Effect of Statistical Fluctuation in Monte Carlo Based Photon Beam Dose Calculation on Gamma Index Evaluation

The gamma-index test has been commonly adopted to quantify the degree of agreement between a reference dose distribution and an evaluation dose distribution. Monte Carlo (MC) simulation has been widely used for the radiotherapy dose calculation for both clinical and research purposes. The goal of this work is to investigate both theoretically and experimentally the impact of the MC statistical fluctuation on the gamma-index test when the fluctuation exists in the reference, the evaluation, or both dose distributions. To the first order approximation, we theoretically demonstrated in a simplified model that the statistical fluctuation tends to overestimate gamma-index values when existing in the reference dose distribution and underestimate gamma-index values when existing in the evaluation dose distribution given the original gamma-index is relatively large for the statistical fluctuation. Our numerical experiments using clinical photon radiation therapy cases have shown that 1) when performing a gamma-index test between an MC reference dose and a non-MC evaluation dose, the average gamma-index is overestimated and the passing rate decreases with the increase of the noise level in the reference dose; 2) when performing a gamma-index test between a non-MC reference dose and an MC evaluation dose, the average gamma-index is underestimated when they are within the clinically relevant range and the passing rate increases with the increase of the noise level in the evaluation dose; 3) when performing a gamma-index test between an MC reference dose and an MC evaluation dose, the passing rate is overestimated due to the noise in the evaluation dose and underestimated due to the noise in the reference dose. We conclude that the gamma-index test should be used with caution when comparing dose distributions computed with Monte Carlo simulation

A GPU-based finite-size pencil beam algorithm with 3D-density correction for radiotherapy dose calculation

Targeting at the development of an accurate and efficient dose calculation engine for online adaptive radiotherapy, we have implemented a finite size pencil beam (FSPB) algorithm with a 3D-density correction method on GPU. This new GPU-based dose engine is built on our previously published ultrafast FSPB computational framework [Gu et al. Phys. Med. Biol. 54 6287-97, 2009]. Dosimetric evaluations against Monte Carlo dose calculations are conducted on 10 IMRT treatment plans (5 head-and-neck cases and 5 lung cases). For all cases, there is improvement with the 3D-density correction over the conventional FSPB algorithm and for most cases the improvement is significant. Regarding the efficiency, because of the appropriate arrangement of memory access and the usage of GPU intrinsic functions, the dose calculation for an IMRT plan can be accomplished well within 1 second (except for one case) with this new GPU-based FSPB algorithm. Compared to the previous GPU-based FSPB algorithm without 3D-density correction, this new algorithm, though slightly sacrificing the computational efficiency (~5-15% lower), has significantly improved the dose calculation accuracy, making it more suitable for online IMRT replanning

Initial estimate of NOAA-9 SBUV/2 total ozone drift: Based on comparison with re-calibrated TOMS measurements and pair justification of SBUV/2

Newly recalibrated version 6 Total Ozone Mapping Spectrometer (TOMS) data are used as a reference measurement in a comparison of monthly means of total ozone in 10 degree latitude zones from SBUV/2 and the nadir measurements from TOMS. These comparisons indicate a roughly linear long-term drift in SBUV/2 total ozone relative to TOMS of about 2.5 Dobson units per year at the equator over the first three years of SBUV/2. The pari justification technique is also applied to the SBUV/2 measurements in a manner similar to that used for SBUV and TOMS. The higher solar zenith angles associated with the afternoon orbit of NOAA-9 and the large changes in solar zenith angle associated with its changing equator crossing time degrade the accuracy of the pair justification method relative to its application to SBUV and TOMS, but the results are consistent with the SBUV/2-TOMS comparisons, and show a roughly linear drift in SBUV/2 of 2.5 to 4.5 Dobson units per year in equatorial ozone

Belief about Nicotine Modulates Subjective Craving and Insula Activity in Deprived Smokers

Little is known about the specific neural mechanisms through which cognitive factors influence craving and associated brain responses, despite the initial success of cognitive therapies in treating drug addiction. In this study, we investigated how cognitive factors such as beliefs influence subjective craving and neural activities in nicotine-addicted individuals using model-based functional magnetic resonance imaging (fMRI) and neuropharmacology. Deprived smokers (N = 24) participated in a two-by-two balanced placebo design, which crossed beliefs about nicotine (told "nicotine" vs. told "no nicotine") with the nicotine content in a cigarette (nicotine vs. placebo) which participants smoked immediately before performing a fMRI task involving reward learning. Subjects' reported craving was measured both before smoking and after the fMRI session. We found that first, in the presence of nicotine, smokers demonstrated significantly reduced craving after smoking when told "nicotine in cigarette" but showed no change in craving when told "no nicotine." Second, neural activity in the insular cortex related to craving was only significant when smokers were told "nicotine" but not when told "no nicotine." Both effects were absent in the placebo condition. Third, insula activation related to computational learning signals was modulated by belief about nicotine regardless of nicotine's presence. These results suggest that belief about nicotine has a strong impact on subjective craving and insula responses related to both craving and learning in deprived smokers, providing insights into the complex nature of belief-drug interactions

Cognitive strategies regulate fictive, but not reward prediction error signals in a sequential investment task.

