Most methods for structure-function analysis of the brain in medical images are usually based on voxel-wise statistical tests performed on registered magnetic resonance (MR) images across subjects. A major drawback of such methods is the inability to accurately locate regions that manifest nonlinear associations with clinical variables. In this paper, we propose Bayesian morphological analysis methods, based on a Bayesian-network representation, for the analysis of MR brain images. First, we describe how Bayesian networks (BNs) can represent probabilistic associations among voxels and clinical (function) variables. Second, we present a model-selection framework, which generates a BN that captures structure-function relationships from MR brain images and function variables. We demonstrate our methods in the context of determining associations between regional brain atrophy (as demonstrated on MR images of the brain), and functional deficits. We employ two data sets for this evaluation: the first contains MR images of 11 subjects, where associations between regional atrophy and a functional deficit are almost linear; the second data set contains MR images of the ventricles of 84 subjects, where the structure-function association is nonlinear. Our methods successfully identify voxel-wise morphological changes that are associated with functional deficits in both data sets, whereas standard statistical analysis (i.e., t-test and paired t-test) fails in the nonlinear-association case.

Information processing in the brain is believed to require coordinated activity across many neurons. With the recent development of techniques for simultaneously recording the spiking activity of large numbers of individual neurons, the search for complex multicell firing patterns that could help reveal this neural code has become possible. Here we develop a new approach for analyzing sequential firing patterns involving an arbitrary number of neurons based on relative firing order. Specifically, we develop a combinatorial method for quantifying the degree of matching between a "reference sequence" of N distinct "letters" (representing a particular target order of firing by N cells) and an arbitrarily long "word" composed of any subset of those letters including repeats (representing the relative time order of spikes in an arbitrary firing pattern). The method involves computing the probability that a random permutation of the word’s letters would by chance alone match the reference sequence as well as or better than the actual word does, assuming all permutations were equally likely. Lower probabilities thus indicate better matching. The overall degree and statistical significance of sequence matching across a heterogeneous set of words (such as those produced during the course of an experiment) can be computed from the corresponding set of probabilities. This approach can reduce the sample size problem associated with analyzing complex firing patterns. The approach is general and thus applicable to other types of neural data beyond multiple spike trains, such as EEG events or imaging signals from multiple locations. We have recently applied this method to quantify memory traces of sequential experience in the rodent hippocampus during slow wave sleep.

The importance of auditory feedback in the development of spoken language in humans is striking. Paradoxically, although auditory-feedback-dependent vocal plasticity has been shown in a variety of taxonomic groups, there is little evidence that our nearest relatives–non-human primates–require auditory feedback for the development of species-typical vocal signals. Because of the apparent lack of developmental plasticity in the vocal production system, neuroscientists have largely ignored the neural mechanisms of non-human primate vocal production and perception. Recently, the absence of evidence for vocal plasticity from developmental studies has been contrasted with evidence for vocal plasticity in adults. We argue that this new evidence makes non-human primate vocal behavior an attractive model system for neurobiological analysis.

We describe a methodology for rapid experimentation in statistical machine translation which we use to add a large number of features to a baseline system exploiting features from a wide range of levels of syntactic representation. Feature values were combined in a log-linear model to select the highest scoring candidate translation from an n-best list. Feature weights were optimized directly against the BLEU evaluation metric on held-out data. We present results for a small selection of features at each level of syntactic representation.

The proteasome is the main ATP-dependent protease in eukaryotic cells and controls the concentration of many regulatory proteins in the cytosol and nucleus. Proteins are targeted to the proteasome by the covalent attachment of polyubiquitin chains. The ubiquitin modification serves as the proteasome recognition element but by itself is not sufficient for efficient degradation of folded proteins. We report that proteolysis of tightly folded proteins is accelerated greatly when an unstructured region is attached to the substrate. The unstructured region serves as the initiation site for degradation and is hydrolyzed first, after which the rest of the protein is digested sequentially. These results identify the initiation site as a novel component of the targeting signal, which is required to engage the proteasome unfolding machinery efficiently. The proteasome degrades a substrate by first binding to its ubiquitin modification and then initiating unfolding at an unstructured region.

