Analyzing the connectivity between regions of interest: An approach based on cluster Granger causality for NRI data analysis

The identification, modeling, and analysis of interactions between nodes of neural systems in the human brain have become the aim of interest of many studies in neuroscience. The complex neural network structure and its correlations with brain functions have played a role in all areas of neuroscience, including the comprehension of cognitive and emotional processing. Indeed, understanding how information is stored, retrieved, processed, and transmitted is one of the ultimate challenges in brain research. In this context, in functional neuroimaging, connectivity analysis is a major tool for the exploration and characterization of the information flow between specialized brain regions. In most functional magnetic resonance imaging (fMRI) studies, connectivity analysis is carried out by first selecting regions of interest (ROI) and then calculating an average BOLD time series (across the voxels in each cluster). Some studies have shown that the average may not be a good choice and have suggested, as an alternative, the use of principal component analysis (PCA) to extract the principal eigen-time series from the ROI(s). In this paper, we introduce a novel approach called cluster Granger analysis (CGA) to study connectivity between ROIs. The main aim of this method was to employ multiple eigen-time series in each ROI to avoid temporal information loss during identification of Granger causality. Such information loss is inherent in averaging (e.g., to yield a single ""representative"" time series per ROI). This, in turn, may lead to a lack of power in detecting connections. The proposed approach is based on multivariate statistical analysis and integrates PCA and partial canonical correlation in a framework of Granger causality for clusters (sets) of time series. We also describe an algorithm for statistical significance testing based on bootstrapping. By using Monte Carlo simulations, we show that the proposed approach outperforms conventional Granger causality analysis (i.e., using representative time series extracted by signal averaging or first principal components estimation from ROIs). The usefulness of the CGA approach in real fMRI data is illustrated in an experiment using human faces expressing emotions. With this data set, the proposed approach suggested the presence of significantly more connections between the ROIs than were detected using a single representative time series in each ROI. (c) 2010 Elsevier Inc. All rights reserved.

Enhancing the phonological processing skills of children with specific reading disability

The present study evaluated the benefits of phonological processing skills training for children with persistent reading difficulties. Children aged between 9-14 years, identified as having a specific reading disability, participated in the study. In a series of three experiments, pedagogical issues related to length of training time, model of intervention and severity of readers' phonological processing skills deficit prior to intervention, were explored. The results indicated that improvement in poor readers' phonological processing skills led to a dramatic improvement in their reading accuracy and reading comprehension performance. Increasing the length of training time significantly improved transfer effects to the reading process. Children with particularly severe phonological processing skill deficits benefited from art extended training period, and both individual and group intervention models for phonological processing training proved successful. Implications for speech and language therapists are discussed.

Structure optimization combining soft-core interaction functions, the diffusion equation method, and molecular dynamics

Smoothing the potential energy surface for structure optimization is a general and commonly applied strategy. We propose a combination of soft-core potential energy functions and a variation of the diffusion equation method to smooth potential energy surfaces, which is applicable to complex systems such as protein structures; The performance of the method was demonstrated by comparison with simulated annealing using the refinement of the undecapeptide Cyclosporin A as a test case. Simulations were repeated many times using different initial conditions and structures since the methods are heuristic and results are only meaningful in a statistical sense.

Hyperplane navigation: A method to set individual scores in fMRI group datasets

Recent studies have demonstrated that spatial patterns of fMRI BOLD activity distribution over the brain may be used to classify different groups or mental states. These studies are based on the application of advanced pattern recognition approaches and multivariate statistical classifiers. Most published articles in this field are focused on improving the accuracy rates and many approaches have been proposed to accomplish this task. Nevertheless, a point inherent to most machine learning methods (and still relatively unexplored in neuroimaging) is how the discriminative information can be used to characterize groups and their differences. In this work, we introduce the Maximum Uncertainty Linear Discrimination Analysis (MLDA) and show how it can be applied to infer groups` patterns by discriminant hyperplane navigation. In addition, we show that it naturally defines a behavioral score, i.e., an index quantifying the distance between the states of a subject from predefined groups. We validate and illustrate this approach using a motor block design fMRI experiment data with 35 subjects. (C) 2008 Elsevier Inc. All rights reserved.

Assessment of factors that confound MRI and neuropathological correlation of human postmortem brain tissue

In spite of considerable technical advance in MRI techniques, the optical resolution of these methods are still limited. Consequently, the delineation of cytoarchitectonic fields based on probabilistic maps and brain volume changes, as well as small-scale changes seen in MRI scans need to be verified by neuronanatomical/neuropathological diagnostic tools. To attend the current interdisciplinary needs of the scientific community, brain banks have to broaden their scope in order to provide high quality tissue suitable for neuroimaging- neuropathology/anatomy correlation studies. The Brain Bank of the Brazilian Aging Brain Research Group (BBBABSG) of the University of Sao Paulo Medical School (USPMS) collaborates with researchers interested in neuroimaging-neuropathological correlation studies providing brains submitted to postmortem MRI in-situ. In this paper we describe and discuss the parameters established by the BBBABSG to select and to handle brains for fine-scale neuroimaging-neuropathological correlation studies, and to exclude inappropriate/unsuitable autopsy brains. We tried to assess the impact of the postmortem time and storage of the corpse on the quality of the MRI scans and to establish fixation protocols that are the most appropriate to these correlation studies. After investigation of a total of 36 brains, postmortem interval and low body temperature proved to be the main factors determining the quality of routine MRI protocols. Perfusion fixation of the brains after autopsy by mannitol 20% followed by formalin 20% was the best method for preserving the original brain shape and volume, and for allowing further routine and immunohistochemical staining. Taken to together, these parameters offer a methodological progress in screening and processing of human postmortem tissue in order to guarantee high quality material for unbiased correlation studies and to avoid expenditures by post-imaging analyses and histological processing of brain tissue.

