163 results found
Polsek D, Cash D, Veronese M, et al., 2020, The innate immune toll-like-receptor-2 modulates the depressogenic and anorexiolytic neuroinflammatory response in obstructive sleep apnoea, SCIENTIFIC REPORTS, Vol: 10, ISSN: 2045-2322
Wimms AJ, Kelly JL, Turnbull CD, et al., 2020, Continuous positive airway pressure versus standard care for the treatment of people with mild obstructive sleep apnoea (MERGE): a multicentre, randomised controlled trial, The Lancet Respiratory Medicine, Vol: 8, Pages: 349-358, ISSN: 2213-2600
BACKGROUND: The evidence base for the treatment of mild obstructive sleep apnoea is limited and definitions of disease severity vary. The MERGE trial investigated the clinical effectiveness of continuous positive airway pressure in patients with mild obstructive sleep apnoea. METHODS: MERGE, a multicentre, parallel, randomised controlled trial enrolled patients (≥18 years to ≤80 years) with mild obstructive sleep apnoea (apnoea-hypopnoea index [AHI] ≥5 to ≤15 events per h using either AASM 2007 or AASM 2012 scoring criteria) from 11 UK sleep centres. Participants were assigned (1:1) to either 3 months of continuous positive airway pressure plus standard care (sleep counselling), or standard care alone, by computer-generated randomisation; neither participants nor researchers were blinded. The primary outcome was a change in the score on the Short Form-36 questionnaire vitality scale in the intention-to-treat population of patients with mild obstructive sleep apnoea diagnosed using the American Academy of Sleep Medicine 2012 scoring criteria. The study is registered with ClinicalTrials.gov, NCT02699463. FINDINGS: Between Nov 28, 2016 and Feb 12, 2019, 301 patients were recruited and randomised. 233 had mild obstructive sleep apnoea using AASM 2012 criteria and were included in the intention-to-treat analysis: 115 were allocated to receive continuous positive airway pressure and 118 to receive standard care. 209 (90%) of these participants completed the trial. The vitality score significantly increased with a treatment effect of a mean of 10·0 points (95% CI 7·2-12·8; p<0·0001) after 3 months of continuous positive airway pressure, compared with standard care alone (9·2 points [6·8 to 11·6] vs -0·8 points [-3·2 to 1·5]). Using the ANCOVA last-observation-carried-forward analysis, a more conservative estimate, the vitality score also significantly increased with a treatment effect of a
Nakamura T, Alqurashi Y, Morrell M, et al., 2020, Hearables: automatic overnight sleep monitoring with standardised in-ear EEG sensor, IEEE Transactions on Biomedical Engineering, Vol: 67, Pages: 203-212, ISSN: 0018-9294
Objective: Advances in sensor miniaturisation and computational power have served as enabling technologies for monitoring human physiological conditions in real-world scenarios. Sleep disruption may impact neural function, and can be a symptom of both physical and mental disorders. This study proposes wearable in-ear electroencephalography (ear- EEG) for overnight sleep monitoring as a 24/7 continuous and unobtrusive technology for sleep quality assessment in the community. Methods: Twenty-two healthy participants took part in overnight sleep monitoring with simultaneous ear-EEG and conventional full polysomnography (PSG) recordings. The ear- EEG data were analysed in the both structural complexity and spectral domains; the extracted features were used for automatic sleep stage prediction through supervised machine learning, whereby the PSG data were manually scored by a sleep clinician. Results: The agreement between automatic sleep stage prediction based on ear-EEG from a single in-ear sensor and the hypnogram based on the full PSG was 74.1% in the accuracy over five sleep stage classification; this is supported by a Substantial Agreement in the kappa metric (0.61). Conclusion: The in-ear sensor is both feasible for monitoring overnight sleep outside the sleep laboratory and mitigates technical difficulties associated with scalp-EEG. It therefore represents a 24/7 continuously wearable alternative to conventional cumbersome and expensive sleep monitoring. Significance: The ‘standardised’ one-size-fits-all viscoelastic in-ear sensor is a next generation solution to monitor sleep - this technology promises to be a viable method for readily wearable sleep monitoring in the community, a key to affordable healthcare and future eHealth.
