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White matter tracts, which play a crucial role in the coordination of information flow between different regions of grey matter, are particularly vulnerable to multiple sclerosis. Many studies have shown that the white matter lesions in multiple sclerosis are associated with focal abnormalities of grey matter, but little is known about the alterations in the coordinated patterns of cortical morphology among regions in the disease. Here, we used cortical thickness measurements from structural magnetic resonance imaging to investigate the relationship between the white matter lesion load and the topological efficiency of structural cortical networks in multiple sclerosis. Network efficiency was defined using a ‘small-world’ network model that quantifies the effectiveness of information transfer within brain networks. In this study, we first classified patients (n = 330) into six subgroups according to their total white matter lesion loads, and identified structural brain networks for each multiple sclerosis group by thresholding the corresponding inter-regional cortical thickness correlation matrix, followed by a network efficiency analysis with graph theoretical approaches. The structural cortical networks in multiple sclerosis demonstrated efficient small-world architecture regardless of the lesion load, an organization that maximizes the information processing at a relatively low wiring cost. However, we found that the overall small-world network efficiency in multiple sclerosis was significantly disrupted in a manner proportional to the extent of total white matter lesions. Moreover, regional efficiency was also significantly decreased in specific brain regions, including the insula and precentral gyrus as well as regions of prefrontal and temporal association cortices. Finally, we showed that the lesions also altered many cortical thickness correlations in the frontal, temporal and parietal lobes. Our results suggest that the white matter lesions in multiple sclerosis might be associated with aberrant neuronal connectivity among widely distributed brain regions, and provide structural (morphological) evidence for the notion of multiple sclerosis as a disconnection syndrome. [ABSTRACT FROM PUBLISHER]

Electrophysiological (EEG/MEG) imaging challenges statistics by providing two views of the same underlying spatio-temporal brain activity: a topographic view (EEG/MEG) and tomographic view (EEG/MEG source reconstructions). It is a common practice that statistical parametric mapping (SPM) for these two situations is developed separately. In particular, assessing statistical significance of functional connectivity is a major challenge in these types of studies. This work introduces statistical tests for assessing simultaneously the significance of spatio-temporal correlation structure between ERP/ERF components as well as that of their generating sources. We introduce a greatest root statistic as the multivariate test statistic for detecting functional connectivity between two sets of EEG/MEG measurements at a given time instant. We use some new results in random field theory to solve the multiple comparisons problem resulting from the correlated test statistics at each time instant. In general, our approach using the union-intersection (UI) principle provides a framework for hypothesis testing about any linear combination of sensor data, which allows the analysis of the correlation structure of both topographic and tomographic views. The performance of the proposed method is illustrated with real ERP data obtained from a face recognition experiment. Hum Brain Mapp 2009. © 2009 Wiley-Liss, Inc. [ABSTRACT FROM AUTHOR]

Brain mapping data have been modeled as Gaussian random fields, and local increases in mean are detected by local maxima of a random field of test statistics derived from these data. Accurate p values for local maxima are readily available for isotropic data based on the expected Euler characteristic of the excursion Set of the test statistic random field. In this article we give a simple method for dealing with nonisotropic data. Our approach has connections to the model of Sampson and Guttorp for nonisotropy in which there exists an unknown mapping of the support of the data to a space in which the random fields are isotropic. Heuristic justification for our approach comes from the Nash embedding theorem. Formally, we show that our method gives consistent unbiased estimators for the true p values based on new results of Taylor and Adler for random fields on manifolds, which replace the Euclidean metric by the variogram. The results are used to detect gender differences in the cortical thickness of the brain and to detect regions of the brain involved in sentence comprehension measured by functional magnetic resonance imaging. [ABSTRACT FROM AUTHOR]

Patients with Parkinson's disease (PD) exhibit impairments in several cognitive functions similar to those observed in patients with prefrontal cortex (PFC) lesions. The physiological origins of these cognitive deficits are not well documented. Two mechanisms have been proposed: disruptions in corticostriatal circuits or a deficiency in frontal dopamine. We previously used functional magnetic resonance imaging (fMRI) in young healthy subjects to separate patterns of PFC and striatum activity during distinct phases of performance of the Wisconsin Card Sorting Task, a set-shifting task that reveals deficits in patients with PD. Here, the same fMRI protocol was used in PD patients and matched controls. Decreased activation was observed in the PD group compared with the matched control group in the ventrolateral PFC when receiving negative feedback and the posterior PFC when matching after negative feedback. In controls, these prefrontal regions specifically coactivated with the striatum during those stages of task performance. In contrast, greater activation was found in the PD group compared with the matched control group in prefrontal regions, such as the posterior and the dorsolateral PFC when receiving positive or negative feedback, that were not coactivated with the striatum in controls. These results suggest that both nigrostriatal dopamine depletion and intracortical dopamine deficiency may play a role in cognitive deficits in PD, depending on the involvement of the striatum in the task at hand. [ABSTRACT FROM AUTHOR]

Examines the testing of signals with unknown location and scale in a Gaussian random field defined on R[supN]. Result of the test statistic in a Gaussian random field in a dimensional space; Similarity of the scale space to a continuous wavelet transform; Use of methods to derive an approximate null distribution.

In van der Heijden and de Leeuw (1985) it was proposed to use loglinear analysis to detect interactions in a multiway contingency table, and to explore the form of these interactions with correspondence analysis. After performing the exploratory phase of the analysis, we will show here how the results found in this phase can be used for confirmation. [ABSTRACT FROM AUTHOR]

The article describes a study that tested lung functions of firefighters who were exposed to toxic fumes during the Mississauga, Ontario rail derailment fire in 1979. For the statistical data, classical multivariate methods were used for analysis, while the GENSTAT statistical system was used for calculations. The study concluded that the lung volumes for toxic fumes of the Mississsauga firefighters were less than those of Etobicoke firefighters right after the accident then returned to normal after two years.

Presents information about the use of different signal processing techniques for clinical applications and for the progress of scientific knowledge of the human brain. Details of various technologies such as MEG (Magnetoencephalography), EEG (Electroencephalography), and PET (positron emission tomography); Remarks on the merits and demerits of these technologies; Information on the journal "IEEE Transactions on Signal Processing."