Diego Z. Carvalho

Insomnia characteristics and clinical correlates in Operation Enduring Freedom/Operation Iraqi Freedom veterans with post-traumatic stress disorder and mild traumatic brain injury: An exploratory study

BACKGROUND There is limited data on chronic insomnia in Operation Enduring Freedom/Operation Iraqi Freedom (OEF/OIF) veterans, in whom post-traumatic stress disorder (PTSD) and mild traumatic brain injury (mTBI) often co-exist. Our aim was to compare sleep characteristics of three groups of OEF/OIF veterans: (1) healthy sleepers (HS), (2) those with insomnia associated with PTSD and mTBI (PTSD-mTBI), and (3) those with insomnia associated with PTSD alone. METHODS Consecutive veterans with insomnia complaints (> 6 months) were recruited over 6 months from the Miami VA Post Deployment clinic. Participants completed a sleep disorders clinical interview, medical history, and questionnaires about insomnia, sleepiness, pain, fatigue, depression, PTSD, and health-related quality of life. They underwent polysomnography (PSG) with 2 weeks of actigraphy (ACT) and sleep diaries. RESULTS There were no differences in demographics or most questionnaire responses between PTSD and PTSD-mTBI groups. Subjective daytime sleepiness was significantly greater in PTSD-mTBI subjects compared with HS and PTSD participants. Significant co-morbid sleep disorders were noted in insomnia patients. PSG and ACT wake after sleep onset was significantly shorter in PTSD-mTBI subjects as compared with PTSD participants. CONCLUSION Insomnia patients with PTSD-mTBI were subjectively sleepier despite spending less time awake during the night than PTSD subjects, possibly as a consequence of head trauma.

Topography-specific spindle frequency changes in Obstructive Sleep Apnea

BackgroundSleep spindles, as detected on scalp electroencephalography (EEG), are considered to be markers of thalamo-cortical network integrity. Since obstructive sleep apnea (OSA) is a known cause of brain dysfunction, the aim of this study was to investigate sleep spindle frequency distribution in OSA. Seven non-OSA subjects and 21 patients with OSA (11 mild and 10 moderate) were studied. A matching pursuit procedure was used for automatic detection of fast (≥13Hz) and slow (<13Hz) spindles obtained from 30min samples of NREM sleep stage 2 taken from initial, middle and final night thirds (sections I, II and III) of frontal, central and parietal scalp regions.ResultsCompared to non-OSA subjects, Moderate OSA patients had higher central and parietal slow spindle percentage (SSP) in all night sections studied, and higher frontal SSP in sections II and III. As the night progressed, there was a reduction in central and parietal SSP, while frontal SSP remained high. Frontal slow spindle percentage in night section III predicted OSA with good accuracy, with OSA likelihood increased by 12.1%for every SSP unit increase (OR 1.121, 95% CI 1.013 - 1.239, p=0.027).ConclusionsThese results are consistent with diffuse, predominantly frontal thalamo-cortical dysfunction during sleep in OSA, as more posterior brain regions appear to maintain some physiological spindle frequency modulation across the night. Displaying changes in an opposite direction to what is expected from the aging process itself, spindle frequency appears to be informative in OSA even with small sample sizes, and to represent a sensitive electrophysiological marker of brain dysfunction in OSA.

Loss of sleep spindle frequency deceleration in Obstructive Sleep Apnea

OBJECTIVE Sleep spindles have been suggested as surrogates of thalamo-cortical activity. Internal frequency modulation within a spindles time frame has been demonstrated in healthy subjects, showing that spindles tend to decelerate their frequency before termination. We investigated internal frequency modulation of slow and fast spindles according to Obstructive Sleep Apnea (OSA) severity and brain topography. METHODS Seven non-OSA subjects and 21 patients with OSA contributed with 30min of Non-REM sleep stage 2, subjected to a Matching pursuit procedure with Gabor chirplet functions for automatic detection of sleep spindles and quantification of sleep spindle internal frequency modulation (chirp rate). RESULTS Moderate OSA patients showed an inferior percentage of slow spindles with deceleration when compared to Mild and Non-OSA groups in frontal and parietal regions. In parietal regions, the percentage of slow spindles with deceleration was negatively correlated with global apnea-hypopnea index (rs=-0.519, p=0.005). DISCUSSION Loss of physiological sleep spindle deceleration may either represent a disruption of thalamo-cortical loops generating spindle oscillations or some compensatory mechanism, an interesting venue for future research in the context of cognitive dysfunction in OSA. SIGNIFICANCE Quantification of internal frequency modulation (chirp rate) is proposed as a promising approach to advance description of sleep spindle dynamics in brain pathology.

