Search

Recently, a functional disconnectivity hypothesis of schizophrenia has been proposed for the physiological explanation of behavioral syndromes of this complex mental disorder. In this paper, we aim at further examining whether syndromes of schizophrenia could be decoded by some special spatiotemporal patterns of resting-state functional connectivity. We designed a data-driven classifier based on machine learning to extract highly discriminative functional connectivity features and to discriminate schizophrenic patients from healthy controls. The proposed classifier consisted of two separate steps. First, we used feature selection based on a correlation coefficient method to extract highly discriminative regions and construct the optimal feature set for classification. Then, an unsupervised-learning classifier combining low-dimensional embedding and self-organized clustering of fMRI was trained to discriminate schizophrenic patients from healthy controls. The performance of the classifier was tested using a leave-one-out cross-validation strategy. The experimental results demonstrated not only high classification accuracy (93.75% for schizophrenic patients, 75.0% for healthy controls), but also good generalization and stability with respect to the number of extracted features. In addition, some functional connectivities between certain brain regions of the cerebellum and frontal cortex were found to exhibit the highest discriminative power, which might provide further evidence for the cognitive dysmetria hypothesis of schizophrenia. This primary study demonstrated that machine learning could extract exciting new information from the resting-state activity of a brain with schizophrenia, which might have potential ability to improve current diagnosis and treatment evaluation of schizophrenia., (Copyright 2009 Elsevier Inc. All rights reserved.)

This paper is concerned with the global convergence of the solutions of a nonautonomous system with variable delays, arising from the description of the states of neurons in delayed Cohen-Grossberg type in a time-varying situation. By exploring intrinsic features between nonautonomous system and its asymptotic equation, several novel sufficient conditions are established to ensure that all solutions of the networks converge to a periodic function or a constant vector for delayed Cohen-Grossberg-type neural network (NN) models in time-varying situation. The results can be applied directly to group of NNs models including Hopfield NNs, bidirectional association memory NNs, and cellular NNs. Our results are not only presented in terms of system parameters and can be easily verified but also are less restrictive than previously known criteria. Numerical simulations have also been presented to demonstrate the theoretical analysis.

Recent discoveries on the way in which the cerebellum carries out higher non-motor functions, have stimulated a proliferation of researches into functional integration and neural mechanisms in the cerebellum. Cerebellar functional asymmetry is a special characteristic of cerebellar functional organization and the cerebro-cerebellar circuitry that underlies task performance. Multi-level neuroimaging studies demonstrate that cerebellar functional asymmetry has a rather complex pattern, and may be correlated with practice or certain disorders. In this review, we summarize some new and important advances in the understanding of functional laterality of the cerebellum in primary motor and higher cognitive functions, and highlight the differences in the patterns of cerebellar functional asymmetry in the various functional domains. We propose that cerebellar functional asymmetry may be associated with the pattern of connectivity between a large number of widely distributed brain areas and between special cerebellar functional regions. It is suggested that cerebro-cerebellar circuits in particular play an important role in cerebellar functional asymmetry. Finally, we propose that multi-scale connectivity analyses and careful studies of high-level cerebellar functional asymmetry would make an important contribution to the understanding of the human cerebellum and cerebral neural networks.

One of the most crucial steps for automatically reconstructing ripped-up documents is to find a globally consistent solution from the ambiguous candidate matches. However, little work has been done so far to solve this problem in a general computational framework without using application-specific features. In this paper, we propose a global approach for reconstructing ripped-up documents by first finding candidate matches from document fragments using curve matching and then disambiguating these candidates through a relaxation process to reconstruct the original document. The candidate disambiguation problem is formulated in a relaxation scheme, in which the definition of compatibility between neighboring matches is proposed and global consistency is defined as the global criterion. Initially, global match confidences are assigned to each of the candidate matches. After that, the overall local relationships among neighboring matches are evaluated by computing their global consistency. Then these confidences are iteratively updated using the gradient projection method to maximize the criterion. This leads to a globally consistent solution and thus provides a sound document reconstruction. The overall performance of our approach in several practical experiments is illustrated. The results indicate that the reconstruction of ripped-up documents up to fifty pieces is possibly accomplished automatically.

