Parametric separation of phase-locked and non-phase-locked activity.

Brain dynamics recorded via electroencephalography (EEG) is conceptualized as a sum of two components: "phase-locked" and "non-phase-locked" to the stimulus. Phase-locked activity is often implicitly studied as event-related potentials (ERPs), and the trial-averaged estimates-evoked potentials (EP) considered both time-locked and phase-locked to the stimulus. The non-phase-locked activity, on the other hand, refers to an increase in power in a narrow band or broadband frequencies in the signal emerging at variable phases from stimulus initiation. Both components are understood to stem from different neuronal mechanisms; hence, accurately characterizing them is of immense importance to neuroscientific studies. Here, we discuss the drawbacks of currently used methods to separate the phase-locked and non-phase-locked activity and propose a novel concurrent phaser method (CPM) that simultaneously decomposes the two components. First, we establish that the single-trial separation of phase-locked and non-phase-locked power is an ill-posed problem. Second, using simulations where ground truth validation is possible, we elucidate how the estimation of non-phase-locked power gets biased by phase-locked power in the state-of-the-art averaging method and ways to resolve the issue using CPM. Next, we use two experimental EEG datasets-audio oddball and auditory steady-state responses (ASSR) to show that empirical signal-to-noise estimates warrant the usage of CPM to separate phase-locked and non-phase-locked activity. Thus, using ground truth validation from simulations and demonstration in real experimental scenarios, the efficacy of the proposed CPM is established.NEW & NOTEWORTHY Parametric models for estimation of phase-locked and non-phase-locked brain signals reveals how estimation of non-phase-locked component is biased by the variability of phase-locked component and at the level of single trial becomes an ill-posed problem. Furthermore, the modeling framework delimits the boundaries where traditional averaging approach can be trusted to estimate the phase-locked and non-phase-locked components.

A peek into the future of radiology using big data applications.

Big data is extremely large amount of data which is available in the radiology department. Big data is identified by four Vs - Volume, Velocity, Variety, and Veracity. By applying different algorithmic tools and converting raw data to transformed data in such large datasets, there is a possibility of understanding and using radiology data for gaining new knowledge and insights. Big data analytics consists of 6Cs - Connection, Cloud, Cyber, Content, Community, and Customization. The global technological prowess and per-capita capacity to save digital information has roughly doubled every 40 months since the 1980's. By using big data, the planning and implementation of radiological procedures in radiology departments can be given a great boost. Potential applications of big data in the future are scheduling of scans, creating patient-specific personalized scanning protocols, radiologist decision support, emergency reporting, virtual quality assurance for the radiologist, etc. Targeted use of big data applications can be done for images by supporting the analytic process. Screening software tools designed on big data can be used to highlight a region of interest, such as subtle changes in parenchymal density, solitary pulmonary nodule, or focal hepatic lesions, by plotting its multidimensional anatomy. Following this, we can run more complex applications such as three-dimensional multi planar reconstructions (MPR), volumetric rendering (VR), and curved planar reconstruction, which consume higher system resources on targeted data subsets rather than querying the complete cross-sectional imaging dataset. This pre-emptive selection of dataset can substantially reduce the system requirements such as system memory, server load and provide prompt results. However, a word of caution, "big data should not become "dump data" due to inadequate and poor analysis and non-structured improperly stored data. In the near future, big data can ring in the era of personalized and individualized healthcare.

Comparative Effect of Insulin Sensitizers and Statin on Metabolic Profile and Ultrasonographical Score in Non Alcoholic Fatty Liver Disease.

INTRODUCTION: Non Alcoholic Fatty Liver Disease (NAFLD) is a metabolic disorder involving fat accumulation in the liver. The initial management for patients with NAFLD includes lifestyle modification and weight loss in overweight or obese patients. AIM: The present study was conducted to compare the efficacy of insulin sensitizers and statin in the patients of NAFLD. MATERIALS AND METHODS: The study included 98 patients diagnosed with NAFLD on USG (Ultrasonography) abdomen, divided into three Groups randomly and administered Metformin (Group I), Rosuvastatin (Group II) or Pioglitazone (Group III) along with dietary intervention and lifestyle modification. Their Body Mass Index (BMI), liver function tests, fasting lipid profile, USG scores for fatty liver were done and followed up at 4 weeks, 12 weeks and 24 week for change in above parameters. RESULTS: Out of the three Groups, Group II showed a maximum improvements in usg scores for NAFLD (p<0.001) and fasting lipid profile. Group II also showed maximum derangement of liver enzymes at 24 weeks though none of the subjects had more than three times elevation of liver enzymes. CONCLUSION: Rosuvastatin may be an effective therapy as add on treatment to dietary and lifestyle intervention in patients of NAFLD. As an add-on treatment Rosuvastatin was superior to Pioglitazone or Metformin and acute decompensation is unlikely with this drug. Metformin was not effective as add on therapy for NAFLD, rather rapid weight loss in short period of time resulted in worsening of hepatic steatosis.

Reaching out to retirees.

The numbers pertaining to the approaching demographic boom in health-care-related expenditures are eye-popping: Health-related financial products to help retiring baby boomers deal with out-of-pocket costs alone will generate up to 80 billion dollars in revenues and 8-12 billion dollars in pre-tax profits by 2014. But health insurers will have to refocus their efforts if they want to take full advantage of this opportunity. (For more, see "Turning Subscribers Into Customers: The Future Is Now" in the July/August issue, the first in this two-part series.) Two areas are critical: product innovation to provide comprehensive solutions that meet seniors' needs more effectively, and advice-based distribution that creates privileged customer relationships.

Turning subscribers into customers: the future is now.

The launch of Medicare's prescription drug plan marks a major milestone in federal health policy. Nearly 90 percent of Medicare's 43 million beneficiaries now have some form of prescription drug coverage, approximately 6 million of whom had no coverage prior to the introduction of Part D. The enrollment boom presents a tremendous opportunity for health insurers. But the new enrollments also present a challenge, as insurers' ability to build customer loyalty will depend on their success in reorienting themselves to the consumer market where tastes can be even more fickle than in the group business. The good news is that as insurers focus on serving senior customers, there is an even more promising opportunity on the horizon than Medicare Part D: providing products that help consumers-particularly retirees and those nearing retirement-to plan for and manage their out-of-pocket health care spending. The retirement health market beyond Medicare Advantage and Part D could significantly impact many health insurers' bottom line; it's expected to generate $80 billion in revenues and $8-$12 billion in pre-tax profits by 2014.