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Transition to drug addiction: a negative reinforcement model based on an allostatic decrease in reward function.

RATIONALE: The transition from initial drug use to drug addiction has been proposed to result from an allostatic decrease in reward function driven by an overactivation of brain antireward processes. OBJECTIVES: How decreased reward function explains compulsive drug use is not entirely clear at present, and is still a subject for debate. METHODS: We present a quantitative model of cocaine self-administration that integrates pharmacokinetic, pharmacodynamic, and motivational factors to address this question. The model assumes that reward system responsivity is a homeostatically regulated process where the desired level of responsivity (called the reward set point) is initially different from the baseline level. The reduction or correction of this difference or error in reward function would drive cocaine self-administration. RESULTS: Theoretical data obtained by computer simulation fit the experimental data obtained in animals self-administering cocaine (i.e., the within-session pattern of self-injections, the shape and curvature of the dose-injection function, the nonlinear relationship between drug intake and regulated drug effects). Importantly, simulation of an allostatic decrease in reward system responsivity exacerbates the initial error that drives self-administration, thereby increasing both the intake of, and the motivation for, the drug. This allostatic change manifests as a vertical shift in the dose-injection function similar to that seen in animals with escalating cocaine self-administration. CONCLUSIONS: The present model provides a satisfactory explanation of escalated drug intake and suggests a novel negative reinforcement view of addiction based on an allostatic decrease in reward function.

Adaptation, Physiological↗

Task-based model/human observer evaluation of SPIHT wavelet compression with human visual system-based quantization.

RATIONALE AND OBJECTIVE: The set partitioning in hierarchical trees (SPIHT) wavelet image compression algorithm with the human visual system (HVS) quantization matrix was investigated using x-ray coronary angiograms. We tested whether the HVS quantization matrix for the SPIHT wavelet compression improved computer model/human observer performance in a detection task with variable signals compared to performance with the default quantization matrix. We also tested the hypothesis of whether evaluating the rank order of the two quantization matrices (HVS versus default) based on performance of computer model observers in a signal known exactly but variable task (SKEV) generalized to model/human performance in the more clinically realistic signal known statistically task (SKS). MATERIALS AND METHODS: Nine hundred test images were created using real x-ray coronary angiograms as backgrounds and simulated arteries with filling defects (signals). The task for the model and human observer was to detect which one of the four computer simulated arterial segments contained the signal, four alternative-forced-choice (4 AFC). We obtained performance for four model observers (nonprewhitening matched filter with an eye filter, Hotelling, Channelized Hotelling, and Laguerre Gauss Hotelling model observers) for both the SKEV and SKS tasks with images compressed with and without the HVS quantization matrix. A psychophysical study measured performance from three human observers for the same conditions and tasks as the model observers. RESULTS: Performance for all four model observers improved with the use of the HVS quantization scheme. Improvements ranged from 5% (at compression ratio 7:1) to 50% (at compression ratio 30:1) for both the SKEV and SKS tasks. Human observer performance improvement averaged across observers ranged from 6% (at compression ratio 7:1) to 35% (at compression ratio 30:1) for the SKEV task and from 2% (at compression ratio 7:1) to 38% (at compression ratio 30:1) for the SKS task. Addition of internal noise to the model observers allowed for good prediction of human performance. CONCLUSIONS: Use of the HVS quantization scheme in the SPIHT wavelet compression led to improved model and human observer performance in clinically relevant detection tasks in x-ray coronary angiograms. Model observer performance can be reliably used to predict the human observer performance for the studied tasks as a function of SPIHT wavelet image compression. Our results further confirmed that model observer performance in the computationally more tractable SKEV task can be potentially used as a figure of merit for the more clinically realistic SKS task with real anatomic backgrounds.

Algorithms↗

Unsupervised clustering in mRNA expression profiles.

