A computational study of whole-brain connectivity in resting state and task fMRI

Background: We compared the functional brain connectivity produced during resting-state in which subjects were not actively engaged in a task with that produced while they actively performed a visual motion task (task-state). Material/Methods In this paper we employed graph-theoretical measures and network statistics in novel ways to compare, in the same group of human subjects, functional brain connectivity during resting-state fMRI with brain connectivity during performance of a high level visual task. We performed a whole-brain connectivity analysis to compare network statistics in resting and task states among anatomically defined Brodmann areas to investigate how brain networks spanning the cortex changed when subjects were engaged in task performance. Results: In the resting state, we found strong connectivity among the posterior cingulate cortex (PCC), precuneus, medial prefrontal cortex (MPFC), lateral parietal cortex, and hippocampal formation, consistent with previous reports of the default mode network (DMN). The connections among these areas were strengthened while subjects actively performed an event-related visual motion task, indicating a continued and strong engagement of the DMN during task processing. Regional measures such as degree (number of connections) and betweenness centrality (number of shortest paths), showed that task performance induces stronger inter-regional connections, leading to a denser processing network, but that this does not imply a more efficient system as shown by the integration measures such as path length and global efficiency, and from global measures such as small-worldness. Conclusions: In spite of the maintenance of connectivity and the “hub-like” behavior of areas, our results suggest that the network paths may be rerouted when performing the task condition

Consumer sleep monitors: is there a baby in the bathwater?

The rapid expansion of consumer sleep devices is outpacing the validation data necessary to assess the potential use of these devices in clinical and research settings. Common sleep monitoring devices utilize a variety of sensors to track movement as well as cardiac and respiratory physiology. The variety of sensors and user-specific factors offer the potential, at least theoretically, for clinically relevant information. We describe the current challenges for interpretation of consumer sleep monitoring data, since the devices are mainly used in non-medical contexts (consumer use) although medically-definable sleep disorders may commonly occur in this setting. A framework for addressing questions of how certain devices might be useful is offered. We suggest that multistage validation efforts are crucially needed, from the level of sensor data and algorithm output, to extrapolations beyond healthy adults and into other populations and real-world environments

Brain Age from the Electroencephalogram of Sleep

The human electroencephalogram (EEG) of sleep undergoes profound changes with age. These changes can be conceptualized as "brain age", which can be compared to an age norm to reflect the deviation from normal aging process. Here, we develop an interpretable machine learning model to predict brain age based on two large sleep EEG datasets: the Massachusetts General Hospital sleep lab dataset (MGH, N = 2,621) covering age 18 to 80; and the Sleep Hearth Health Study (SHHS, N = 3,520) covering age 40 to 80. The model obtains a mean absolute deviation of 8.1 years between brain age and chronological age in the healthy participants in the MGH dataset. As validation, we analyze a subset of SHHS containing longitudinal EEGs 5 years apart, which shows a 5.5 years difference in brain age. Participants with neurological and psychiatric diseases, as well as diabetes and hypertension medications show an older brain age compared to chronological age. The findings raise the prospect of using sleep EEG as a biomarker for healthy brain aging

Big data in sleep medicine: prospects and pitfalls in phenotyping

Clinical polysomnography (PSG) databases are a rich resource in the era of “big data” analytics. We explore the uses and potential pitfalls of clinical data mining of PSG using statistical principles and analysis of clinical data from our sleep center. We performed retrospective analysis of self-reported and objective PSG data from adults who underwent overnight PSG (diagnostic tests, n=1835). Self-reported symptoms overlapped markedly between the two most common categories, insomnia and sleep apnea, with the majority reporting symptoms of both disorders. Standard clinical metrics routinely reported on objective data were analyzed for basic properties (missing values, distributions), pairwise correlations, and descriptive phenotyping. Of 41 continuous variables, including clinical and PSG derived, none passed testing for normality. Objective findings of sleep apnea and periodic limb movements were common, with 51% having an apnea–hypopnea index (AHI) >5 per hour and 25% having a leg movement index >15 per hour. Different visualization methods are shown for common variables to explore population distributions. Phenotyping methods based on clinical databases are discussed for sleep architecture, sleep apnea, and insomnia. Inferential pitfalls are discussed using the current dataset and case examples from the literature. The increasing availability of clinical databases for large-scale analytics holds important promise in sleep medicine, especially as it becomes increasingly important to demonstrate the utility of clinical testing methods in management of sleep disorders. Awareness of the strengths, as well as caution regarding the limitations, will maximize the productive use of big data analytics in sleep medicine

