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Background: Frontostriatal and frontoparietal abnormalities likely contribute to deficits in control and attentional processes in individuals with bulimia nervosa and to the persistence of dysregulated eating across development. This study assessed these processes and cortical thickness in a large sample of adolescent girls and women with bulimia nervosa compared with healthy controls. Methods: We collected anatomical MRI data from adolescent girls and women (ages 12-38 yr) with full or subthreshold bulimia nervosa and age-matched healthy controls who also completed the Conners Continuous Performance Test-II (CPT-II). Groups were compared on task performance and cortical thickness. Mediation analyses explored associations among cortical thickness, CPT-II variables, bulimia nervosa symptoms and age. Results: We included 60 girls and women with bulimia nervosa and 54 controls in the analyses. Compared with healthy participants, those with bulimia nervosa showed increased impulsivity and inattention on the CPT-II, along with reduced thickness of the right pars triangularis, right superior parietal and left dorsal posterior cingulate cortices. In the bulimia nervosa group, exploratory analyses revealed that binge eating frequency correlated inversely with cortical thickness of frontoparietal and insular regions and that reduced frontoparietal thickness mediated the association between age and increased symptom severity and inattention. Binge eating frequency also mediated the association between age and lower prefrontal cortical thickness. Limitations: These findings are applicable to only girls and women with bulimia nervosa, and our cross-sectional design precludes understanding of whether cortical thickness alterations precede or result from bulimia nervosa symptoms. Conclusion: Structural abnormalities in the frontoparietal and posterior cingulate regions comprising circuits that support control and attentional processes should be investigated as potential contributors to the maintenance of bulimia nervosa and useful targets for novel interventions., Introduction Bulimia nervosa is characterized by recurrent binge eating and compensatory behaviours to avoid weight gain. (1) The disorder affects 1%-3% of women and is associated with significant medical complications [...]

It is well known that least absolute deviation (LAD) criterion or [L.sub.1]-norm used for estimation of parameters is characterized by robustness, i.e., the estimated parameters are totally resistant (insensitive) to large changes in the sampled data. This is an extremely useful feature, especially, when the sampled data are known to be contaminated by occasionally occurring outliers or by spiky noise. In our previous works, we have proposed the least absolute deviation neural network (LADNN) to solve unconstrained LAD problems. The theoretical proofs and numerical simulations have shown that the LADNN is Lyapunov-stable and it can globally converge to the exact solution to a given unconstrained LAD problem. We have also demonstrated its excellent application value in time-delay estimation. More generally, a practical LAD application problem may contain some linear constraints, such as a set of equalities and/or inequalities, which is called constrained LAD problem, whereas the unconstrained LAD can be considered as a special form of the constrained LAD. In this paper, we present a new neural network called constrained least absolute deviation neural network (CLADNN) to solve general constrained LAD problems. Theoretical proofs and numerical simulations demonstrate that the proposed CLADNN is Lyapunov stable and globally converges to the exact solution to a given constrained LAD problem, independent of initial values. The numerical simulations have also illustrated that the proposed CLADNN can be used to robustly estimate parameters for nonlinear curve fitting, which is extensively used in signal and image processing. Index Terms--Equality and inequality constraints, least absolute deviation (LAD), [L.sub.1]-norm optimization, neural network (NN), nonlinear curve fitting.