Benjafield AV, Ayas NT, Eastwood PR, et al., 2019, Estimation of the global prevalence and burden of obstructive sleep apnoea: a literature-based analysis, The Lancet Respiratory Medicine, Vol: 7, Pages: 687-698, ISSN: 2213-2600
BackgroundThere is a scarcity of published data on the global prevalence of obstructive sleep apnoea, a disorder associated with major neurocognitive and cardiovascular sequelae. We used publicly available data and contacted key opinion leaders to estimate the global prevalence of obstructive sleep apnoea.MethodsWe searched PubMed and Embase to identify published studies reporting the prevalence of obstructive sleep apnoea based on objective testing methods. A conversion algorithm was created for studies that did not use the American Academy of Sleep Medicine (AASM) 2012 scoring criteria to identify obstructive sleep apnoea, allowing determination of an equivalent apnoea-hypopnoea index (AHI) for publications that used different criteria. The presence of symptoms was not specifically analysed because of scarce information about symptoms in the reference studies and population data. Prevalence estimates for obstructive sleep apnoea across studies using different diagnostic criteria were standardised with a newly developed algorithm. Countries without obstructive sleep apnoea prevalence data were matched to a similar country with available prevalence data; population similarity was based on the population body-mass index, race, and geographical proximity. The primary outcome was prevalence of obstructive sleep apnoea based on AASM 2012 diagnostic criteria in individuals aged 30–69 years (as this age group generally had available data in the published studies and related to information from the UN for all countries).FindingsReliable prevalence data for obstructive sleep apnoea were available for 16 countries, from 17 studies. Using AASM 2012 diagnostic criteria and AHI threshold values of five or more events per h and 15 or more events per h, we estimated that 936 million (95% CI 903–970) adults aged 30–69 years (men and women) have mild to severe obstructive sleep apnoea and 425 million (399–450) adults aged 30–69 years have moderate to s
Alqurashi YD, Nakamura T, Goverdovsky V, et al., 2018, A novel in-ear sensor to determine sleep latency during the Multiple Sleep Latency Test in healthy adults with and without sleep restriction, Nature and Science of Sleep, Vol: 10, Pages: 385-396, ISSN: 1179-1608
Objectives: Detecting sleep latency during the Multiple Sleep Latency Test (MSLT) using electroencephalogram (scalp-EEG) is time-consuming. The aim of this study was to evaluate the efficacy of a novel in-ear sensor (in-ear EEG) to detect the sleep latency, compared to scalp-EEG, during MSLT in healthy adults, with and without sleep restriction.Methods: We recruited 25 healthy adults (28.5±5.3 years) who participated in two MSLTs with simultaneous recording of scalp and in-ear EEG. Each test followed a randomly assigned sleep restriction (≤5 hours sleep) or usual night sleep (≥7 hours sleep). Reaction time and Stroop test were used to assess the functional impact of the sleep restriction. The EEGs were scored blind to the mode of measurement and study conditions, using American Academy of Sleep Medicine 2012 criteria. The Agreement between the scalp and in-ear EEG was assessed using Bland-Altman analysis.Results: Technically acceptable data were obtained from 23 adults during 69 out of 92 naps in the sleep restriction condition and 25 adults during 85 out of 100 naps in the usual night sleep. Meaningful sleep restrictions were confirmed by an increase in the reaction time (mean ± SD: 238±30 ms vs 228±27 ms; P=0.045). In the sleep restriction condition, the in-ear EEG exhibited a sensitivity of 0.93 and specificity of 0.80 for detecting sleep latency, with a substantial agreement (κ=0.71), whereas after the usual night’s sleep, the in-ear EEG exhibited a sensitivity of 0.91 and specificity of 0.89, again with a substantial agreement (κ=0.79).Conclusion: The in-ear sensor was able to detect reduced sleep latency following sleep restriction, which was sufficient to impair both the reaction time and cognitive function. Substantial agreement was observed between the scalp and in-ear EEG when measuring sleep latency. This new in-ear EEG technology is shown to have a significant value as a convenient measure for sleep lat
Morrell MJ, 2018, Controlling for Obesity in OSA: Results from Dynamic MR Imaging., Am J Respir Crit Care Med
Patel S, Kon S, Nolan C, et al., 2018, The Epworth sleepiness scale: minimum clinically important difference in obstructive sleep apnea, American Journal of Respiratory and Critical Care Medicine, Vol: 197, Pages: 961-961, ISSN: 1073-449X
