Functional and structural mapping of human cerebral cortex: Solutions are in the surfaces

The human cerebral cortex is notorious for the depth and irregularity of its convolutions and for its variability from one individual to the next. These complexities of cortical geography have been a chronic impediment to studies of functional specialization in the cortex. In this report, we discuss ways to compensate for the convolutions by using a combination of strategies whose common denominator involves explicit reconstructions of the cortical surface. Surface-based visualization involves reconstructing cortical surfaces and displaying them, along with associated experimental data, in various complementary formats (including three-dimensional native configurations, two-dimensional slices, extensively smoothed surfaces, ellipsoidal representations, and cortical flat maps). Generating these representations for the cortex of the Visible Man leads to a surface-based atlas that has important advantages over conventional stereotaxic atlases as a substrate for displaying and analyzing large amounts of experimental data. We illustrate this by showing the relationship between functionally specialized regions and topographically organized areas in human visual cortex. Surface-based warping allows data to be mapped from individual hemispheres to a surface-based atlas while respecting surface topology, improving registration of identifiable landmarks, and minimizing unwanted distortions. Surface-based warping also can aid in comparisons between species, which we illustrate by warping a macaque flat map to match the shape of a human flat map. Collectively, these approaches will allow more refined analyses of commonalities as well as individual differences in the functional organization of primate cerebral cortex.

Anatomy of word and sentence meaning

Reading and listening involve complex psychological processes that recruit many brain areas. The anatomy of processing English words has been studied by a variety of imaging methods. Although there is widespread agreement on the general anatomical areas involved in comprehending words, there are still disputes about the computations that go on in these areas. Examination of the time relations (circuitry) among these anatomical areas can aid in understanding their computations. In this paper, we concentrate on tasks that involve obtaining the meaning of a word in isolation or in relation to a sentence. Our current data support a finding in the literature that frontal semantic areas are active well before posterior areas. We use the subject’s attention to amplify relevant brain areas involved either in semantic classification or in judging the relation of the word to a sentence to test the hypothesis that frontal areas are concerned with lexical semantics and posterior areas are more involved in comprehension of propositions that involve several words.

The candidate tumor suppressor gene, RASSF1A, from human chromosome 3p21.3 is involved in kidney tumorigenesis

Clear cell-type renal cell carcinomas (clear RCC) are characterized almost universally by loss of heterozygosity on chromosome 3p, which usually involves any combination of three regions: 3p25-p26 (harboring the VHL gene), 3p12-p14.2 (containing the FHIT gene), and 3p21-p22, implying inactivation of the resident tumor-suppressor genes (TSGs). For the 3p21-p22 region, the affected TSGs remain, at present, unknown. Recently, the RAS association family 1 gene (isoform RASSF1A), located at 3p21.3, has been identified as a candidate lung and breast TSG. In this report, we demonstrate aberrant silencing by hypermethylation of RASSF1A in both VHL-caused clear RCC tumors and clear RCC without VHL inactivation. We found hypermethylation of RASSF1A's GC-rich putative promoter region in most of analyzed samples, including 39 of 43 primary tumors (91%). The promoter was methylated partially or completely in all 18 RCC cell lines analyzed. Methylation of the GC-rich putative RASSF1A promoter region and loss of transcription of the corresponding mRNA were related causally. RASSF1A expression was reactivated after treatment with 5-aza-2′-deoxycytidine. Forced expression of RASSF1A transcripts in KRC/Y, a renal carcinoma cell line containing a normal and expressed VHL gene, suppressed growth on plastic dishes and anchorage-independent colony formation in soft agar. Mutant RASSF1A had reduced growth suppression activity significantly. These data suggest that RASSF1A is the candidate renal TSG gene for the 3p21.3 region.

Chronique de Michel le Syrien : patriarche jacobite d'Antioche (1166-1199) /

Syriac text at the end of each vol. making tome IV.

Proceedings of the symposium on current research on wood-destroying organisms and future prospects for protecting wood in use : September 13, 1989, Bend, Oregon /

Cover title.

Grammatike tes hellenikes glossas : Enkritheisa en to diagonismo dia ten pentaetiau 1902-1907

Mode of access: Internet.

Reacting to an assumed situation vs. conforming to an assumed reaction: The role of perceived speaker attitude in vicarious dissonance

Witnessing an ingroup member acting against his or her belief can lead individuals who identify with that group to change their own attitude in the direction of that counterattitudinal behavior. Two studies demonstrate this vicarious dissonance effect among high ingroup identifiers and show that this attitude change is not attributable to conformity to a perceived change in speaker attitude. Study I shows that the effect occurs-indeed, is stronger-even when it is clear that the speaker disagrees with the position espoused, and Study 2 shows that foreseeable aversive consequences bring about attitude change in the observer without any parallel impact on the perceived attitude of the speaker. Furthermore, the assumption that vicarious dissonance is at heart a group phenomenon is supported by the results indicating that attitude change is not impacted either by individual differences in dispositional empathy or measures of interpersonal affinity.