The development of microarray technologies gives scientists the ability to examine, discover and monitor the mRNA transcript levels of thousands of genes in a single experiment. Nonetheless, the tremendous amount of data that can be obtained from microarray studies presents a challenge for data analysis. The most commonly used computational approach for analyzing microarray data is cluster analysis, since the number of genes is usually very high compared to the number of samples. In this paper, we investigate the application of the recently proposed k-windows clustering algorithm on gene expression microarray data. This algorithm apart from identifying the clusters present in a data set also calculates their number and thus requires no special knowledge about the data. To improve the quality of the clustering, we employ various dimension reduction techniques and propose a hybrid one. The results obtained by the application of the algorithm exhibit high classification success.

Algorithms↗

A two step approach for semi-automated particle selection from low contrast cryo-electron micrographs.

Over recent years advances in cryo-electron microscopy for the study of macromolecular structure have resulted in resolutions in the range 10-15 A becoming routine. With this drive for increased resolution comes the need to collect larger datasets, commonly >10,000 particle images. Manual selection of particles from micrographs is often difficult and with such large numbers of particles now involved it is also laborious and a common bottleneck. Automated methods do exist but are normally restricted to specific samples or data, i.e., spherical particles, no aggregation, high contrast, and low noise. A two step approach has been developed that remains general and can be applied to low contrast, high noise micrographs of small molecules. Specifically, application of the approach is presented using micrographs of Escherichia coli RNA polymerase, which due to low contrast and the relatively small size of the molecule prove difficult to pick manually. To test the automated approach, independent reconstructions of RNA polymerase were carried out using manual and automatically picked data. The two reconstructions are shown to be comparable and the reconstruction from the automatically picked dataset is at a higher resolution, due to an increase in the number of particles picked.

Algorithms↗

Modeling of growing networks with directional attachment and communities.

In this paper, we propose a new network growth model and its learning algorithm to more precisely model such a real-world growing network as the Web. Unlike the conventional models, we have incorporated directional attachment and community structure for this purpose. We show that the proposed model exhibits a degree distribution with a power-law tail, which is an important characteristic of many large-scale real-world networks including the Web. Using real Web data, we experimentally show that predictive ability can be improved by incorporating directional attachment and community structure. Also, using synthetic data, we experimentally show that predictive ability can definitely be improved by incorporating community structure.

Algorithms↗

Online algorithm for the self-organizing map of symbol strings.

In this work an online algorithm is presented for the construction of the self-organizing map (SOM) of symbol strings. Each node of the SOM grid is associated with a model string which is a variable-vector sequence. Smooth interpolation method is applied in the training which performs simultaneous adaptation of the symbol content and the length of the model string. The efficiency of the method is demonstrated by the clustering of a 100,000-word English dictionary.

Algorithms↗

A hierarchical classifier using new support vector machines for automatic target recognition.

A binary hierarchical classifier is proposed for automatic target recognition. We also require rejection of non-object (non-target) inputs, which are not seen during training or validation, thus producing a very difficult problem. The SVRDM (support vector representation and discrimination machine) classifier is used at each node in the hierarchy, since it offers good generalization and rejection ability. Using this hierarchical SVRDM classifier with magnitude Fourier transform (|FT|) features, which provide shift-invariance, initial test results on infra-red (IR) data are excellent.

Algorithms↗

Generalized 2D principal component analysis for face image representation and recognition.

In the tasks of image representation, recognition and retrieval, a 2D image is usually transformed into a 1D long vector and modelled as a point in a high-dimensional vector space. This vector-space model brings up much convenience and many advantages. However, it also leads to some problems such as the Curse of Dimensionality dilemma and Small Sample Size problem, and thus produces us a series of challenges, for example, how to deal with the problem of numerical instability in image recognition, how to improve the accuracy and meantime to lower down the computational complexity and storage requirement in image retrieval, and how to enhance the image quality and meanwhile to reduce the transmission time in image transmission, etc. In this paper, these problems are solved, to some extent, by the proposed Generalized 2D Principal Component Analysis (G2DPCA). G2DPCA overcomes the limitations of the recently proposed 2DPCA (Yang et al., 2004) from the following aspects: (1) the essence of 2DPCA is clarified and the theoretical proof why 2DPCA is better than Principal Component Analysis (PCA) is given; (2) 2DPCA often needs much more coefficients than PCA in representing an image. In this work, a Bilateral-projection-based 2DPCA (B2DPCA) is proposed to remedy this drawback; (3) a Kernel-based 2DPCA (K2DPCA) scheme is developed and the relationship between K2DPCA and KPCA (Scholkopf et al., 1998) is explored. Experimental results in face image representation and recognition show the excellent performance of G2DPCA.