Adaptive resonance theory (ART) demonstrates how the brain learns to recognize and categorize vast amounts of information by using top-down expectations and attentional focusing. ART 3, one member of the ART family, embeds the computational properties of the chemical synapse in its search process, but it converges slowly and is lack of stability when being applied in pattern recognition and analysis. To overcome these problems, Nitric Oxide (NO), which serves as a newly discovered retrograde messenger in Long-Term Potentiation (LTP), is introduced in retrograde adaptive resonance theory (ReART) model presented in this paper. In the presented model a novel search hypothesis is proposed to incorporate angle and amplitude information of an external input vector to decide whether the input matches the long-term memory (LTM) weights of an active node or not, and the embedded NO retrograde mechanism makes the search procedure a closed loop, which improves the stability and convergence speed of the transmitter releasing mechanism in a synapse. To make the model more adaptive and practical, a forgetting mechanism is built to improve the weights updating process. Experimental results indicate that the proposed ReART model achieves low error rate, fast convergence and self-organizing weights regulation.

In this paper, we present a kernel-based least squares policy iteration (KLSPI) algorithm for reinforcement learning (RL) in large or continuous state spaces, which can be used to realize adaptive feedback control of uncertain dynamic systems. By using KLSPI, near-optimal control policies can be obtained without much a priori knowledge on dynamic models of control plants. In KLSPI, Mercer kernels are used in the policy evaluation of a policy iteration process, where a new kernel-based least squares temporal-difference algorithm called KLSTD-Q is proposed for efficient policy evaluation. To keep the sparsity and improve the generalization ability of KLSTD-Q solutions, a kernel sparsification procedure based on approximate linear dependency (ALD) is performed. Compared to the previous works on approximate RL methods, KLSPI makes two progresses to eliminate the main difficulties of existing results. One is the better convergence and (near) optimality guarantee by using the KLSTD-Q algorithm for policy evaluation with high precision. The other is the automatic feature selection using the ALD-based kernel sparsification. Therefore, the KLSPI algorithm provides a general RL method with generalization performance and convergence guarantee for large-scale Markov decision problems (MDPs). Experimental results on a typical RL task for a stochastic chain problem demonstrate that KLSPI can consistently achieve better learning efficiency and policy quality than the previous least squares policy iteration (LSPI) algorithm. Furthermore, the KLSPI method was also evaluated on two nonlinear feedback control problems, including a ship heading control problem and the swing up control of a double-link underactuated pendulum called acrobot. Simulation results illustrate that the proposed method can optimize controller performance using little a priori information of uncertain dynamic systems. It is also demonstrated that KLSPI can be applied to online learning control by incorporating an initial controller to ensure online performance.

Objective: To achieve better boundary integrities and recall accuracies for segmented liver images, use of the advanced fuzzy cellular neural network (AFCNN), as a variant of the fuzzy cellular neural network (FCNN), is proposed to effectively segment CT liver images., Materials and Methods: In order to better utilize relevant contour and gray information from liver images, we have improved the FCNN [Wang S, Wang M. A new algorithm NDA based on fuzzy cellular neural networks for white blood cell detection. IEEE Trans Inform Technol Biomed, in press], which proved to be very effective for the segmentation of microscopic white blood cell images, to create the novel neural network, AFCNN. Its convergent property and global stability are proved. Based on the FCNN-based NDA algorithm [Wang S, Wang M. A new algorithm NDA based on fuzzy cellular neural networks for white blood cell detection. IEEE Trans Inform Technol Biomed, in press], we developed the AFCNN-based NDA algorithm, which we used to segment 5 CT liver images. For comparison, we also segmented the same 5 CT liver images using the FCNN-based NDA algorithm., Results and Conclusion: : AFCNN has distinct advantages over FCNN in both boundary integrity and recall accuracy. In particular, the performance index Binary_rate is generally much higher for AFCNN than for FCNN when applied to CT liver images.