Brill A-K, Pickersgill R, Moghal M, et al., 2018, Mask pressure effects on the nasal bridge during short-term noninvasive ventilation, ERJ Open Research, Vol: 4, ISSN: 2312-0541
The aim of this study was to assess the influence of different masks, ventilator settings and body positions on the pressure exerted on the nasal bridge by the mask and subjective comfort during noninvasive ventilation (NIV). We measured the pressure over the nasal bridge in 20 healthy participants receiving NIV via four different NIV masks (three oronasal masks, one nasal mask) at three different ventilator settings and in the seated or supine position. Objective pressure measurements were obtained with an I-Scan pressure-mapping system. Subjective comfort of the mask fit was assessed with a visual analogue scale. The masks exerted mean pressures between 47.6±29 mmHg and 91.9±42.4 mmHg on the nasal bridge. In the supine position, the pressure was lower in all masks (57.1±31.9 mmHg supine, 63.9±37.3 mmHg seated; p<0.001). With oronasal masks, a change of inspiratory positive airway pressure (IPAP) did not influence the objective pressure over the nasal bridge. Subjective discomfort was associated with higher IPAP and positively correlated with the pressure on the skin. Objective measurement of pressure on the skin during mask fitting might be helpful for mask selection. Mask fitting in the supine position should be considered in the clinical routine.
Polsek D, Gildeh N, Cash D, et al., 2018, Obstructive sleep apnoea and Alzheimer's disease: in search of shared pathomechanisms, Neuroscience and Biobehavioral Reviews, Vol: 86, Pages: 142-149, ISSN: 0149-7634
Alzheimer’s disease (AD) is a significant public health concern. The incidence continues to rise, and it is set to be over one million in the UK by 2025. The processes involved in the pathogenesis of AD have been shown to overlap with those found in cognitive decline in patients with Obstructive Sleep Apnoea (OSA). Currently, the standard treatment for OSA is Continuous Positive Airway Pressure. Adherence to treatment can, however, be an issue, especially in patients with dementia. Also, not all patients respond adequately, necessitating the use of additional treatments. Based on the body of data, we here suggest that excessive and prolonged neuronal activity might contribute to genesis and acceleration of both AD and OSA in the absence of appropriately structured sleep. Further, we argue that external factors, including systemic inflammation and obesity, are likely to interfere with immunological processes of the brain, and further promote disease progression. If this hypothesis is proven in future studies, it could have far-reaching clinical translational implications, as well as implications for future treatment strategies in OSA.
Malhotra A, Morrell MJ, Eastwood PR, 2018, Update in respiratory sleep disorders: Epilogue to a modern review series, RESPIROLOGY, Vol: 23, Pages: 16-17, ISSN: 1323-7799
Patrick Y, Lee A, Raha O, et al., 2017, EFFECTS OF SLEEP DEPRIVATION ON COGNITIVE AND PHYSICAL PERFORMANCE IN UNIVERSITY STUDENTS, Publisher: ELSEVIER SCIENCE BV, Pages: E182-E183, ISSN: 1389-9457
Bucks RS, Olaithe M, Rosenzweig I, et al., 2017, Reviewing the relationship between OSA and cognition: Where do we go from here?, RESPIROLOGY, Vol: 22, Pages: 1253-1261, ISSN: 1323-7799
Goverdovsky V, von Rosenberg W, Nakamura T, et al., 2017, Hearables: multimodal physiological in-ear sensing, Scientific Reports, Vol: 7, ISSN: 2045-2322
Future health systems require the means to assess and track the neural and physiological function of a user over long periods of time, and in the community. Human body responses are manifested through multiple, interacting modalities – the mechanical, electrical and chemical; yet, current physiological monitors (e.g. actigraphy, heart rate) largely lack in cross-modal ability, are inconvenient and/or stigmatizing. We address these challenges through an inconspicuous earpiece, which benefits from the relatively stable position of the ear canal with respect to vital organs. Equipped with miniature multimodal sensors, it robustly measures the brain, cardiac and respiratory functions. Comprehensive experiments validate each modality within the proposed earpiece, while its potential in wearable health monitoring is illustrated through case studies spanning these three functions. We further demonstrate how combining data from multiple sensors within such an integrated wearable device improves both the accuracy of measurements and the ability to deal with artifacts in real-world scenarios.
