Project description:Abstract In our ongoing phase III randomized trial (NCT03741673) evaluating risk of leptomeningeal disease (LMD) in patients with brain metastasis, patients are stratified by tumor size, location, and histology, and randomized to pre-operative SRS versus post-operative SRS. The primary endpoint is 1-year LMD-free rate, and exploratory endpoints include neurocognitive function (NCF). NCF is assessed prior to treatment and longitudinally (1.5, 6, and 12 months from the start of treatment) with the HVLT-R, TMT, and COWA yielding the Clinical Trial Battery Composite score (CTB COMP). Patients also complete the MDASI-BT, which includes a Cognitive Factor measuring patient reported cognitive concerns. Currently, 41 patients completed pre-treatment NCF baseline. Characteristics of patients with pre-treatment NCF baseline include mean (SD): age 60.0+/-11.7 years, 2.6+/-2.2 metastases, 15.2+/-10.1 cc brain metastases volume, median KPS 90 (range: 70-100), 83% white, 52% male, and 50% received AEDs at time of surgery. The most common primary cancers among this cohort were: 31% NSCLC, 19% RCC, 14% melanoma, 7% breast, 2% thyroid, and 26% other. Mean (SD) NCF z-scores ranged from z= -0.09 (1.08) to -3.09 (6.13), CTB COMP z=-1.13 (1.65), and the mean (SD) MDASI-BT Cognitive Factor score was 1.1 (1.3). Using multivariable linear regression including CTB COMP, MDASI-BT Cognitive and Affective Factor, KPS, number of and intracranial volume of metastases, only intracranial volume of metastases was statistically significantly associated with CTB COMP (B=-.058, p=.034) and only the MDASI-BT Affective Factor was statistically significantly associated with the MDASI-BT Cognitive Factor (B=.305, p=<.001). At baseline, disease factors such as tumor burden appear to be the primary driver of objectively measured neurocognitive dysfunction and mood factors appear to be the primary driver of subjective patient reported neurocognitive concerns. This has important implications for interpretation of these different clinical outcome assessments in clinical trials measuring objective neurocognitive function and subjective neurocognitive concerns.

Project description:Background: Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia and post-operative atrial fibrillation (POAF) is a major healthcare burden, contributing to an increased risk of stroke, kidney failure, heart attack and death. Genetic studies have identified associations with AF, but no molecular diagnostic exists to predict POAF based on pre-operative measurements. Such a tool would be of great value for perioperative planning to improve patient care and reduce healthcare costs. In this pilot study of epigenetic precision medicine in the perioperative period, we carried out bisulfite sequencing to measure DNA methylation status in blood collected from patients prior to cardiac surgery to identify biosignatures of POAF. Methods: We enrolled 221 patients undergoing cardiac surgery in this prospective observational study. DNA methylation measurements were obtained from blood samples drawn from awake patients prior to surgery. After controlling for clinical and methylation covariates, we analyzed DNA methylation loci in the discovery cohort of 110 patients for association with POAF. We also constructed predictive models for POAF using clinical and DNA methylation data. We subsequently performed targeted analyses of a separate cohort of 101 cardiac surgical patients to measure the methylation status solely of significant methylation loci in the discovery cohort. Results: A total of 47 patients in the discovery cohort (42.7%) and 43 patients in the validation cohort (42.6%) developed POAF. We identified 12 CpGs that were statistically significant in the discovery cohort after correcting for multiple hypothesis testing. Of these sites, 6 were amenable to targeted bisulfite sequencing and chr16:24640902 was statistically significant in the validation cohort. In addition, the methylation POAF prediction model had an AUC of 0.79 in the validation cohort. Conclusions: We have identified DNA methylation biomarkers that can predict future occurrence of POAF associated with cardiac surgery. This research demonstrates the use of precision medicine to develop models combining epigenomic and clinical data to predict disease.