Quantifying chirp in sleep spindles.

Sleep spindles are considered as a marker of integrity for thalamo-cortical circuits. Recently, attention has been given to internal frequency variation in sleep spindles. In this study, a procedure based on matching pursuit with a Gabor-chirplet dictionary was applied in order to measure chirp rate in atoms representing sleep spindles, also categorized into negative, positive or zero chirp types. The sample comprised 707 EEG segments containing visual sleep spindles, labeled TP, obtained from nine healthy male volunteers (aged 20-34, average 24.6 y). Control datasets were 333 non-REM (NREM) sleep background segments and 287 REM sleep intervals, each with 16s duration. Analyses were carried out on the C3-A2 EEG channel. In TP and NREM groups, the proportion of non-null chirp types was non-random and total chirp distribution was asymmetrical towards negative values, in contrast to REM. Median negative chirp rate in the TP and NREM groups was significantly lower than in REM (-0.4 Hz/s vs -0.3 Hz/s, P < 0.05). Negative chirp atoms outnumbered positives by 50% in TP, while in NREM and REM, they were, respectively, only 22% and 12% more prevalent. TP negative chirp atoms were significantly higher in amplitude compared to positive or zero types. Considering individual subjects, 88.9% had a TP negative/positive chirp ratio above 1 (mean ± sd=1.64 ± 0.65). We propose there is increasing evidence, corroborated by the present study, favoring systematic measurement of sleep spindle chirp rate or internal frequency variation. Preferential occurrence of negatively chirping spindles is consistent with the hypothesis of electrophysiological modulation of neocortical memory consolidation.

Insomnia symptoms in South Florida military veterans with epilepsy.

BACKGROUND Despite the high prevalence of insomnia in veterans with epilepsy, it remains understudied. Our aim was to identify the associations of insomnia with epilepsy, comorbidities, and treatment-related variables in South Florida veterans. METHODS We performed a cross-sectional analysis of veterans attending an epilepsy clinic over 18 months. Participants completed standardized assessments of seizure and sleep. Insomnia was defined as 1) difficulty with sleep onset, maintenance, or premature awakenings with daytime consequences or 2) sedative-hypnotic use on most nights of the previous month. RESULTS One hundred sixty-five veterans (87% male, age 56 ± 15 years) were included: 66 reporting insomnia (40%). In logistic regression analysis, insomnia was significantly associated with post-traumatic seizure etiology, lamotrigine prescription, and mood and psychotic disorders. Female gender and levetiracetam treatment were associated with lower odds for insomnia. CONCLUSION Insomnia was associated with post-traumatic epilepsy, mood/psychotic comorbidities, and antiepileptic regimen. Insomnia represents an under-recognized opportunity to improve comprehensive epilepsy care.

Sleep-related falling out of bed in Parkinson's disease

Background and Purpose Sleep-related falling out of bed (SFOB), with its potential for significant injury, has not been a strong focus of investigation in Parkinsons disease (PD) to date. We describe the demographic and clinical characteristics of PD patients with and without SFOB. Methods We performed a retrospective analysis of 50 consecutive PD patients, who completed an REM sleep behavior disorder screening questionnaire (RBDSQ), questionnaires to assess for RBD clinical mimickers and questions about SFOB and resulting injuries. Determination of high risk for RBD was based on an RBDSQ score of 5 or greater. Results Thirteen patients reported history of SFOB (26%). Visual hallucinations, sleep-related injury, quetiapine and amantadine use were more common in those patients reporting SFOB. Twenty-two patients (44%) fulfilled criteria for high risk for RBD, 12 of which (55%) reported SFOB. Five patients reported injuries related to SFOB. SFOB patients had higher RBDSQ scores than non-SFOB patients (8.2±3.0 vs. 3.3±2.0, p<0.01). For every one unit increase in RBDSQ score, the likelihood of SFOB increased two-fold (OR 2.4, 95% CI 1.3-4.2, p<0.003). Conclusions SFOB may be a clinical marker of RBD in PD and should prompt confirmatory polysomnography and pharmacologic treatment to avoid imminent injury. Larger prospective studies are needed to identify risk factors for initial and recurrent SFOB in PD.