Nakamura T, adjei T, alqurashi Y, et al., 2017, Complexity science for sleep stage classification from EEG, IEEE International Joint Conference on Neural Networks (IJCNN) 2017, Publisher: IEEE, Pages: 4387-4394, ISSN: 2161-4407
Automatic sleep stage classification is an importantparadigm in computational intelligence and promises consider-able advantages to the health care. Most current automatedmethods require the multiple electroencephalogram (EEG) chan-nels and typically cannot distinguish the S1 sleep stage fromEEG. The aim of this study is to revisit automatic sleep stageclassification from EEGs using complexity science methods. Theproposed method applies fuzzy entropy and permutation entropyas kernels of multi-scale entropy analysis. To account for sleeptransition, the preceding and following 30 seconds of epoch datawere used for analysis as well as the current epoch. Combiningthe entropy and spectral edge frequency features extracted fromone EEG channel, a multi-class support vector machine (SVM)was able to classify 93.8% of 5 sleep stages for the SleepEDFdatabase [expanded], with the sensitivity of S1 stage was 49.1%.Also, the Kappa’s coefficient yielded 0.90, which indicates almostperfect agreement.
The monitoring of sleep patterns without patient’s inconvenience or involvement of a medical specialist is a clinical question of significant importance. To this end, we propose an automatic sleep stage monitoring system based on an affordable, unobtrusive, discreet, and long-term wearable in-ear sensor for recording the Electroencephalogram (ear-EEG). The selected features for sleep pattern classification from a single ear-EEG channel include the spectral edge frequency (SEF) and multiscale fuzzy entropy (MSFE), a structural complexity feature. In this preliminary study, the manually scored hypnograms from simultaneous scalp-EEG and ear-EEG recordings of four subjects are used as labels for two analysis scenarios: 1) classification of ear-EEG hypnogram labels from ear-EEG recordings and 2) prediction of scalp-EEG hypnogram labels from ear-EEG recordings. We consider both 2-class and 4-class sleep scoring, with the achieved accuracies ranging from 78.5% to 95.2% for ear-EEG labels predicted from ear-EEG, and 76.8% to 91.8% for scalp-EEG labels predicted from ear-EEG. The corresponding Kappa coefficients range from 0.64 to 0.83 for Scenario 1, and indicate Substantial to Almost Perfect Agreement, while for Scenario 2 the range of 0.65 to 0.80 indicates Substantial Agreement, thus further supporting the feasibility of in-ear sensing for sleep monitoring in the community.
