Scanning patients with tasks they can perform.

We present an overview of the types of imaging experiments that can be performed on psychologically impaired patients. The critical observation from such studies is a differential pattern of activation in the patients and normals. Underactivity is interpretable only when the patients make normal responses. In this context, a failure to activate a component region of the normal system implies that this region was not necessary for task performance. Overactivity indicates either cognitive or neuronal reorganisation. Neuronal reorganisation is indicated only if the patient performs the task using the same set of cognitive operations as normal subjects. Cognitive reorganisation can be demonstrated if the same activation pattern is elicited by normals when they are co-erced into using the same cognitive implementation as the patient. We conclude that the interpretation of neuroimaging studies of psychologically impaired patients depends on intact task performance and a detailed task analysis. When these criteria are met, patient studies can be used to identify: (1) necessary and sufficient brain systems, (2) dysfunction at sites distant to damage, (3) peri-damage activation, and (4) compensation either at a neuronal level when pre-existing cognitive strategies are re-instantiated using duplicated neuronal systems (degeneracy), or at a cognitive level when alternative cognitive strategies (and their corresponding brain systems) are adopted.     

MEG source localization under multiple constraints: an extended Bayesian framework

To use Electroencephalography (EEG) and Magnetoencephalography (MEG) as functional brain 3D imaging techniques, identifiable distributed source models are required. The reconstruction of EEG/MEG sources rests on inverting these models and is ill-posed because the solution does not depend continuously on the data and there is no unique solution in the absence of prior information or constraints. We have described a general framework that can account for several priors in a common inverse solution. An empirical Bayesian framework based on hierarchical linear models was proposed for the analysis of functional neuroimaging data [Friston, K., Penny, W., Phillips, C., Kiebel, S., Hinton, G., Ashburner, J., 2002. Classical and Bayesian inference in neuroimaging: theory. NeuroImage 16, 465-483] and was evaluated recently in the context of EEG [Phillips, C., Mattout, J., Rugg, M.D., Maquet, P., Friston, K., 2005. An empirical Bayesian solution to the source reconstruction problem in EEG. NeuroImage 24, 997-1011]. The approach consists of estimating the expected source distribution and its conditional variance that is constrained by an empirically determined mixture of prior variance components. Estimation uses Expectation-Maximization (EM) to give the Restricted Maximum Likelihood (ReML) estimate of the variance components (in terms of hyperparameters) and the Maximum A Posteriori (MAP) estimate of the source parameters. In this paper, we extend the framework to compare different combinations of priors, using a second level of inference based on Bayesian model selection. Using Monte-Carlo simulations, ReML is first compared to a classic Weighted Minimum Norm (WMN) solution under a single constraint. Then, the ReML estimates are evaluated using various combinations of priors. Both standard criterion and ROC-based measures were used to assess localization and detection performance. The empirical Bayes approach proved useful as: (1) ReML was significantly better than WMN for single priors; (2) valid location priors improved ReML source localization; (3) invalid location priors did not significantly impair performance. Finally, we show how model selection, using the log-evidence, can be used to select the best combination of priors. This enables a global strategy for multiple prior-based regularization of the MEG/EEG source reconstruction.     

Dynamic causal modeling with neural fields

The aim of this paper is twofold: first, to introduce a neural field model motivated by a well-known neural mass model; second, to show how one can estimate model parameters pertaining to spatial (anatomical) properties of neuronal sources based on EEG or LFP spectra using Bayesian inference. Specifically, we consider neural field models of cortical activity as generative models in the context of dynamic causal modeling (DCM). This paper considers the simplest case of a single cortical source modeled by the spatiotemporal dynamics of hidden neuronal states on a bounded cortical surface or manifold. We build this model using multiple layers, corresponding to cortical lamina in the real cortical manifold. These layers correspond to the populations considered in classical (Jansen and Rit) neural mass models. This allows us to formulate a neural field model that can be reduced to a neural mass model using appropriate constraints on its spatial parameters. In turn, this enables one to compare and contrast the predicted responses from equivalent neural field and mass models respectively. We pursue this using empirical LFP data from a single electrode to show that the parameters controlling the spatial dynamics of cortical activity can be recovered, using DCM, even in the absence of explicit spatial information in observed data.     