Association of Excessive Daytime Sleepiness With Longitudinal β-Amyloid Accumulation in Elderly Persons Without Dementia

Importance Aging is associated with excessive daytime sleepiness (EDS), which has been linked to cognitive decline in the elderly. However, whether EDS is associated with the pathologic processes of Alzheimer disease remains unclear. Objective To investigate whether EDS at baseline is associated with a longitudinal increase in regional &bgr;-amyloid (A&bgr;) accumulation in a cohort of elderly individuals without dementia. Design, Setting, and Participants This prospective analysis included participants enrolled in the Mayo Clinic Study of Aging, a longitudinal population-based study in Olmsted County, Minnesota. Of 2900 participants, 2172 (74.9%) agreed to undergo carbon 11–labeled Pittsburgh compound B positron emission tomography (PiB-PET). We included 283 participants 70 years or older without dementia who completed surveys assessing sleepiness at baseline and had at least 2 consecutive PiB-PET scans from January 1, 2009, through July 31, 2016, after excluding 45 (13.7%) who had a comorbid neurologic disorder. Main Outcomes and Measures Excessive daytime sleepiness was defined as an Epworth Sleepiness Scale score of at least 10. The difference in A&bgr; levels between the 2 consecutive scans (&Dgr;PiB) in A&bgr;-susceptible regions (prefrontal, anterior cingulate, posterior cingulate-precuneus, and parietal) was determined. Multiple linear regression models were fit to explore associations between baseline EDS and &Dgr;PiB while adjusting for baseline age, sex, presence of the apolipoprotein E &egr;4 allele, educational level, baseline PiB uptake, global PiB positivity (standardized uptake value ratio ≥1.4), physical activity, cardiovascular comorbidities (obesity, hypertension, hyperlipidemia, and diabetes), reduced sleep duration, respiratory symptoms during sleep, depression, and interval between scans. Results Of the initial 283 participants, mean (SD) age was 77.1 (4.8) years; 204 (72.1%) were men and 79 (27.9%) were women. Sixty-three participants (22.3%) had EDS. Baseline EDS was significantly associated with increased regional A&bgr; accumulation in the anterior cingulate (B coefficient = 0.031; 95% CI, 0.001-0.061; P = .04), posterior cingulate-precuneus (B coefficient = 0.038; 95% CI, 0.006-0.069; P = .02), and parietal (B coefficient = 0.033; 95% CI, 0.001-0.065; P = .04) regions. Association of EDS with longitudinal A&bgr; accumulation was stronger in participants with baseline global PiB positivity in the anterior cingulate (B coefficient = 0.065; 95% CI, 0.010-0.118; P = .02) and cingulate-precuneus (B coefficient = 0.068; 95% CI, 0.009-0.126; P = .02) regions. Conclusions and Relevance Baseline EDS was associated with increased longitudinal A&bgr; accumulation in elderly persons without dementia, suggesting that those with EDS may be more vulnerable to pathologic changes associated with Alzheimer disease. Further work is needed to elucidate whether EDS is a clinical marker of greater sleep instability, synaptic or network overload, or neurodegeneration of wakefulness-promoting centers. Early identification of patients with EDS and treatment of underlying sleep disorders could reduce A&bgr; accumulation in this vulnerable group.

NREM sleep alpha and sigma activity in Parkinson’s disease: Evidence for conflicting electrophysiological activity?

OBJECTIVES Sleep EEG spectral patterns were investigated in eight newly diagnosed, non-depressed, non-demented, drug-naïve Parkinsons disease patients compared to nine controls. METHODS Mean relative spectral power density calculated for 0.25 Hz frequency bins and for classical EEG frequency bands. RESULTS Differences between patients and controls were most prominent in non-REM sleep, specially around 8.6 Hz (slow alpha), 12.5 Hz (fast alpha/slow sigma) and 15 Hz (fast sigma). Slow alpha showed lower p-values over frontal and occipital electrodes, whereas fast sigma activity was more important on central and parietal sites. Significantly increased NREM sleep alpha activity was found in left and right frontal (Mann-Whitney U=12,000, p=.021; U=14,000, p=.036), left and right central (U=14,000, p=.036), left parietal and left occipital (U=13,000, p=.027; U=15,000, p=.046) areas. Increased sigma activity was found in right frontal (U=14,000, p=.036), left central (U=12,000, p=.021), left and right parietal (U=12,000, p=.021; U=13,000, p=.027) and left occipital (U=15,000, p=.046) areas. CONCLUSIONS Concomitantly increased scalp EEG alpha and sigma activity was found during NREM sleep in initial Parkinsons disease. SIGNIFICANCE These non-REM sleep microstructure changes may represent evidence for altered electrophysiological mechanisms leading to sleep-wake instability in early disease stages.