Computational models of reward processing suggest that foregone or fictive outcomes serve as important information sources for learning and augment those generated by experienced rewards (e.g. reward prediction errors). An outstanding question is how these learning signals interact with top-down cognitive influences, such as cognitive reappraisal strategies. Using a sequential investment task and functional magnetic resonance imaging, we show that the reappraisal strategy selectively attenuates the influence of fictive, but not reward prediction error signals on investment behavior; such behavioral effect is accompanied by changes in neural activity and connectivity in the anterior insular cortex, a brain region thought to integrate subjective feelings with high-order cognition. Furthermore, individuals differ in the extent to which their behaviors are driven by fictive errors versus reward prediction errors, and the reappraisal strategy interacts with such individual differences; a finding also accompanied by distinct underlying neural mechanisms. These findings suggest that the variable interaction of cognitive strategies with two important classes of computational learning signals (fictive, reward prediction error) represent one contributing substrate for the variable capacity of individuals to control their behavior based on foregone rewards. These findings also expose important possibilities for understanding the lack of control in addiction based on possibly foregone rewarding outcomes. Hum Brain Mapp 35:3738-3749, 2014. © 2013 The Authors. Human Brain Mapping Published by Wiley Periodicals, Inc

Elastic shape matching of parameterized surfaces using square root normal fields.

In this paper we define a new methodology for shape analysis of parameterized surfaces, where the main issues are: (1) choice of metric for shape comparisons and (2) invariance to reparameterization. We begin by defining a general elastic metric on the space of parameterized surfaces. The main advantages of this metric are twofold. First, it provides a natural interpretation of elastic shape deformations that are being quantified. Second, this metric is invariant under the action of the reparameterization group. We also introduce a novel representation of surfaces termed square root normal fields or SRNFs. This representation is convenient for shape analysis because, under this representation, a reduced version of the general elastic metric becomes the simple \ensuremathL2\ensuremathL2 metric. Thus, this transformation greatly simplifies the implementation of our framework. We validate our approach using multiple shape analysis examples for quadrilateral and spherical surfaces. We also compare the current results with those of Kurtek et al. [1]. We show that the proposed method results in more natural shape matchings, and furthermore, has some theoretical advantages over previous methods

Mindfulness training modulates value signals in ventromedial prefrontal cortex through input from insular cortex

Neuroimaging research has demonstrated that ventromedial prefrontal cortex (vmPFC) encodes value signals that can be modulated by top-down cognitive input such as semantic knowledge, price incentives, and monetary favors suggesting that such biases may have an identified biological basis. It has been hypothesized that mindfulness training (MT) provides one path for gaining control over such top-down influences; yet, there have been no direct tests of this hypothesis. Here, we probe the behavioral and neural effects of MT on value signals in vmPFC in a randomized longitudinal design of 8weeks of MT on an initially naïve subject cohort. The impact of this within-subject training was assessed using two paradigms: one that employed primary rewards (fruit juice) in a simple conditioning task and another that used a well-validated art-viewing paradigm to test bias of monetary favors on preference. We show that MT behaviorally censors the top-down bias of monetary favors through a measurable influence on value signals in vmPFC. MT also modulates value signals in vmPFC to primary reward delivery. Using a separate cohort of subjects we show that 8weeks of active control training (ACT) generates the same behavioral impact also through an effect on signals in the vmPFC. Importantly, functional connectivity analyses show that value signals in vmPFC are coupled with bilateral posterior insula in the MT groups in both paradigms, but not in the ACT groups. These results suggest that MT integrates interoceptive input from insular cortex in the context of value computations of both primary and secondary rewards

Independent Eigenstates of Angular Momentum in a Quantum N-body System

The global rotational degrees of freedom in the Schr\"{o}dinger equation for an $N$-body system are completely separated from the internal ones. After removing the motion of center of mass, we find a complete set of $(2\ell+1)$ independent base functions with the angular momentum $\ell$. These are homogeneous polynomials in the components of the coordinate vectors and the solutions of the Laplace equation, where the Euler angles do not appear explicitly. Any function with given angular momentum and given parity in the system can be expanded with respect to the base functions, where the coefficients are the functions of the internal variables. With the right choice of the base functions and the internal variables, we explicitly establish the equations for those functions. Only (3N-6) internal variables are involved both in the functions and in the equations. The permutation symmetry of the wave functions for identical particles is discussed.Comment: 24 pages, no figure, one Table, RevTex, Will be published in Phys. Rev. A 64, 0421xx (Oct. 2001

Mindfulness training increases cooperative decision making in economic exchanges: evidence from fMRI

Emotions have been shown to exert influences on decision making during economic exchanges. Here we investigate the underlying neural mechanisms of a training regimen which is hypothesized to promote emotional awareness, specifically mindfulness training (MT). We test the hypothesis that MT increases cooperative economic decision making using fMRI in a randomized longitudinal design involving 8weeks of either MT or active control training (CT). We find that MT results in an increased willingness to cooperate indexed by higher acceptance rates to unfair monetary offers in the Ultimatum Game. While controlling for acceptance rates of monetary offers between intervention groups, subjects in the MT and CT groups show differential brain activation patterns. Specifically, a subset of more cooperative MT subjects displays increased activation in the septal region, an area linked to social attachment, which may drive the increased willingness to express cooperative behavior in the MT cohort. Furthermore, MT resulted in attenuated activity in anterior insula compared with the CT group in response to unfair monetary offers post-training, which may suggest that MT enables greater ability to effectively regulate the anterior insula and thereby promotes social cooperation. Finally, functional connectivity analyses show a coupling between the septal region and posterior insula in the MT group, suggesting an integration of interoceptive inputs. Together, these results highlight that MT may be employed in contexts where emotional regulation is required to promote social cooperation