Healthy Normals Research Articles

Amyloid Burden in Alzheimer's Disease Patients Is Associated with Alterations in Circadian Rhythm.

Background and PurposeIn this study we evaluated the relationship between amyloid-beta (Aβ) deposition and 3 aspects of sleep quality in a group of clinically diagnosed Alzheimer's disease (AD) patients.MethodsWe used self-report questionnaires to assess the quality of sleep using 3 previously established surveys: the Glasgow Sleep Effort Scale (GSES), the Pittsburgh Sleep Quality Index (PSQI), and the Morningness-Eveningness Questionnaire (MEQ). These questionnaires focused on the sleep effort, sleep efficiency, and circadian rhythm patterns of each participant. Also, we evaluated the regional distribution of Aβ in the brain by amyloid positron emission tomography-computed tomography (PET-CT) standardized uptake value ratios (SUVRs) in healthy normal (HN), mild cognitive impairment (MCI), and AD dementia groups. The MCI and AD dementia groups were combined to form the group with cognitive impairment due to AD (CIAD).ResultsGSES and MEQ scores differed significantly between the HN, MCI, and AD dementia groups (p<0.037), whereas PSQI scores were similar across the groups (p=0.129). GSES and MEQ scores also differed between the HN and CIAD groups (p<0.018). Circadian rhythm scores positively correlated with amyloid PET-CT SUVR in posterior cingulate cortices (p<0.049).ConclusionsSleep effort and abnormal shifts in circadian rhythm were more significant in the CIAD group than in the HN group. At the same time, HN subjects had minimal sleep disturbance, irrespective of clinical status. Thus, alterations in circadian rhythm may be indicative of neurodegeneration due to Aβ deposition.

A State Space and Density Estimation Framework for Sleep Staging in Obstructive Sleep Apnea.

Although the importance of sleep is increasingly recognized, the lack of robust and efficient algorithms hinders scalable sleep assessment in healthy persons and those with sleep disorders. Polysomnography (PSG) and visual/manual scoring remain the gold standard in sleep evaluation, but more efficient/automated systems are needed. Most previous works have demonstrated algorithms in high agreement with the gold standard in healthy/normal (HN) individuals-not those with sleep disorders. This paper presents a statistical framework that automatically estimates whole-night sleep architecture in patients with obstructive sleep apnea (OSA)-the most common sleep disorder. Single-channel frontal electroencephalography was extracted from 65 HN/OSA sleep studies, and decomposed into 11 spectral features in 60903 30s sleep epochs. The algorithm leveraged kernel density estimation to generate stage-specific likelihoods, and a 5-state hidden Markov model to estimate per-night sleep architecture. Comparisons to full PSG expert scoring revealed the algorithm was in fair agreement with the gold standard (median Cohen's kappa = 0.53). Further, analysis revealed modest decreases in median scoring agreement as OSA severity increased from HN (kappa = 0.63) to severe (kappa = 0.47). A separate implementation on HN data from the Physionet Sleep-EDF Database resulted in a median kappa = 0.65, further indicating the algorithm's broad applicability. Results of this work indicate the proposed single-channel framework can emulate expert-level scoring of sleep architecture in OSA. Algorithms constructed to more accurately model physiological variability during sleep may help advance automated sleep assessment, for practical and general use in sleep medicine.

Local Tissue Water in At-Risk and Contralateral Forearms of Women with and without Breast Cancer Treatment-Related Lymphedema

Quantitative measurements to help detect incipient or latent lymphedema in patients at risk for breast cancer treatment-related lymphedema (BCRL) are potentially useful supplements to clinical assessments. Suitable measurements for routine use include arm volumes, arm bioimpedance, and local tissue water (LTW) determined from the tissue dielectric constant (TDC). Because BCRL initially develops in skin and subcutis, measures that include whole arms may not be optimally sensitive for detecting the earliest changes. Thus, there is also a need for a local measurement in which tissues most likely to demonstrate early lymphedematous changes can be more selectively assessed. The TDC method satisfies this criterion. Our goal was to use this method to compare arm-to-arm differences in LTW within and among women grouped as healthy normal (HN), diagnosed with breast cancer (BC), but prior to surgery and established unilateral lymphedema (LE). LTW was determined on both anterior forearms to a measurement depth of 2.5 mm in 30 women of each group. TDC arm ratios were determined as dominant/nondominant for HN and BC, at-risk/contralateral for BC, and lymphedematous/contralateral for LE. Results showed that TDC values for all arms except lymphedematous arms were very similar and insignificantly different with values among arms (mean +/- SD) ranging from 24.9 +/- 3.8 to 25.7 +/- 3.8. Arm ratios did not differ between HC and BC whereas dominant/non-dominant arm ratios for HN and BC separately and combined (1.006 +/- 0.085) were significantly less than the lymphedematous/contralateral ratio of the LE group (1.583 +/- 0.292). The findings indicate that LTW of at-risk arms is not affected by breast cancer and that lymphedema does not significantly affect LTW of contralateral arms as measured with the TDC method. Further, based on the standard deviation of measured arm ratios, an at-risk/contralateral TDC ratio of 1.26 is suggested as a possible threshold for detecting preclinical or latent lymphedema.