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At least 991 records · Page 55Linked to original sources
PubMed2004

Controlling for premorbid brain size in imaging studies: T1-derived cranium scaling factor vs. T2-derived intracranial vault volume.

Intracranial vault (ICV) volume, obtained from T2-weighted magnetic resonance imaging (MRI), is generally used to estimate premorbid brain size in imaging studies. T1-weighted sequences lack the signal characteristics for ICV measurements [they have poor contrast at the outer boundary of sulcal cranium scaling factor (CSF)] but are valuable in imaging studies due to their excellent gray vs. white matter contrast. Smith et al. [NeuroImage 17 (2002) 479] suggested a T1-derived cranium scaling factor as an alternative control variable for premorbid brain size in cross-sectional studies. This index, which is computed using the SIENAX software, is a scaling factor comparing an individual's skull to a template skull derived from the Montreal Neurological Institute (MNI) average of 152 T1 studies (the MNI152). SIENAX computes coarsely defined estimates for the individual and MNI skulls rather than well-defined volumes. To test how well this approach would work as a control variable for premorbid brain size in cross-sectional studies, we compared the T1-derived cranium scaling factor to T2-derived ICV measurements in a sample of 92 individuals: 39 white males, 22 white females, and 31 African-American males, with an age range of 26-78 years. The correlation between T1- and T2-derived variables was 0.94 and did not differ across subject groups. The T1-derived cranium scaling factor accounted for a statistically significant portion (87%) of the variance of the T2-derived ICV measure and thus is a good surrogate for ICV measurement of premorbid brain size as a reference measure in MRI atrophy studies. Furthermore, neither race, sex, nor age accounted for any additional variance in ICV, indicating that neither race-, gender-, nor age-associated cranial bone thickness effects were present in this data set.

Adult↗
PubMed2003

Proliferation and competition in discrete biological systems.

We study the emergence of collective spatio-temporal objects in biological systems by representing individually the elementary interactions between their microscopic components. We use the immune system as a prototype for such interactions. The results of this detailed explicit analysis are compared with the traditional procedure of representing the collective dynamics in terms of densities that obey partial differential equations. The simulations show even for very simple elementary reactions the spontaneous emergence of localized complex structures, from microscopic noise. In turn the effective dynamics of these structures affects the average behaviour of the system in a very decisive way: systems which would according to the differential equations approximation die, display in reality a very lively behaviour. As the optimal modelling method we propose a mixture of microscopic simulation systems describing each reaction separately, and continuous methods describing the average behaviour of the agents.

Adaptation, Physiological↗
PubMed2003

Compact and efficient 3D shape description through radial function approximation.

A fast and simple method for three-dimensional shape description is described. The method views a 3D object as a radial distance function on the unit sphere, and thus reduces the dimensionality of the description problem by one. The radial distance function is approximated by Fourier methods in the basis of the spherical harmonic polynomials. The necessary integration is carried out on the object boundary, rather than on the unit sphere. Consequently, there is no need of a parameterisation of the object surface. The description makes it possible to compare shapes in a computationally very simple way. Solutions on how to cope with translated and rotated objects are discussed. The method is developed for star-shaped objects, but is stable even if the input image is non-star-shaped. The method is tested in a data set from magnetic resonance imaging (MRI) of the brain. Potential medical applications are discussed.

Brain↗
PubMedDate not supplied

A new method for estimating T2 distributions from NMR measurements.

A semi-continuous relaxation model is constructed using sums of gamma functions and non-negative least squares for the inversion of Carr-Purcell-Meiboom-Gill (CPMG) echo data. No regularization is necessary for this approach, and yet the solution is stable even with noisy data. Test results derived from 60 echo trains are presented. Computational advantages of the method are presented.

Computer Graphics↗
PubMedDate not supplied

Examples of uniform-penalty inversion of multiexponential relaxation data.

When multiexponential relaxation data are inverted to give quasi-continuous distributions of relaxation times, the computed distribution is usually smoothed by means of an applied penalty function equal to a coefficient, C, times the integrated square of amplitude, slope, or curvature. When the distribution has a sharp peak and either a broad peak or long tail, smoothing with a fixed coefficient, C, widens the sharp peak and/or breaks up the broad peak or tail into two or more separate peaks. An iterative feedback procedure is used to generate a separate C value for each computed point in such a way as to give roughly equal contributions to the penalty function from each computed point. This permits adequate smoothing of broad features without oversmoothing sharp peaks. Examples are given for artificial data, for nuclear magnetic resonance in fluids in porous media, and for nuclear magnetic resonance in biological tissues.