Rosenzweig I, Morrell MJ, 2017, Hypotrophy versus Hypertrophy: It's Not Black or White with Gray Matter, AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE, Vol: 195, Pages: 1416-1418, ISSN: 1073-449X
Khazaie H, Veronese M, Noori K, et al., 2017, Functional reorganization in obstructive sleep apnoea and insomnia: A systematic review of the resting-state fMRI, NEUROSCIENCE AND BIOBEHAVIORAL REVIEWS, Vol: 77, Pages: 219-231, ISSN: 0149-7634
Brill A-K, Moghal M, Morrell MJ, et al., 2017, Randomized crossover trial of a pressure sensing visual feedback system to improve mask fitting in noninvasive ventilation., Respirology, Vol: 22, Pages: 1343-1349
BACKGROUND AND OBJECTIVE: A good mask fit, avoiding air leaks and pressure effects on the skin are key elements for a successful noninvasive ventilation (NIV). However, delivering practical training for NIV is challenging, and it takes time to build experience and competency. This study investigated whether a pressure sensing system with real-time visual feedback improved mask fitting. METHODS: During an NIV training session, 30 healthcare professionals (14 trained in mask fitting and 16 untrained) performed two mask fittings on the same healthy volunteer in a randomized order: one using standard mask-fitting procedures and one with additional visual feedback on mask pressure on the nasal bridge. Participants were required to achieve a mask fit with low mask pressure and minimal air leak (<10 L/min). Pressure exerted on the nasal bridge, perceived comfort of mask fit and staff- confidence were measured. RESULTS: Compared with standard mask fitting, a lower pressure was exerted on the nasal bridge using the feedback system (71.1 ± 17.6 mm Hg vs 63.2 ± 14.6 mm Hg, P < 0.001). Both untrained and trained healthcare professionals were able to reduce the pressure on the nasal bridge (74.5 ± 21.2 mm Hg vs 66.1 ± 17.4 mm Hg, P = 0.023 and 67 ± 12.1 mm Hg vs 60 ± 10.6 mm Hg, P = 0.002, respectively) using the feedback system and self-rated confidence increased in the untrained group. CONCLUSION: Real-time visual feedback using pressure sensing technology supported healthcare professionals during mask-fitting training, resulted in a lower pressure on the skin and better mask fit for the volunteer, with increased staff confidence.
Patrick Y, Lee A, Raha O, et al., 2017, Effects of sleep deprivation on cognitive and physical performance in university students, Sleep and Biological Rhythms, Vol: 15, Pages: 217-225, ISSN: 1446-9235
Sleep deprivation is common among university students, and has been associated with poor academic performance and physical dysfunction. However, current literature has a narrow focus in regard to domains tested, this study aimed to investigate the effects of a night of sleep deprivation on cognitive and physical performance in students. A randomized controlled crossover study was carried out with 64 participants [58% male (n = 37); 22 ± 4 years old (mean ± SD)]. Participants were randomized into two conditions: normal sleep or one night sleep deprivation. Sleep deprivation was monitored using an online time-stamped questionnaire at 45 min intervals, completed in the participants’ homes. The outcomes were cognitive: working memory (Simon game© derivative), executive function (Stroop test); and physical: reaction time (ruler drop testing), lung function (spirometry), rate of perceived exertion, heart rate, and blood pressure during submaximal cardiopulmonary exercise testing. Data were analysed using paired two-tailed T tests and MANOVA. Reaction time and systolic blood pressure post-exercise were significantly increased following sleep deprivation (mean ± SD change: reaction time: 0.15 ± 0.04 s, p = 0.003; systolic BP: 6 ± 17 mmHg, p = 0.012). No significant differences were found in other variables. Reaction time and vascular response to exercise were significantly affected by sleep deprivation in university students, whilst other cognitive and cardiopulmonary measures showed no significant changes. These findings indicate that acute sleep deprivation can have an impact on physical but not cognitive ability in young healthy university students. Further research is needed to identify mechanisms of change and the impact of longer term sleep deprivation in this population.