The time delay estimation (TDE) is an important issue in modern signal processing and it has found extensive applications in the spatial propagation feature extraction of biomedical signals as well. Due to the extreme complexity and variability of the underlying systems, biomedical signals are usually nonstationary, unstable and even chaotic. Furthermore, due to the limitations of the measurement environments, biomedical signals are often noise-contaminated. Therefore, the TDE of biomedical signals is a challenging issue. A new TDE algorithm based on the least absolute deviation neural network (LADNN) and its application experiments are presented in this paper. The LADNN is the neural implementation of the least absolute deviation (LAD) optimization model, also called unconstrained minimum [L.sub.1]-norm model, with a theoretically proven global convergence. In the proposed LADNN-based TDE algorithm, a given signal is modeled using the moving average (MA) model. The MA parameters are estimated by using the LADNN and the time delay corresponds to the time index at which the MA coefficients have a peak. Due to the excellent features of [L.sub.1]-norm model superior to [L.sub.p]-norm (p > 1) models in non-Gaussian noise environments or even in chaos, especially for signals that contain sharp transitions (such as biomedical signals with spiky series or motion artifacts) or chaotic dynamic processes, the LADNN-based TDE is more robust than the existing TDE algorithms based on wavelet-domain correlation and those based on higher-order spectra (HOS). Unlike these conventional methods, especially the current state-of-the-art HOS-based TDE, the LADNN-based method is free of the assumption that the signal is non-Gaussian and the noises are Gaussian and, thus, it is more applicable in real situations. Simulation experiments under three different noise environments, Gaussian, non-Gaussian and chaotic, are conducted to compare the proposed TDE method with the existing HOS-based method. Real application experiment is conducted to extract time delay information between every two adjacent channels of gastric myoelectrical activity (GMA) to assess the spatial propagation characteristics of GMA during different phases of the migrating myoelectrical complex (MMC). Index Terms--Gastric myoelectrical activity and myoelectrical migrating complex (MMC), least absolute deviation (LAD), [L.sub.1]-norm optimization, neural network, time delay estimation.

Nonlinear feedback induces oscillation, whereas dynamic equilibrium between positive and negative nonlinear feedback generates rhythm. Physiological rhythms are central to life. No absolutely stable or periodic rhythm exists in living tissues. It has been extensively reported that many rhythms in human and animal organs, such as the heart and brain, are, in fact chaotic. The aim of this paper is to investigate whether the migrating myoelectricai complex (MMC) of the stomach was chaotic. The study was performed in eight healthy female hound dogs (15-22 kg), implanted with four pairs of bipolar electrodes on the serosa of the stomach along the greater curvature. After the dogs were completely recovered from the surgery, one complete cycle of gastric MMC was recorded from the serosal electrodes. Using Takens' embedding theorem, two parameters reflecting chaotic behavior, the attractor and the Lyapunov exponent of the myoelectrical recording, were reconstructed and computed, respectively. Statistical analysis was performed to investigate the difference in the Lyapunov exponents among different phases of the MMC. The results show that the MMC of the stomach is chaotic. Different phases of the MMC are characterized with different shapes of the attractors and different values of Lyapunov exponents. The characteristic chaotic behavior of the gastric MMC may be utilized for the identification of different phases. Index Terms--Chaos, electrogastrography (EGG), gastric migrating myoelectrical complex (MMC), gastric myoelectrical activity (GMA), Lyapunov exponent.

Electroacupuncture accelerates gastric emptying in association with changes in vagal activity. Am J Physiol Gastrointest Liver Physiol 282: G390-G396, 2002; 10.1152/ajpgi.00272.2001.--Gastroparesis is a disorder with a lack of treatment options and this study investigated the effect of electroacupuncture on gastric emptying and involved mechanisms. Dogs implanted with a duodenal cannula and serosal electrodes were fed with Ensure mixed with phenol red, and the gastric effluent was collected. Electroacupuncture was performed from 30 min before until 45 min after the meal. Gastric myoelectrical activity and electrocardiogram were recorded. Gastric emptying was significantly improved with electroacupuncture. Vagal activity assessed from the spectral analysis of heart rate variability was markedly increased with electroacupuncture. Electroacupuncture increased the regularity of gastric slow waves in both the proximal and distal stomach. It also increased the number of spike bursts in the distal but not proximal stomach. Electroacupuncture accelerates gastric emptying of liquid in dogs and its potential for treating gastroparesis may be explored. The effect may be attributed to improvement in gastric slow-wave rhythmicity and antral contractile (spike) activity and may possibly involve the vagal pathway. gastroparesis; gastric myoelectrical activity; heart rate variability

