzhou Key Laboratory of Otorhinolaryngology, Guangzhou 510080, China.
To analyze the correlation between respiratory event duration and nocturnal oxygen saturation (SpO) in adults with obstructive sleep apnea (OSA), and to explore its significance in assessing nocturnal hypoxemia and OSA severity. A prospective study was conducted on adult OSA patients diagnosed via overnight standard polysomnography (PSG) at the Department of Otolaryngology, First Affiliated Hospital of Sun Yat-sen University from June 2019 to December 2023. Data collected included demographic information, PSG reports, scale scores, and comorbidities.
View Article and Find Full Text PDFProc Jpn Acad Ser B Phys Biol Sci
August 2025
Department of Biosystems Engineering, Graduate School of Science and Engineering, Yamagata University (emeritus).
This study assessed the feasibility of unconstrained deep-learning-based sleep stage classification using cardiorespiratory and body movement activities derived from piezoelectric sensors installed under a bed mattress. Heart and respiratory rates and their respective variabilities, cardiorespiratory coupling index, and body movement were simultaneously acquired through polysomnography (PSG) for 106 untreated participants with suspected sleep apnea. We used a bidirectional long short-term memory network to predict the five sleep stages using five different input feature combinations.
View Article and Find Full Text PDFNutr Metab Cardiovasc Dis
July 2025
Division of Nephrology, Department of Internal Medicine, Koc University, School of Medicine, Istanbul, Turkey.
Background And Aims: The impact of night-eating behavior (NEB) on metabolic health remains underexplored, particularly in healthy populations. We have hypothesized that NEB adversely affects metabolic parameters, liver function, and sleep via circadian disruption and neurohormonal alterations.
Methods And Results: In this single-center crossover study, sixteen healthy adults (aged 18-35 years) with no comorbidities, no medication use, and a body mass index between 18 and 30 kg/m participated in two one-week dietary regimens: regular eating (no food after 7:30 p.
IEEE Trans Autom Sci Eng
May 2025
UF Health Sleep Center, University of Florida, FL, USA.
Sleep apnea, a prevalent sleep-related breathing disorder, often remains undiagnosed and untreated in a large patient population due to the need of extensive manual annotations on various physiological signals for clinical diagnosis. Despite the surge of interest in applying machine learning to automate apnea detection, the effectiveness of existing techniques highly relies on strongly supervised learning that requires massive finely labeled training data for sufficiently short time intervals - a requirement often unmet due to the prohibitively high cost of manual labeling in clinical practice. In this article, we incorporate clinical knowledge to establish a weakly supervised deep learning framework for automatically estimating the latent fine-grained apnea severity when only coarse-grained labels indicating apnea presence are available in the training data.
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