Atalla A, Carlisle TW, Simonds AK, et al., 2017, Sleepiness and activity in heart failure patients with reduced ejection fraction and central sleep-disordered breathing, SLEEP MEDICINE, Vol: 34, Pages: 217-223, ISSN: 1389-9457
Carlisle T, Ward NR, Atalla A, et al., 2017, Investigation of the link between fluid shift and airway collapsibility as a mechanism for obstructive sleep apnea in congestive heart failure, Physiological Reports, Vol: 5, ISSN: 2051-817X
The increased prevalence of obstructive sleep apnea (OSA) in congestive heart failure (CHF) may be associated with rostral fluid shift. We investigated the effect of overnight rostral fluid shift on pharyngeal collapsibility (Pcrit), pharyngeal caliber (APmean), and apnea‐hypopnea index (AHI) in CHF patients. Twenty‐three optimally treated systolic CHF patients were studied. Neck circumference was measured immediately prior to sleep in the evening and immediately after waking in the morning as a marker of rostral fluid shift. Pcrit was measured during sleep, early and late in the night. APmean was measured using acoustic reflection at the same times as neck circumference measurements. 15/23 CHF patients experienced an overnight increase in neck circumference; overall neck circumference significantly increased overnight (mean±SD, evening: 41.7 ± 3.2 cm; morning: 42.3 ± 3.1 cm; P = 0.03). Pcrit increased significantly overnight (early‐night: −3.8 ± 3.3 cmH2O; late‐night: −2.6 ± 3.0 cmH2O; P = 0.03) and APmean decreased (evening: 4.2 ± 1.3 cm2; morning: 3.7 ± 1.3 cm2; P = 0.006). The total AHI correlated with neck circumference (r = 0.4; P = 0.04) and Pcrit (r = 0.5; P = 0.01). APmean correlated with neck circumference (r = −0.47; P = 0.02). There was no significant change in AHI between the first and second half of the night (first‐half: 12.9 ± 12.4/h; second‐half: 13.7 ± 13.3/h; P = 0.6). Overnight rostral fluid shift was associated with increased pharyngeal collapsibility and decreased pharyngeal caliber during sleep in CHF patients. Rostral fluid shift may be an important mechanism of OSA in this patient group.
Alqurashi Y, Moss J, Nakamura T, et al., 2017, The Efficacy Of In-Ear Electroencephalography (eeg) To Monitor Sleep Latency And The Impact Of Sleep Deprivation, International Conference of the American-Thoracic-Society (ATS), Publisher: AMER THORACIC SOC, ISSN: 1073-449X
Eastwood PR, Morrell MJ, Malhotra A, 2016, Update in respiratory sleep disorders: Prologue to a modern review series, RESPIROLOGY, Vol: 22, Pages: 17-18, ISSN: 1323-7799
Looney D, Goverdovsky V, Rosenzweig I, et al., 2016, A Wearable In-Ear Encephalography Sensor for Monitoring Sleep: Preliminary Observations from Nap Studies, Annals of the American Thoracic Society, Vol: 13, Pages: 2229-2233, ISSN: 2329-6933
RATIONALE: To date the only quantifiable measure of neural changes that define sleep is electroencephalography (EEG). Although widely used for clinical testing, scalp-electrode EEG is costly and poorly tolerated by sleeping patients. OBJECTIVES: This is a pilot study to assess the agreement between EEG recordings obtained from a new ear-EEG sensor and those obtained simultaneously from standard scalp electrodes. METHODS: Participants were 4 healthy men, ages 25 to 36 years. During naps, EEG tracings were recorded simultaneously from the ear sensor and standard scalp electrodes. A clinical expert, blinded to the data collection, analyzed 30-second epochs of recordings from both devices using standardized criteria. The agreement between scalp- and ear-recordings was assessed. MEASUREMENTS AND MAIN RESULTS: We scored 360 epochs (scalp-EEG and ear-EEG) of which 254 (70.6%) were scored as non-rapid-eye movement (NREM) sleep using scalp-EEG. The ear-EEG sensor had a sensitivity of 0.88 (95% CI 0.82 to 0.92) and specificity of 0.78 (95% CI 0.70 to 0.84) in detecting N2/N3 sleep. The kappa coefficient, between the scalp- and ear-EEG, was 0.65 (95% CI 0.58 to 0.73). As a sleep monitor (all NREM sleep stages versus wake), the in-ear sensor had a sensitivity of 0.91 (95% CI 0.87 to 0.94) and specificity of 0.66 (95% CI 0.56 to 0.75). The kappa coefficient was 0.60 (95% CI 0.50 to 0.69). CONCLUSIONS: Substantial agreement was observed between recordings derived from a new ear-EEG sensor and conventional scalp electrodes on 4 healthy volunteers during daytime naps.