Project description:Converging evidence suggests that human cognition and behavior emerge from functional brain networks interacting on local and global scales. We investigated two information-theoretic measures of functional brain segregation and integration-interaction complexity C I (X), and integration I(X)-as applied to electroencephalographic (EEG) signals and how these measures are affected by choice of EEG reference. CI(X) is a statistical measure of the system entropy accounted for by interactions among its elements, whereas I(X) indexes the overall deviation from statistical independence of the individual elements of a system. We recorded 72 channels of scalp EEG from human participants who sat in a wakeful resting state (interleaved counterbalanced eyes-open and eyes-closed blocks). CI(X) and I(X) of the EEG signals were computed using four different EEG references: linked-mastoids (LM) reference, average (AVG) reference, a Laplacian (LAP) "reference-free" transformation, and an infinity (INF) reference estimated via the Reference Electrode Standardization Technique (REST). Fourier-based power spectral density (PSD), a standard measure of resting state activity, was computed for comparison and as a check of data integrity and quality. We also performed dipole source modeling in order to assess the accuracy of neural source CI(X) and I(X) estimates obtained from scalp-level EEG signals. CI(X) was largest for the LAP transformation, smallest for the LM reference, and at intermediate values for the AVG and INF references. I(X) was smallest for the LAP transformation, largest for the LM reference, and at intermediate values for the AVG and INF references. Furthermore, across all references, CI(X) and I(X) reliably distinguished between resting-state conditions (larger values for eyes-open vs. eyes-closed). These findings occurred in the context of the overall expected pattern of resting state PSD. Dipole modeling showed that simulated scalp EEG-level CI(X) and I(X) reflected changes in underlying neural source dependencies, but only for higher levels of integration and with highest accuracy for the LAP transformation. Our observations suggest that the Laplacian-transformation should be preferred for the computation of scalp-level CI(X) and I(X) due to its positive impact on EEG signal quality and statistics, reduction of volume-conduction, and the higher accuracy this provides when estimating scalp-level EEG complexity and integration.

Project description:This study was based on the contemporary proposal that distinct states of consciousness are quantifiable by neural complexity and critical dynamics. To test this hypothesis, it was aimed at comparing the electrophysiological correlates of three meditation conditions using nonlinear techniques from the complexity and criticality framework as well as power spectral density. Thirty participants highly proficient in meditation were measured with 64-channel electroencephalography (EEG) during one session consisting of a task-free baseline resting (eyes closed and eyes open), a reading condition, and three meditation conditions (thoughtless emptiness, presence monitoring, and focused attention). The data were analyzed applying analytical tools from criticality theory (detrended fluctuation analysis, neuronal avalanche analysis), complexity measures (multiscale entropy, Higuchi's fractal dimension), and power spectral density. Task conditions were contrasted, and effect sizes were compared. Partial least square regression and receiver operating characteristics analysis were applied to determine the discrimination accuracy of each measure. Compared to resting with eyes closed, the meditation categories emptiness and focused attention showed higher values of entropy and fractal dimension. Long-range temporal correlations were declined in all meditation conditions. The critical exponent yielded the lowest values for focused attention and reading. The highest discrimination accuracy was found for the gamma band (0.83-0.98), the global power spectral density (0.78-0.96), and the sample entropy (0.86-0.90). Electrophysiological correlates of distinct meditation states were identified and the relationship between nonlinear complexity, critical brain dynamics, and spectral features was determined. The meditation states could be discriminated with nonlinear measures and quantified by the degree of neuronal complexity, long-range temporal correlations, and power law distributions in neuronal avalanches.

Project description:BackgroundNeurocognitive disorder (NCD) is common in stroke survivors. We aimed to identify clinically accessible imaging markers of stroke and chronic pathology that are associated with early post-stroke NCD.MethodsWe included 231 stroke survivors from the "Norwegian Cognitive Impairment after Stroke (Nor-COAST)" study who underwent a standardized cognitive assessment 3 months after the stroke. Any NCD (mild cognitive impairment and dementia) and major NCD (dementia) were diagnosed according to "Diagnostic and Statistical Manual of Mental Disorders (DSM-5)" criteria. Clinically accessible imaging findings were analyzed on study-specific brain MRIs in the early phase after stroke. Stroke lesion volumes were semi automatically quantified and strategic stroke locations were determined by an atlas based coregistration. White matter hyperintensities (WMH) and medial temporal lobe atrophy (MTA) were visually scored. Logistic regression was used to identify neuroimaging findings associated with major NCD and any NCD.ResultsMean age was 71.8 years (SD 11.1), 101 (43.7%) were females, mean time from stroke to imaging was 8 (SD 16) days. At 3 months 63 (27.3%) had mild NCD and 65 (28.1%) had major NCD. Any NCD was significantly associated with WMH pathology (odds ratio (OR) = 2.73 [1.56 to 4.77], p = 0.001), MTA pathology (OR = 1.95 [1.12 to 3.41], p = 0.019), and left hemispheric stroke (OR = 1.8 [1.05 to 3.09], p = 0.032). Major NCD was significantly associated with WMH pathology (OR = 2.54 [1.33 to 4.84], p = 0.005) and stroke lesion volume (OR (per ml) =1.04 [1.01 to 1.06], p = 0.001).ConclusionWMH pathology, MTA pathology and left hemispheric stroke were associated with the development of any NCD. Stroke lesion volume and WMH pathology were associated with the development of major NCD 3 months after stroke. These imaging findings may be used in the routine clinical setting to identify patients at risk for early post-stroke NCD.Trial registrationClinicalTrials.gov, NCT02650531 , Registered 8 January 2016 - Retrospectively registered.

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Electroencephalogram-Based Complexity Measures as Predictors of Post-operative Neurocognitive Dysfunction.

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