Least absolute deviation (LAD) optimization model, also called the unconstrained minimum [L.sub.1]-norm optimization model, has found extensive applications in linear parameter estimations. [L.sub.1]-norm model is superior to [L.sub.p]-norm (p [is greater than] 1) models in non-Gaussian noise environments or even in chaos, especially for signals that contain sharp transitions (such as biomedical signals with spiky series or motion artifacts) or chaotic dynamic processes. However, its implementation is more difficult due to discontinuous derivatives, especially compared with the least-squares model ([L.sub.2]-norm). In this paper, neural implementation of LAD optimization model is presented, where a new neural network is constructed and its performance in LAD optimization is evaluated theoretically and experimentally. Then, the application of the proposed LAD neural network (LADNN) to time delay estimation (TDE) is presented. In TDE, a given signal is modeled using the moving average (MA) model. The MA parameters are estimated by using the LADNN and the time delay corresponds to the time index at which the MA coefficients have a peak. Compared with higher order spectra (HOS)-based TDE methods, the LADNN-based method is free of the assumption that the signal is non-Gaussian and the noises are Gaussian, which is closer to real situations. Experiments under three different noise environments, Gaussian, non-Gaussian and chaotic, are conducted to compare the proposed TDE method with the existing HOS-based method. Index Terms--[L.sub.1]-norm optimization, least absolute deviation (LAD), neural network, time delay estimation.

The overcomplete signal representation (OSR) is a recently established adaptive signal representation method. As an adaptive signal representation method, the OSR means that a given signal is decomposed onto a number of optimal basis components, which are found from an overcomplete basis dictionary via some optimization algorithms, such as the matching pursuit (MP), method of frame (MOF) and basis pursuit (BP). Such ideas are actually very close to or exactly the same as solving a minimum fuel (MF) problem. The MF problem is a well-established minimum [L.sub.1]-norm optimization model with linear constraints. The BP-based OSR proposed by Chen and Donoho is exactly the same model as the MF model. The work of Chen and Donoho showed that the MF model could be used as a generalized method for solving an OSR problem and it outperformed the MP and the MOF. In this paper, the neural implementation of the MF model and its applications to the OSR are presented. A new neural network, namely the minimum fuel neural network (MFNN), is constructed and its convergence in solving the MF problem is proven theoretically and validated experimentally. Compared with the implementation of the original BP, the MFNN does not double the scales of the problem and its convergence is independent of initial conditions. It is shown that the MFNN is promising for the application in the OSR's of various kinds of nonstationary signals with a high time-frequency resolution and feasibility of real-time implementation. As an extension, a two-dimensional (2-D) MF model suitable for image data compression is also proposed and its neural implementation is presented. Index Terms--Basis pursuit (BP), [L.sub.1]-norm optimization model, matching pursuit (MP), method of frame (MOF), minimum fuel (MF) problem, neural network (NN), overcomplete signal representation (OSR), time-frequency representation (TFR), wavelet.

In this paper, a generalized fast computational algorithm for the n-dimensional discrete cosine transform (DCT) of length N = [2.sup.m] (m [greater than or equal to] 2) is presented. The developed algorithm is proved and its efficiency is evaluated theoretically. The theoretical results show that compared with the conventional method of computing the one-dimensional along n directions, the number of multiplications needed by our algorithm is only 1/n of that required by the conventional method; for the total number of additions, the latter is a bit more when N [less than or equal to] 8 and much fewer when N [greater than or equal to] 16 than the former. To validate the proposed algorithm, we take the case when n = 3 as an example and apply it to motion-picture coding. The results show that our method is superior to MPEG-2 in speed and coding performance. The algorithm is clearly described and it is easy to make a computer program for implementation. Index Terms - Discrete cosine transform (DCT), fast algorithm, motion picture coding, MPEG, multidimensional signal processing.