Emamian F, Khazaie H, Tahmasian M, et al., 2016, The Association Between Obstructive Sleep Apnea and Alzheimer’s Disease: A Meta-Analysis Perspective, Frontiers in Aging Neuroscience, Vol: 8, ISSN: 1663-4365
Morrell MJ, McMillan A, 2016, Does Age Matter? The Relationship between Sleep-disordered Breathing and Incident Atrial Fibrillation in Older Men, American Journal of Respiratory and Critical Care Medicine, Vol: 193, Pages: 712-714, ISSN: 1535-4970
Tahmasian M, Rosenzweig I, Eickhoff SB, et al., 2016, Structural and functional neural adaptations in obstructive sleep apnea: An activation likelihood estimation meta-analysis, Neuroscience and Biobehavioral Reviews, Vol: 65, Pages: 142-156, ISSN: 1873-7528
Obstructive sleep apnea (OSA) is a common multisystem chronic disorder. Functional and structural neuroimaging has been widely applied in patients with OSA, but these studies have often yielded diverse results. The present quantitative meta-analysis aims to identify consistent patterns of abnormal activation and grey matter loss in OSA across studies. We used PubMed to retrieve task/resting-state functional magnetic resonance imaging and voxel-based morphometry studies. Stereotactic data were extracted from fifteen studies, and subsequently tested for convergence using activation likelihood estimation. We found convergent evidence for structural atrophy and functional disturbances in the right basolateral amygdala/hippocampus and the right central insula. Functional characterization of these regions using the BrainMap database suggested associated dysfunction of emotional, sensory, and limbic processes. Assessment of task-based co-activation patterns furthermore indicated that the two regions obtained from the meta-analysis are part of a joint network comprising the anterior insula, posterior-medial frontal cortex and thalamus. Taken together, our findings highlight the role of right amygdala, hippocampus and insula in the abnormal emotional and sensory processing in OSA.
Rosenzweig I, Glasser M, Crum WR, et al., 2016, Changes in Neurocognitive Architecture in Patients with Obstructive Sleep Apnea Treated with Continuous Positive Airway Pressure., EBioMedicine, Vol: 7, Pages: 221-229, ISSN: 2352-3964
BACKGROUND: Obstructive sleep apnea (OSA) is a chronic, multisystem disorder that has a bidirectional relationship with several major neurological disorders, including Alzheimer's dementia. Treatment with Continuous Positive Airway Pressure (CPAP) offers some protection from the effects of OSA, although it is still unclear which populations should be targeted, for how long, and what the effects of treatment are on different organ systems. We investigated whether cognitive improvements can be achieved as early as one month into CPAP treatment in patients with OSA. METHODS: 55 patients (mean (SD) age: 47.6 (11.1) years) with newly diagnosed moderate-severe OSA (Oxygen Desaturation Index: 36.6 (25.2) events/hour; Epworth sleepiness score (ESS): 12.8 (4.9)) and 35 matched healthy volunteers were studied. All participants underwent neurocognitive testing, neuroimaging and polysomnography. Patients were randomized into parallel groups: CPAP with best supportive care (BSC), or BSC alone for one month, after which they were re-tested. FINDINGS: One month of CPAP with BSC resulted in a hypertrophic trend in the right thalamus [mean difference (%): 4.04, 95% CI: 1.47 to 6.61], which was absent in the BSC group [-2.29, 95% CI: -4.34 to -0.24]. Significant improvement was also recorded in ESS, in the CPAP plus BSC group, following treatment [mean difference (%): -27.97, 95% CI: -36.75 to -19.19 vs 2.46, 95% CI: -5.23 to 10.15; P=0.012], correlated to neuroplastic changes in brainstem (r=-0.37; P=0.05), and improvements in delayed logical memory scores [57.20, 95% CI: 42.94 to 71.46 vs 23.41, 95% CI: 17.17 to 29.65; P=0.037]. INTERPRETATION: One month of CPAP treatment can lead to adaptive alterations in the neurocognitive architecture that underlies the reduced sleepiness, and improved verbal episodic memory in patients with OSA. We propose that partial neural recovery occurs during short periods of treatment with CPAP.
McMillan A, Morrell MJ, 2016, Sleep disordered breathing at the extremes of age: the elderly, BREATHE, Vol: 12, Pages: 51-60, ISSN: 1810-6838
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