High level motion: neural correlates and functional connectivity

Thesis (M.Sc.Eng.) PLEASE NOTE: Boston University Libraries did not receive an Authorization To Manage form for this thesis or dissertation. It is therefore not openly accessible, though it may be available by request. If you are the author or principal advisor of this work and would like to request open access for it, please contact us at [email protected]. Thank you.This thesis uses functional magnetic resonance imaging (fMRI) data to investigate: 1. The neural substrate of high level visual motion 2. The functional connectivity between a behavioral task and resting state. In chapter 1, we find the neural substrate of a set of psychophysical high level motion tasks. Specifically, we used tasks of visually guided navigation, such as heading from optic flow, landmarks, motion parallax, and collision detection. We also used tasks underlying the ability to perform object recognition from motion cues alone such as 3D Structure From Motion (SFM) and Biological Motion (BM). fMRI data was analyzed with Brain Voyager and activated anatomical areas were delineated using Matlab scripts developed in the laboratory. Several regions within the dorsal visual system elicited significant BOLD activity: the dorsal-occipital (BA19) and parietal lobes (BA 37, 40, 7). The ventral areas (BA 20, 21, 22, 38) showed significant BOLD activity only in BM and SFM and in heading tests using landmarks or motion parallax. We generated a schematic map with the overlapping areas among high level motion tasks, which can aid in diagnosis and rehabilitation of motion deficits in neurological patients. In chapter 2, we computed the functional brain connectivity between the brain areas in a resting state (subject performs no task), and during task (subject performs a visual motion task). In the resting state, we found connectivity using correlations between the posterior cingulate cortex (PCC), precuneus, medial prefrontal cortex (MPFC), lateral parietal cortex, and the hippocampal formation, which have been reported as the default mode network (DMN) since it represents correlated neural activity during a state of rest. We used bivariate correlations to compute functional connectivity using the CONN fMRI toolbox and in-house Matlab scripts. We computed a whole-brain analysis and compared network statistics in both, resting state and during task to investigate measures of integration such as path length and global efficiency, regional measures such as degree (number of connections) and betweenness centrality (number of shortest paths), and global measures such as small-worldness. The DMN and graph theoretical measures connectivity during task was stronger as compared with the resting state. We also computed these measures in task using a similar frequency spectrum as rest (0.009 Hz < f < 0.08 Hz), and in the full frequency spectrum. We find that on the whole, the connectivity measures in the DMN and the graph theoretical measure are stronger in the fullband signal processing analysis as compared to the bandpass version of the analysis.2031-01-0

Periodic limb movements of sleep: empirical and theoretical evidence supporting objective at-home monitoring

Introduction: Periodic limb movements of sleep (PLMS) may increase cardiovascular and cerebrovascular morbidity. However, most people with PLMS are either asymptomatic or have nonspecific symptoms. Therefore, predicting elevated PLMS in the absence of restless legs syndrome remains an important clinical challenge. Methods: We undertook a retrospective analysis of demographic data, subjective symptoms, and objective polysomnography (PSG) findings in a clinical cohort with or without obstructive sleep apnea (OSA) from our laboratory (n=443 with OSA, n=209 without OSA). Correlation analysis and regression modeling were performed to determine predictors of periodic limb movement index (PLMI). Markov decision analysis with TreeAge software compared strategies to detect PLMS: in-laboratory PSG, at-home testing, and a clinical prediction tool based on the regression analysis. Results: Elevated PLMI values (>15 per hour) were observed in >25% of patients. PLMI values in No-OSA patients correlated with age, sex, self-reported nocturnal leg jerks, restless legs syndrome symptoms, and hypertension. In OSA patients, PLMI correlated only with age and self-reported psychiatric medications. Regression models indicated only a modest predictive value of demographics, symptoms, and clinical history. Decision modeling suggests that at-home testing is favored as the pretest probability of PLMS increases, given plausible assumptions regarding PLMS morbidity, costs, and assumed benefits of pharmacological therapy. Conclusion: Although elevated PLMI values were commonly observed, routinely acquired clinical information had only weak predictive utility. As the clinical importance of elevated PLMI continues to evolve, it is likely that objective measures such as PSG or at-home PLMS monitors will prove increasingly important for clinical and research endeavors