Computer Simulation↗
PubMed1996

Neurons, networks, and motor behavior.

The field of motor pattern generation and motor control has progressed markedly in the last decade. There has been a revolutionary shift in thinking from hard-wired circuits to multifunctional networks. Yet, it is clear that we still have a long way to go before we understand how very large ensembles of neurons produce behaviors. The systems where we have made the most headway are those that have an orderly topography, such as the superior colliculus (Sparks) or motor cortex (Georgopoulos). However, even in these systems, although we understand how to interpret the combined activity of the neuronal population, it is not clear how this population activity is translated into a motor command. Similarly, the directional behavior produced in cockroach (Ritzmann) and fish escape (Eaton) systems can be predicted based on the activity of neurons, but the cellular mechanisms producing the turning responses in cockroaches and teleost fish are not completely understood. Undoubtedly, computational approaches, including new mathematical formalisms and computer simulations, will play a role in elucidating how very large ensembles of neurons produce their coordinated output. For now, the systems where motor pattern generation is best understood at the cellular level are those with small numbers of neurons (such as invertebrate circuits) or small numbers of cell types, such as lamprey and tadpole spinal circuits. These systems are thus valuable for pointing to potential mechanisms used in larger systems. (Note that I avoid using the term "simple" systems to describe invertebrates because it is quite clear that these systems are anything but simple.) However, "interphyletic awareness," as it was referred to at this conference, is not important just for what it can tell us about how mammals work. It is also important to learn of alternative ways in which organisms solve similar problems. This may prove to be particularly important for the future of robotics. Already, robots have been designed based on insights gained from studying insect visual (Strausfeld) and motor (Ritzmann) systems. Robotics engineers have also independently converged on some of the same mechanisms used by biological systems (MacPherson). There is clearly a need for better understanding of higher control of pattern-generating circuits. This is not limited to how motor patterns are initiated, but also includes how they are altered on a moment to moment basis to suit the needs of the animal. The next revolution in the field is likely to come from a paradigm shift regarding such control of motor circuits, similar to the shift that has already occurred in our understanding of the pattern-generating circuits themselves. Such flexibility of control is the basis for decision making in the nervous system and the very essence of what animals must do throughout their daily lives. I look forward to the next conference in 2005 to see how far we've progressed in these pursuits.

Animals↗
PubMed2003

Voxel-based morphometry of grey matter densities in subjects at high risk of schizophrenia.

The grey matter (GM) segments from T1 structural magnetic resonance (MR) images of the brain in subjects at high risk of schizophrenia (n=146) were compared with normal control subjects (n=36) and first episode schizophrenic subjects (n=34) using automated voxel-based morphometry (VBM). The subjects were recruited for the Edinburgh High Risk Study (EHRS) and regional brain volumes had previously been measured using a semi-automated volumetric region of interest (ROI) method of analysis. For the current report, the images were processed using a study specific template and statistically analysed using the SPM99 program. The small volume correction tool in SPM was also used to restrict the analyses to specific voxels. Reductions in the probability of grey matter (GM) density were seen bilaterally in the anterior cingulate, and as a trend in the left parahippocampal gyrus for the high-risk vs. control subjects. In contrast, first episode schizophrenia subjects had less GM than high-risk subjects in several frontal and temporal regions. These results are compatible with the findings of our previous volumetric ROI analysis.

Acute Disease↗
PubMed2000

Moment analysis as a systematic tool for NMR powder pattern analysis.

The low order moments for chemical shift and second-order quadrupolar powder patterns have been calculated as functions of the anisotropy and asymmetry parameter of the governing interaction, and the expressions inverted to give these parameters as a function of the moments. Theoretical simulations and experimental experience show that moment analysis in most cases equals and in some cases exceeds the accuracy of direct inspection as a method of obtaining NMR parameters. We illustrate the efficacy of the method applied to 31P chemical shift spectra of nucleic acids, and 39K second-order patterns of series of potassium salts.

Adenosine Monophosphate↗
PubMed2000

Characterization of 15N chemical shift tensors via 15N-13C REDOR and 1N-1H dipolar-shift CPMAS NMR spectroscopy.