Algorithms↗

Batch and median neural gas.

Neural Gas (NG) constitutes a very robust clustering algorithm given Euclidean data which does not suffer from the problem of local minima like simple vector quantization, or topological restrictions like the self-organizing map. Based on the cost function of NG, we introduce a batch variant of NG which shows much faster convergence and which can be interpreted as an optimization of the cost function by the Newton method. This formulation has the additional benefit that, based on the notion of the generalized median in analogy to Median SOM, a variant for non-vectorial proximity data can be introduced. We prove convergence of batch and median versions of NG, SOM, and k-means in a unified formulation, and we investigate the behavior of the algorithms in several experiments.

Algorithms↗

Webometry: measuring the synergy of the World-Wide Web.

This is the second progress report on the webometry project: acquisition of data regarding the density of links on the world-wide web (WWW). We illustrate the primary visualization strategy, the synergy matrix, in the case of a model subnet of nine nodes.

Computer Communication Networks↗

Computer-assisted decision making in portal verification--optimization of the neural network approach.

PURPOSE: Conventional portal verification requires that a qualified radiation oncologist make decisions as to the set-up acceptability. This scheme is no longer sustainable with the large numbers of images available on-line and stringent time constraints. Therefore the objective of this study was to develop, optimize, and evaluate on clinical data an artificial intelligence decision-making tool for portal verification. The tool, based on the artificial neural network (ANN) approach, should approximate, as closely as possible, portal verification assessments made by a radiation oncologist expert. METHODS AND MATERIALS: A total of 328 electronic portal images of tangential breast irradiations were included in the study. A radiation oncologist expert evaluated these images and rated the treatment set-up acceptability on a scale from 0 to 10. Translational and rotational errors in the placement of the radiation field boundaries formed seven-dimensional feature vectors that represented each of the 328 portal images/treatments. The feature vectors were used as inputs to a three-layer, feedforward ANN. The neural network was trained on the oncologist's ratings. RESULTS: The rms discrepancy between the ANN and the expert's ratings was 1.05 rating points. Using the decision threshold equal to 5 for both sets of ratings, the ANN classifier was capable of detecting 100% of the portals classified as "unacceptable" by the expert. Only 6.5% of portals acceptable to the oncologist were misclassified as "unacceptable" by the ANN. CONCLUSION: The results of this study indicate the feasibility of using the ANN portal image classifier as an automated assistant to the radiation oncologist. Its role would be to recommend an appropriate decision as to the acceptability or otherwise of a given treatment set-up depicted in a portal image.

Breast Neoplasms↗

An economised craniofacial identification system.

It has been attempted to develop an economised craniofacial identification system, as a special automated version of photo/video superimposition technique, that can deal with common cases of personal identification with the aid of a skull and a nearly front view face photograph of the suspected victim. The proposed method is economic in respect of (i) cost of hardware configuration, (ii) processing time as well as (iii) manual labour involved. Over and above, it has got a capability to take care of ambiguities due to soft tissue thickness during the selection of facial features, which is a part of the procedure. In order to reconstruct a 2-D cranial image, superimposable over the facial one, the new method does not need any reconstruction of a digitised 3-D cranial image. It works simply by a suitable segment-wise processing of a 2-D cranial image with the aid of the symmetry perceiving adaptive neuronet (SPAN), that has recently been introduced in connection with nearly front view facial image recognition. The final comparison of the facial and the superimposable cranial images is as versatile as the same for facial image recognition by SPAN.A practical application of this extended version of SPAN has been demonstrated in the present paper.