Overnight S100B in Parkinson’s Disease: A glimpse into sleep-related neuroinflammation

Calcium-binding protein B (S100B), a primary product of astrocytes, is a proposed marker of Parkinsons Disease (PD) pathophysiology, diagnosis and progression. However, it has also been implicated in sleep disruption, which is very common in PD. To explore the relationship between S100B, disease severity, sleep symptoms and polysomnography (PSG) findings, overnight changes in serum S100B levels were investigated for the first time in PD. 17 fully treated, non-demented, moderately advanced PD patients underwent PSG and clinical assessment of sleep symptoms. Serum S100B samples were collected immediately before and after the PSG. Results are shown as median [interquartile range]. Night and morning S100B levels were similar, but uncorrelated (rs=-0.277, p=0.28). Morning S100B levels, as opposed to night levels, positively correlated with the Unified Parkinsons Disease rating scale (UPDRS) subsections I and II (rs=0.547, p=0.023; rs=0.542, p=0.025). Compared to those with overnight S100B reduction, patients with overnight S100B elevation had higher H&Y scores (2.5 [0.87] vs. 2 [0.25], p=0.035) and worse total Pittsburgh Sleep Quality Index (PSQI) and Parkinsons Disease Sleep Scores (10 [3.2] vs. 8 [4.5], p=0.037; 92.9 [39] vs. 131.4 [28], p=0.034). Correlation between morning S100B levels and total UPDRS score was strengthened after controlling for total PSQI score (rs=0.531, p=0.034; partial rs=0.699, p=0.004, respectively). Overnight S100B variation and morning S100B were associated with PD severity and perceived sleep disruption. S100B is proposed as a putative biomarker for sleep-related neuroinflammation in PD. Noradrenergic-astrocytic dysfunction is hypothesized as a possible mechanism underlying these findings.

S141. Predicting brain age from the electroencephalogram of sleep

Introduction The human electroencephalogram (EEG) of sleep undergoes profound changes with age, such as decreased sleep spindle amplitude and density in non-rapid eye movement stage 2 (NREM2). However, it is unknown how accurately a patient’s age can be predicted from EEG activity during sleep. A quantitative characterization of age-related EEG provides important insights into healthy aging. Moreover, the ability to detect deviations of EEG patterns from those typical for age could provide insights into age-related neurological disorders, and might provide a way to gauge the effects of interventions designed to preserve or improve brain health. Here we develop a model to predict a patient’s age based on large-scale and heterogeneous sleep EEG datasets. The prediction is called “brain age” (BA). Methods Datasets: (1) MGH sleep dataset: 3100 patients aged 18–80 years. (2) sleep-heart health study (SHHS): 3680 paired recordings aged 18–80 years, where each pair is recorded approximately 5 years apart from the same subject. This dataset is used to verify the longitudinal reliability of the brain age. EEG features: 102 features are extracted from 30s-epochs, and then averaged separately for the 5 sleep stages, yielding 510 features to summarize each patient’s overnight sleep. We also analyze medications and clinical variables to identify factors that help account for brain age being older or younger than chronological age. Results For 1000 testing patients from MGH dataset, the Pearson’s correlation between EEG-based brain age and chronological age is 0.86 (95% CI 0.84–0.87). The mean absolute prediction error (MAE) is 6.6 years. For SHHS dataset, training the model on a subset of 2000 records and testing on the other 1680 records achieves correlation at 0.71 (95% CI 0.69–0.73) and MAE 5.9 yrs. The average difference of BA between each pair is 4.8yrs. Training the entire MGH dataset and testing on SHHS achieves correlation at 0.61 (95% CI 0.59–0.63) and MAE 8.6 yrs. The average difference of BA between each pair is 3.7 yrs. In the MGH dataset, older brain age (predicted age greater than chronological age) is associated diabetes (Kruskal-Wallis test p-value 0.01) and weakly associated with wake time after sleep onset (Pearson’s correlation p-value 0.07). Conclusion Our results indicate that, at the population level, chronological age can be accurately predicted from overnight sleep EEG. Moreover, brain age accurately tracks chronological age. Further research is needed to characterize how EEG-based brain age relates to cognitive function and to what degree and by what means brain age is modifiable.