Aquaporin-0 (AQP0), previously known as major intrinsic protein (MIP), is the only water pore protein expressed in lens fiber cells. AQP0 is highly specific to lens fiber cells and constitutes the most abundant intrinsic membrane protein in these cells. The protein is initially expressed as a full-length protein in young fiber cells in the lens cortex, but becomes increasingly cleaved in the lens core region. Reconstitution of AQP0 isolated from the core of sheep lenses containing a proportion of truncated protein, produced double-layered two-dimensional (2D) crystals, which displayed the same dimensions as the thin 11 nm lens fiber cell junctions, which are prominent in the lens core. In contrast reconstitution of full-length AQP0 isolated from the lens cortex reproducibly yielded single-layered 2D crystals. We present electron diffraction patterns and projection maps of both crystal types. We show that cleavage of the intracellular C terminus enhances the adhesive properties of the extracellular surface of AQP0, indicating a conformational change in the molecule. This change of function of AQP0 from a water pore in the cortex to an adhesion molecule in the lens core constitutes another manifestation of the gene sharing concept originally proposed on the basis of the dual function of crystallins.

The lens-specific water pore aquaporin-0 (AQP0) is the only aquaporin known to form membrane junctions in vivo. We show here that AQP0 from the lens core, containing some carboxy-terminally cleaved AQP0, forms double-layered crystals that recapitulate in vivo junctions. We present the structure of the AQP0 membrane junction as determined by electron crystallography. The junction is formed by three localized interactions between AQP0 molecules in adjoining membranes, mainly mediated by proline residues conserved in AQP0s from different species but not present in most other aquaporins. Whereas all previously determined aquaporin structures show the pore in an open conformation, the water pore is closed in AQP0 junctions. The water pathway in AQP0 also contains an additional pore constriction, not seen in other known aquaporin structures, which may be responsible for pore gating.

We investigate rules that govern neuronal arborization, speci%cally the local geometry of the

bifurcation of a neurite into its sub-branches. In the present study we set out to determine

the relationship between branch diameter and angle. Existing theories are based on minimizing a

neuronal volume cost function, or, alternatively, on the equilibrium of mechanical tension forces,

whichdepend on branchdiameters. Our experimental results utilizing two-dimensional cultured

neural networks partly corroborate both the volume optimization principles and the tension theory.

Deviation from pure tension forces equilibrium is explained by an additional force exerted by

the anchoring of the junction to the substrate.

In the CA1 region of the hippocampus, LTP is thought to be initiated by a transient activation of NMDA receptors and is expressed as a persistent increase in synaptic transmission through AMPA receptors. To investigate the postsynaptic modifications of AMPA receptors involved in this enhanced synaptic transmission, the channel density and single-channel properties of extrasynaptic AMPA receptors located in synaptically active dendritic regions were examined following the induction of LTP. Following tetanic stimulation an outside-out patch was excised from the apical dendrite near the point of stimulation and saturating concentrations of glutamate were rapidly applied to the patch. AMPA current amplitude and duration were increased significantly in patches pulled from dendrites that expressed LTP. Non-stationary fluctuation analysis of AMPA currents indicated that AMPA channel number was nearly twofold larger than in controls, while single channel conductance and maximum open-probability were unchanged. Furthermore, while subtle changes in AMPA channel kinetics could also be observed, we did not find any evidence that receptor affinity or rectification properties were altered by LTP induction. Very similar results were found when CaMK-II activity was increased through the intracellular application of Ca/CaM. Together, we interpret our data to indicate that the stimuli used here produce an increased delivery of AMPA receptors to synaptically active regions of the apical dendrite without inducing any significant changes in their basic biophysical properties and that such delivery is a key element in this form of synaptic plasticity.

Brain function relies on specificity of synaptic connectivity patterns among different classes of neurons. Yet, the substrates of specificity in complex neuropil remain largely unknown. We search for imprints of specificity in the layout of axonal and dendritic arbors from the rat neocortex. An analysis of 3D reconstructions of pairs consisting of pyramidal cells (PCs) and GABAergic interneurons (GIs) revealed that the layout of GI axons is specific. This specificity is manifested in a relatively high tortuosity, small branch length of these axons, and correlations of their trajectories with the positions of postsynaptic neuron dendrites. Axons of PCs show no such specificity, usually taking a relatively straight course through neuropil. However, wiring patterns among PCs hold a large potential for circuit remodeling and specificity through growth and retraction of dendritic spines. Our results define distinct class-specific rules in establishing synaptic connectivity, which could be crucial in formulating a canonical cortical circuit.