Child↗

The enhanced LBG algorithm.

Clustering applications cover several fields such as audio and video data compression, pattern recognition, computer vision, medical image recognition, etc. In this paper, we present a new clustering algorithm called Enhanced LBG (ELBG). It belongs to the hard and K-means vector quantization groups and derives directly from the simpler LBG. The basic idea we have developed is the concept of utility of a codeword, a powerful instrument to overcome one of the main drawbacks of clustering algorithms: generally, the results achieved are not good in the case of a bad choice of the initial codebook. We will present our experimental results showing the ELBG is able to find better codebooks than previous clustering techniques and the computational complexity is virtually the same as the simpler LBG.

Algorithms↗

Unified stabilization approach to principal and minor components extraction algorithms.

Principal component and minor component extractions provide powerful techniques in many information processing fields. There have been proposed a number of algorithms for principal and minor component (or subspace) extraction, which have different dynamical behaviors. In this paper, we give rigorous stability analysis of these algorithms, obtaining a unified insight view on the dynamical behaviors of various algorithms.

Algorithms↗

Neural maps in remote sensing image analysis.

We study the application of self-organizing maps (SOMs) for the analyses of remote sensing spectral images. Advanced airborne and satellite-based imaging spectrometers produce very high-dimensional spectral signatures that provide key information to many scientific investigations about the surface and atmosphere of Earth and other planets. These new, sophisticated data demand new and advanced approaches to cluster detection, visualization, and supervised classification. In this article we concentrate on the issue of faithful topological mapping in order to avoid false interpretations of cluster maps created by an SOM. We describe several new extensions of the standard SOM, developed in the past few years: the growing SOM, magnification control, and generalized relevance learning vector quantization, and demonstrate their effect on both low-dimensional traditional multi-spectral imagery and approximately 200-dimensional hyperspectral imagery.

Image Processing, Computer-Assisted↗

Extracting multisource brain activity from a single electromagnetic channel.

This paper develops a methodology for the extraction of multisource brain activity using only single channel recordings of electromagnetic (EM) brain signals. Measured electroencephalogram (EEG) and magnetoencephalogram (MEG) signals are used to demonstrate the utility of the method on extracting multisource activity from a single channel recording. At the heart of the method is dynamical embedding (DE) where first an appropriate embedding matrix is constructed out of a series of delay vectors from the measured signal. The embedding matrix contains the information we require, but in a mixed form which therefore needs to be deconstructed. In particular, we demonstrate how one form of independent component analysis (ICA) performed on the embedding matrix can deconstruct the single channel recording into its underlying informative components. The components are treated as a convenient expansion basis and subjective methods are then used to identify components of interest relevant to the application. The framework has been applied to single channels of both EEG and MEG recordings and is shown to isolate multiple sources of activity which includes: (i) artifactual components such as ocular, electrocardiographic and electrode artefact, (ii) seizure components in epileptic EEG recordings, and (iii) theta band, tumour related, activity in MEG recordings. The results are intuitive and meaningful in a neurophysiological setting.

Brain↗

Fuzzy logic in computer-aided breast cancer diagnosis: analysis of lobulation.

This paper illustrates how a fuzzy logic approach can be used to formalize terms in the American College of Radiology (ACR) Breast Imaging Lexicon. In current practice, radiologists make a relatively subjective determination for many terms from the lexicon related to breast cancer diagnosis. Lobulation and microlobulation of nodules are two important features in the ACR lexicon. We offer an approach for formalizing the distinction of these features and also formalize the description of intermediate cases between lobulated and microlobulated masses. In this paper it is shown that fuzzy logic can be an effective tool in dealing with this kind of problem. The proposed formalization creates a basis for the next three steps (i) extended verification with blinded comparison studies. (ii) the automatic extraction of the related primitives from the image, and (iii) the detection of lobulated and microlobulated masses based on these primitives.

Breast Neoplasms↗