As part of our studies on the characterization of 15N chemical shift anisotropy (CSA) via magic angle spinning (MAS) NMR spectroscopy, we have investigated via numerical simulations the sensitivity of two different REDOR experimental protocols to the angles defining the orientation of the 15N-13C' bond vector in the principal axis system of the 15N CSA tensor of the amide nitrogen in a peptide bond. Additionally, employing polycrystalline samples of 15N and 13C', 15N-labeled acetanilide, we have obtained, in a first study of this type, the orientation of the 15N CSA tensor in the molecular frame by orienting the tensor with respect to the 15N-3C' and 15N-1H dipolar vectors via 15N-13C' REDOR and 15N-1H dipolar-shift MAS experiments, respectively.

Acetanilides↗
PubMed2003

Spatiotemporal analysis of optical imaging data.

Previous methods for analyzing optical imaging data have relied heavily on temporal averaging. However, response dynamics are rich sources of information. Here, we develop and present a method that combines principal component analysis and multitaper harmonic analysis to extract the statistically significant spatial and temporal response from optical imaging data. We apply the method to both simulated data and experimental optical imaging data from the cat primary visual cortex.

Animals↗
PubMed2003

Characterizing volume and surface deformations in an atlas framework: theory, applications, and implementation.

Given deformations for mapping images or surfaces into an atlas configuration, methods are described for characterizing the mean deformation and deviations from this mean. Jacobian matrices are used to characterize the deformations locally, and the method can be applied to any image warping method for which Jacobian matrices can be computed. The method makes use of the fact that each matrix descriptor of the local deformation required to match an image to the atlas corresponds to a point on a semi-Riemannian manifold. By assuring that the mean matrix lies within this manifold, fundamental geometric properties common to all of the images can be preserved. Local deviations from the mean can be characterized in a euclidean space tangent to the semi-Riemannian manifold at the mean and can be accumulated globally across multiple sampling locations within the atlas to generate a global multivariate characterization of how each image deviates from the mean.

Algorithms↗
PubMed2003

Functional magnetic resonance imaging (fMRI) "brain reading": detecting and classifying distributed patterns of fMRI activity in human visual cortex.

Traditional (univariate) analysis of functional MRI (fMRI) data relies exclusively on the information contained in the time course of individual voxels. Multivariate analyses can take advantage of the information contained in activity patterns across space, from multiple voxels. Such analyses have the potential to greatly expand the amount of information extracted from fMRI data sets. In the present study, multivariate statistical pattern recognition methods, including linear discriminant analysis and support vector machines, were used to classify patterns of fMRI activation evoked by the visual presentation of various categories of objects. Classifiers were trained using data from voxels in predefined regions of interest during a subset of trials for each subject individually. Classification of subsequently collected fMRI data was attempted according to the similarity of activation patterns to prior training examples. Classification was done using only small amounts of data (20 s worth) at a time, so such a technique could, in principle, be used to extract information about a subject's percept on a near real-time basis. Classifiers trained on data acquired during one session were equally accurate in classifying data collected within the same session and across sessions separated by more than a week, in the same subject. Although the highest classification accuracies were obtained using patterns of activity including lower visual areas as input, classification accuracies well above chance were achieved using regions of interest restricted to higher-order object-selective visual areas. In contrast to typical fMRI data analysis, in which hours of data across many subjects are averaged to reveal slight differences in activation, the use of pattern recognition methods allows a subtle 10-way discrimination to be performed on an essentially trial-by-trial basis within individuals, demonstrating that fMRI data contain far more information than is typically appreciated.

Adult↗
PubMed2003

Functional MRI using sensitivity-encoded echo planar imaging (SENSE-EPI).

Parallel imaging methods become increasingly available on clinical MR scanners. To investigate the potential of sensitivity-encoded single-shot EPI (SENSE-EPI) for functional MRI, five imaging protocols at different SENSE reduction factors (R) and matrix sizes were compared with respect to their noise characteristics and their sensitivity toward functional activation in a motor task examination. At constant echo times, SENSE-EPI was either used to shorten the single volume acquisition times (TR(min)) at matrix size 128 x 100 (22 slices) from 3.9 s (no SENSE) to 2.0 s at R = 3, or to increase the matrix size to 192 x 153 (22 slices), resulting in TR(min) = 5.3 s for R = 2 or TR(min) = 3.4 s for R = 3. At the lower resolution, the bisection of echo train length (R = 2) substantially reduced distortions and blurring, while signal-to-noise and statistical power (measured by cluster size and maximum t value per unit time) were hardly reduced. At R = 3 the additional gain in speed and distortion reduction was quite small, while signal-to-noise and statistical power dropped significantly. With enhanced spatial resolution the time course signal-to-noise was better than expected from theory for purely thermal noise because of a reduced contribution of physiological noise, and statistical power almost reached that of the regular, low-resolution single-shot EPI, with a slight drop off toward R = 3. Thus, SENSE-EPI allows to substantially increase speed and spatial resolution in fMRI. At SENSE reduction factors up to R = 2, the potential drawbacks regarding signal-to-noise and statistical power are almost negligible.