Validation of a Spectrophotometric Method for Quantification of Carboxyhemoglobin

Universidade de São Paulo - LIM[40/HC-FM-USP]

Scanning electron microscopy as an auxiliary method in the study of exhumed bones

Scanning electronmicroscopy (SEM) has been used in forensic science in many ways. The reports of cases in which SEM has been used as an auxiliary method in the investigation of exhumed bones are rare. In this article, we report an exhumation that was made to determine if a seized weapon could have been used in a homicide. We used SEM to analyze a fracture in the interior of the skull of the victim. The findings described in this article showed us that it is possible to develop new researches in this field. (C) 2010 Elsevier Ireland Ltd. All rights reserved.

A method to immunolabel rodent spinal cord neurons and glia for molecular study in specific laser microdissected cells involved in neurodegenerative disorders

Neuron-glia interaction is involved in physiological function of neurons, however, recent evidences have suggested glial cells as participants in neurotoxic and neurotrophic mechanisms of neurodegenerative/neuroregenerative processes. Laser microdissection offers a unique opportunity to study molecular regulation in specific immunolabeled cell types. However, an adequate protocol to allow morphological and molecular analysis of rodent spinal cord astrocyte, microglia and motoneurons remains a big challenge. In this paper we present a quick method to immunolabel those cells in flash frozen sections to be used in molecular biology analyses after laser microdissection and pressure catapulting.

Does the degree of smoking effect the severity of tardive dyskinesia? A longitudinal clinical trial

Background. - Tardive dyskinesia (TD) is a movement disorder observed after chronic neuroleptic treatment. Smoking is presumed to increase the prevalence of TD. The question of a cause-effect-relationship between smoking and TD, however, remains to be answered. Purpose of this study was to examine the correlation between the degree of smoking and the severity of TD with respect to differences caused by medication. Method. - We examined 60 patients suffering from schizophrenia and TD, We compared a clozapine-treated group With a group treated with typical neuroleptics. Movement disorders were assessed using the Abnormal-Involuntary-Movement-Scale and the technical device digital image processing, providing rater independent information on perioral movements. Results. - We found a strong correlation (.80 < r < .90, always p < .0001) between the degree of smoking and severity of TD. Repeated measurements revealed a positive correlation between changes in cigarette consumption and changes of the severity of TD (p < .0001). Analyses of covariance indicated a significant group-effect with a lower severity of TD in the clozapine-group compared to the typical-neuroleptics-group (p = .010). Interaction-analyses indicated a higher impact of smoking oil the severity of TD in the typical-neuroleptics-group compared to the clozapine-group (p = .033). Conclusion. - Concerning a possible cause-effect-relationship between smoking and TD, smoking is more of a general health hazard than neuroleptic exposure in terms of TD. (C) 2008 Elsevier Masson SAS. All rights reserved.

Classification System of Raman Spectra using Cluster Analysis to Diagnose Coronary Artery Lesions

The traditional methods employed to detect atherosclerotic lesions allow for the identification of lesions; however, they do not provide specific characterization of the lesion`s biochemistry. Currently, Raman spectroscopy techniques are widely used as a characterization method for unknown substances, which makes this technique very important for detecting atherosclerotic lesions. The spectral interpretation is based on the analysis of frequency peaks present in the signal; however, spectra obtained from the same substance can show peaks slightly different and these differences make difficult the creation of an automatic method for spectral signal analysis. This paper presents a signal analysis method based on a clustering technique that allows for the classification of spectra as well as the inference of a diagnosis about the arterial wall condition. The objective is to develop a computational tool that is able to create clusters of spectra according to the arterial wall state and, after data collection, to allow for the classification of a specific spectrum into its correct cluster.

Prefrontal cortical and striatal activity to happy and fear faces in bipolar disorder is associated with comorbid substance abuse and eating disorder

Background: The spectrum approach was used to examine contributions of comorbid symptom dimensions of substance abuse and eating disorder to abnormal prefrontal-cortical and subcortical-striatal activity to happy and fear faces previously demonstrated in bipolar disorder (BD). Method: Fourteen remitted BD-type I and sixteen healthy individuals viewed neutral, mild and intense happy and fear faces in two event-related fMRI experiments. All individuals completed Substance-Use and Eating-Disorder Spectrum measures. Region-of-Interest analyses for bilateral prefrontal and subcortical-striatal regions were performed. Results: BD individuals scored significantly higher on these spectrum measures than healthy individuals (p<0.05), and were distinguished by activity in prefrontal and subcortical-striatal regions. BD relative to healthy individuals showed reduced dorsal prefrontal-cortical activity to all faces. Only BD individuals showed greater subcortical-striatal activity to happy and neutral faces. In BD individuals, negative correlations were shown between substance use severity and right PFC activity to intense happy faces (p<0.04), and between substance use severity and right caudate nucleus activity to neutral faces (p<0.03). Positive correlations were shown between eating disorder and right ventral putamen activity to intense happy (p<0.02) and neutral faces (p<0.03). Exploratory analyses revealed few significant relationships between illness variables and medication upon neural activity in BID individuals. Limitations: Small sample size of predominantly medicated BD individuals. Conclusion: This study is the first to report relationships between comorbid symptom dimensions of substance abuse and eating disorder and prefrontal-cortical and subcortical-striatal activity to facial expressions in BD. Our findings suggest that these comorbid features may contribute to observed patterns of functional abnormalities in neural systems underlying mood regulation in BD. (C) 2009 Elsevier B.V. All rights reserved.