Alternative remedies for insomnia: a proposed method for personalized therapeutic trials

Insomnia is a common symptom, with chronic insomnia being diagnosed in 5–10% of adults. Although many insomnia patients use prescription therapy for insomnia, the health benefits remain uncertain and adverse risks remain a concern. While similar effectiveness and risk concerns exist for herbal remedies, many individuals turn to such alternatives to prescriptions for insomnia. Like prescription hypnotics, herbal remedies that have undergone clinical testing often show subjective sleep improvements that exceed objective measures, which may relate to interindividual heterogeneity and/or placebo effects. Response heterogeneity can undermine traditional randomized trial approaches, which in some fields has prompted a shift toward stratified trials based on genotype or phenotype, or the so-called n-of-1 method of testing placebo versus active drug in within-person alternating blocks. We reviewed six independent compendiums of herbal agents to assemble a group of over 70 reported to benefit sleep. To bridge the gap between the unfeasible expectation of formal evidence in this space and the reality of common self-medication by those with insomnia, we propose a method for guided self-testing that overcomes certain operational barriers related to inter- and intraindividual sources of phenotypic variability. Patient-chosen outcomes drive a general statistical model that allows personalized self-assessment that can augment the open-label nature of routine practice. The potential advantages of this method include flexibility to implement for other (nonherbal) insomnia interventions

Analytical Biochemistry 337 1 70 75 United States

Alkaline comet assay is a simple sensitive method for detecting DNA strand breaks. However, at the time of cell lysis, only a fraction of the entire DNA damage appears as DNA strand breaks, while some DNA strand breaks may have been rejoined and some DNA lesions may still remain unexcised. We showed that nuclear extract (NE) prepared from human cells could excise the DNA adducts induced by UVC, X-ray, and methyl methanesulfonate (MMS). Thus, the comet assay with NE incubation allows a closer estimation of total DNA damage. Among the human urothelial carcinoma cell lines we tested, the NE of NTUB1 cells showed higher activity in excising the DNA adducts induced by UVC, but with a lower activity in excising the DNA adducts induced by MMS than the NE of BFTC905 cells. Moreover, under the same dose of X-ray irradiation, a larger difference in total DNA damage between two cell lines was revealed in comet assay incubated with NE than without NE. Therefore, the comet assay with NE incubation may be useful in the research of cancer risk, drug resistance, and DNA repair proteins

Brain age from the electroencephalogram of sleep

The human electroencephalogram (EEG) of sleep undergoes profound changes with age. These changes can be conceptualized as “brain age (BA),” which can be compared to chronological age to reflect the degree of deviation from normal aging. Here, we develop an interpretable machine learning model to predict BA based on 2 large sleep EEG data sets: the Massachusetts General Hospital (MGH) sleep lab data set (N = 2532; ages 18–80); and the Sleep Heart Health Study (SHHS, N = 1974; ages 40–80). The model obtains a mean absolute deviation of 7.6 years between BA and chronological age (CA) in healthy participants in the MGH data set. As validation, a subset of SHHS containing longitudinal EEGs 5.2 years apart shows an average of 5.4 years increase in BA. Participants with significant neurological or psychiatric disease exhibit a mean excess BA, or “brain age index” (BAI = BA-CA) of 4 years relative to healthy controls. Participants with hypertension and diabetes have a mean excess BA of 3.5 years. The findings raise the prospect of using the sleep EEG as a potential biomarker for healthy brain aging