Adult↗
PubMed2003

Sustained attention impairment correlates to gray matter decreases in first episode neuroleptic-naive schizophrenic patients.

Impaired sustained attention seems to be a specific neuropsychological deficit that is closely linked to schizophrenia. Voxel based morphometry has emerged as a useful tool for the detection of subtle gray matter (GM) abnormalities. The aim of our study was to identify the cerebral regions related to the Identical-Pair version of the Continuous Performance Test (CPT-IP) performance in schizophrenic patients. The study included 13 right-handed, male, first-episode, paranoic, neuroleptic-naive schizophrenic patients and 13 matched controls. High-resolution whole-brain MR images were segmented and analyzed for the whole brain and for regions of interest (ROI) using SPM99. Furthermore, the correlation between CPT-IP performance and GM density was examined. Volumetric analysis of the thalami was also carried out. GM density analysis shown decreases in patients in anterior cingulate gyrus, left inferior frontal, right claustrum, left pulvinar, and dorsomedial bilateral thalamic nuclei, and caudate nuclei as well as left hippocampus and parahippocampal gyrus. Thalamic ROIs revealed a strong correlation between groups differences. The thalamic GM density allowed a good individual classification. GM increases were detected in left insula, superior temporal gyrus, and putamen nucleus, and right supramarginal gyrus. Schizophrenic patients showed smaller left and right thalamic volumes. We found that GM density of the left thalamic nucleus, left angular, and supramarginal gyrus, and left inferior frontal and postcentral gyri correlated significantly with CPT-IP performance in patients but not in controls. Moreover, the restricted ROIs regression was strongly significant for both left and right thalamus. In summary, we provide evidence for the involvement of thalamic, inferior-parietal, and frontal regions in the attentional deficits observed in schizophrenic patients.

Adult↗
PubMed2003

Independent component analysis of nondeterministic fMRI signal sources.

Neuronal activation can be separated from other signal sources of functional magnetic resonance imaging (fMRI) data by using independent component analysis (ICA). Without deliberate neuronal activity of the brain cortex, the fMRI signal is a stochastic sum of various physiological and artifact related signal sources. The ability of spatial-domain ICA to separate spontaneous physiological signal sources was evaluated in 15 anesthetized children known to present prominent vasomotor fluctuations in the functional cortices. ICA separated multiple clustered signal sources in the primary sensory areas in all of the subjects. The spatial distribution and frequency spectra of the signal sources correspond to the known properties of 0.03-Hz very-low-frequency vasomotor waves in fMRI data. In addition, ICA was able to separate major artery and sagittal sinus related signal sources in each subject. The characteristics of the blood vessel related signal sources were different from the parenchyma sources. ICA analysis of fMRI can be used for both assessing the statistical independence of brain signals and segmenting nondeterministic signal sources for further analysis.

Artifacts↗
PubMed2003

Properties of MEG tomographic maps obtained with spatial filtering.

Magnetoencephalography (MEG) has, in comparison with other functional imaging modalities, unique properties which makes it the prime candidate for the noninvasive investigation of long-range oscillatory interactions in the human brain. Recent methodological developments based on spatial filtering introduced the computation of functional tomographic maps covering the entire brain and representing the distribution of coherence to a given reference signal or the distribution of power. Because of the spatially inhomogeneous sensitivity profile of the MEG sensors, the spatial resolution of the resulting functional maps is not isotropic across the brain. Here, we introduce a convenient analytic expression for the computation of the spatial resolution at any given point in the brain. We derive the dependence of the resolution on the signal-to-noise ratio and on the changes of the leadfields. The resolution map can be displayed on anatomical MRI in the same way as the functional maps. In addition, we establish a procedure for computing a confidence volume of local maxima which is based on a bootstrap method. The confidence volume is a measure for the uncertainty of the localization. It is important for assigning local maxima of activation to specific anatomical structures and may be used to test for differences in localization between different experimental conditions.

Artifacts↗