A new method for conservation planning for the persistence of multiple species

Although the aim of conservation planning is the persistence of biodiversity, current methods trade-off ecological realism at a species level in favour of including multiple species and landscape features. For conservation planning to be relevant, the impact of landscape configuration on population processes and the viability of species needs to be considered. We present a novel method for selecting reserve systems that maximize persistence across multiple species, subject to a conservation budget. We use a spatially explicit metapopulation model to estimate extinction risk, a function of the ecology of the species and the amount, quality and configuration of habitat. We compare our new method with more traditional, area-based reserve selection methods, using a ten-species case study, and find that the expected loss of species is reduced 20-fold. Unlike previous methods, we avoid designating arbitrary weightings between reserve size and configuration; rather, our method is based on population processes and is grounded in ecological theory.

The learning dynamics of a universal approximator

The learning properties of a universal approximator, a normalized committee machine with adjustable biases, are studied for on-line back-propagation learning. Within a statistical mechanics framework, numerical studies show that this model has features which do not exist in previously studied two-layer network models without adjustable biases, e.g., attractive suboptimal symmetric phases even for realizable cases and noiseless data.

Regression with input-dependent noise: A Gaussian process treatment

Gaussian processes provide natural non-parametric prior distributions over regression functions. In this paper we consider regression problems where there is noise on the output, and the variance of the noise depends on the inputs. If we assume that the noise is a smooth function of the inputs, then it is natural to model the noise variance using a second Gaussian process, in addition to the Gaussian process governing the noise-free output value. We show that prior uncertainty about the parameters controlling both processes can be handled and that the posterior distribution of the noise rate can be sampled from using Markov chain Monte Carlo methods. Our results on a synthetic data set give a posterior noise variance that well-approximates the true variance.

Regression with input-dependent noise: A Bayesian treatment

In most treatments of the regression problem it is assumed that the distribution of target data can be described by a deterministic function of the inputs, together with additive Gaussian noise having constant variance. The use of maximum likelihood to train such models then corresponds to the minimization of a sum-of-squares error function. In many applications a more realistic model would allow the noise variance itself to depend on the input variables. However, the use of maximum likelihood to train such models would give highly biased results. In this paper we show how a Bayesian treatment can allow for an input-dependent variance while overcoming the bias of maximum likelihood.

Globally optimal on-line learning rules

We present a method for determining the globally optimal on-line learning rule for a soft committee machine under a statistical mechanics framework. This work complements previous results on locally optimal rules, where only the rate of change in generalization error was considered. We maximize the total reduction in generalization error over the whole learning process and show how the resulting rule can significantly outperform the locally optimal rule.

Imaging the dynamics of the auditory steady-state evoked response

This study used magnetoencephalography (MEG) to examine the dynamic patterns of neural activity underlying the auditory steady-state response. We examined the continuous time-series of responses to a 32-Hz amplitude modulation. Fluctuations in the amplitude of the evoked response were found to be mediated by non-linear interactions with oscillatory processes both at the same source, in the alpha and beta frequency bands, and in the opposite hemisphere. © 2005 Elsevier Ireland Ltd. All rights reserved.

Updating finite element models using measured vibration data

The modelling of mechanical structures using finite element analysis has become an indispensable stage in the design of new components and products. Once the theoretical design has been optimised a prototype may be constructed and tested. What can the engineer do if the measured and theoretically predicted vibration characteristics of the structure are significantly different? This thesis considers the problems of changing the parameters of the finite element model to improve the correlation between a physical structure and its mathematical model. Two new methods are introduced to perform the systematic parameter updating. The first uses the measured modal model to derive the parameter values with the minimum variance. The user must provide estimates for the variance of the theoretical parameter values and the measured data. Previous authors using similar methods have assumed that the estimated parameters and measured modal properties are statistically independent. This will generally be the case during the first iteration but will not be the case subsequently. The second method updates the parameters directly from the frequency response functions. The order of the finite element model of the structure is reduced as a function of the unknown parameters. A method related to a weighted equation error algorithm is used to update the parameters. After each iteration the weighting changes so that on convergence the output error is minimised. The suggested methods are extensively tested using simulated data. An H frame is then used to demonstrate the algorithms on a physical structure.