Interleukin-3 treatment of rhesus monkeys leads to increased production of histamine-releasing cells that express interleukin-3 receptors at high levels

To understand the hematopoietic and nonhematopoietic responses to interleukin-3 (IL-3), expression of cell-surface IL-3 receptors (IL-3R) was examined on bone marrow (BM) cells and peripheral blood (PB) cells of rhesus monkeys during the course of in vivo IL-3 treatment. Whereas IL-3R expression is low in untreated monkeys, IL-3 administration led to a gradual increase in both low- and high-affinity binding sites for IL-3. This increase reflected the total number of cells expressing IL- 3Rs, as detected by flow cytometry using biotinylated IL-3. Most of these IL-3R+ cells in both BM and PB could be characterized as basophilic granulocytes that contained high levels of histamine. In contrast to the effect on these differentiated cells, IL-3 administration did not significantly alter the low level IL-3R expression on immature, CD34+ cells. Further flow cytometric analysis using biotinylated growth factors showed that the IL-3R+ basophils also expressed receptors for granulocyte-macrophage colony-stimulating factor (GM-CSF), but not for IL-6 or Kit ligand. These findings indicated that the IL-3R+ cells included neither monocytes, which express GM-CSFRs and IL-6Rs abundantly, nor mast cells, which express c- kit. By combining flow cytometric and Scatchard data, it was calculated that the basophils contain as many as 1 to 2 x 10(3) high-affinity IL- 3Rs and 15 to 30 x 10(3) low-affinity sites. The finding that in vivo IL-3 treatment leads to the production of large numbers of cells that express high levels of IL-3R and are capable of producing histamine provides an explanation for the often severe allergic reactions that occur during prolonged IL-3 administration. It also indicates that IL- 3, in addition to its direct effects on hematopoietic cells, may also stimulate hematopoiesis through the release of secondary mediators such as histamine by IL-3-responsive mature cells.     

Bayesian fMRI time series analysis with spatial priors

We describe a Bayesian estimation and inference procedure for fMRI time series based on the use of General Linear Models (GLMs). Importantly, we use a spatial prior on regression coefficients which embodies our prior knowledge that evoked responses are spatially contiguous and locally homogeneous. Further, using a computationally efficient Variational Bayes framework, we are able to let the data determine the optimal amount of smoothing. We assume an arbitrary order Auto-Regressive (AR) model for the errors. Our model generalizes earlier work on voxel-wise estimation of GLM-AR models and inference in GLMs using Posterior Probability Maps (PPMs). Results are shown on simulated data and on data from an event-related fMRI experiment.     

Molecular characterization of human factor XSan Antonio

Enzymatic amplification technique was used to isolate all eight exons and sequences around the splice junctions, putative promoter, and polyadenylation sites of human factor X DNA from a patient with factor X deficiency. Two genetic changes in factor X have been observed in this patient. The patient is most likely a compound heterozygote since there is only 14% activity associated with factor X. A point mutation that resulted in the substitution of cysteine (TGC) for arginine (CGC) at amino acid 366 was found in exon VIII of one allele of the factor X gene. This mutation, which occurs in the catalytic domain, can affect the formation of a disulfide bridge and thus could result in a reduction in factor X activity. Sequencing all the regions revealed a second mutation: a deletion of one nucleotide (TCCT to TCT) in exon VII that would cause a frame shift at amino acid 272 followed by termination. We have also shown that the point mutation in exon VIII creates an ApaL1 restriction site and destroys the HinP1 site. Enzymatic DNA amplification followed by restriction digestion provides a quick, reliable, and sensitive method for carrier detection and antenatal diagnosis in affected kindreds. This is the first characterization of factor X deficiency at the molecular level. We propose to name this mutation Factor XSan Antonio.     

Silent myocardial ischemia: Current perspectives and future directions

Silent myocardial ischemia (SMI) is increasingly being recognized as part of the spectrum of ischemic heart disease. The spectrum of SMI ranges from asymptomatic coronary artery disease to critical illness necessitating intensive care. Although many diagnostic tools have been used to identify low- and high-risk subgroups, their use is limited by modest sensitivities and specificities. The present review identifies current concepts in the management of SMI in various clinical settings, as well as emerging technologies that may simplify the diagnosis and treatment of this condition.