Enhancing the robustness and interpretability of a multilayer perceptron (MLP) with a sigmoid activation function is a challenging topic. As a particular MLP, additive TS-type MLP (ATSMLP) can be interpreted based on single-stage fuzzy IF-THEN rules, but its robustness will be degraded with the increase in the number of intermediate layers. This paper presents a new MLP model called cascaded ATSMLP (CATSMLP), where the ATSMLPs are organized in a cascaded way. The proposed CATSMLP is a universal approximator and is also proven to be functionally equivalent to a fuzzy inference system based on syllogistic fuzzy reasoning. Therefore, the CATSMLP may be interpreted based on syllogistic fuzzy reasoning in a theoretical sense. Meanwhile, due to the fact that syllogistic fuzzy reasoning has distinctive advantage over single-stage IF-THEN fuzzy reasoning in robustness, this paper proves in an indirect way that the CATSMLP is more robust than the ATSMLP in an upper-bound sense. Several experiments were conducted to confirm such a claim.

Ample evidence from functional magnetic resonance imaging studies has demonstrated functional brain asymmetries in the motor cortex and cerebellum within the task of finger-to-thumb (intratask), but the asymmetries across left and right hand-performed tasks of finger-to-thumb (intertask) are little known. Here, we found that intratask evoked asymmetry responses in the motor cortex and cerebellum, and interestingly in the Extra-Nuclear and insula. Intertask comparisons between left and right hand-activated images and between the asymmetries of intratasks of left and right hand, however, revealed distinct lateralization responses in the temporal cortex, extranuclear, parahippocampal gyrus, cingulate gyrus and prominently in basal ganglia. These results suggest that multiple aspects of functional asymmetries in motor organization are reflected in specific brain regions.

Voxel-based morphometry (VBM) is an objective whole-brain technique for characterizing regional cerebral volume and tissue concentration differences in structural magnetic resonance images. In the current study, we used VBM to examine possible cerebral gray matter abnormalities in patients with posttraumatic stress disorder (PTSD) due to fire. The subjects included 12 victims of a fire disaster with PTSD and 12 matched victims of the same fire without PTSD. Magnetic resonance images were obtained on a 1.5-Tesla General Electric scanner at Central South University, and an entire brain volume of 248 contiguous slices was obtained for each subject. Then, gray matter density in patients with PTSD and control groups was compared by using a VBM approach in SPM2. Group analysis was thresholded at P<0.001, uncorrected, at the voxel level. The following three regions of reduced gray matter volume were found in patients with PTSD compared with controls: left hippocampus, left anterior cingulate cortex (ACC), and bilateral insular cortex. It was concluded that there are structural abnormalities of the hippocampus, the ACC and the insular cortex in patients with PTSD due to fire.

A widely used tool for functional magnetic resonance imaging (fMRI) data analysis, statistical parametric mapping (SPM), is based on the general linear model (GLM). SPM therefore requires a priori knowledge or specific assumptions about the time courses contributing to signal changes. In contradistinction, independent component analysis (ICA) is a data-driven method based on the assumption that the causes of responses are statistically independent. Here we describe a unified method, which combines ICA, temporal ICA (tICA), and SPM for analyzing fMRI data. tICA was applied to fMRI datasets to disclose independent components, whose number was determined by the Bayesian information criterion (BIC). The resulting components were used to construct the design matrix of a GLM. Parameters were estimated and regionally-specific statistical inferences were made about activations in the usual way. The sensitivity and specificity were evaluated using Monte Carlo simulations. The receiver operating characteristic (ROC) curves indicated that the unified SPM-ICA method had a better performance. Moreover, SPM-ICA was applied to fMRI datasets from twelve normal subjects performing left and right hand movements. The areas identified corresponded to motor (premotor, sensorimotor areas and SMA) areas and were consistently task related. Part of the frontal lobe, parietal cortex, and cingulate gyrus also showed transiently task-related responses. The unified method requires less supervision than the conventional SPM and enables classical inference about the expression of independent components. Our results also suggest that the method has a higher sensitivity than SPM analyses.