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Biomedical subjects

Federico E Turkheimer

Publications and source records attributed to Federico E Turkheimer.

11 recordsLinked to original sources
PubMed2007

Reference and target region modeling of [11C]-(R)-PK11195 brain studies.

UNLABELLED: PET with [(11)C]-(R)-PK11195 is currently the modality of choice for the in vivo imaging of microglial activation in the human brain. In this work we devised a supervised clustering procedure and a new quantification methodology capable of producing binding potential (BP) estimates quantitatively comparable with those derived from plasma input with robust quantitative implementation at the pixel level. METHODS: The new methodology uses predefined kinetic classes to extract a gray matter reference tissue without specific tracer binding and devoid of spurious signals (in particular, blood pool and muscle). Kinetic classes were derived from an historical database of 12 healthy control subjects and from 3 patients with Huntington's disease. BP estimates were obtained using rank-shaping exponential spectral analysis (RS-ESA) (both plasma and reference input) and the simplified reference tissue model (SRTM). Comparison between plasma- derived BPs and those produced with the new reference methodology was performed using 6 additional healthy control subjects. Reliability of the new methodology was performed on 4 test-retest studies of patients with Alzheimer's disease. RESULTS: The new algorithm selected reference voxels in gray matter tissue avoiding regions with specific binding located, in particular, in the venous and arterial circulation. Using the new reference, BP values obtained using a plasma input and a reference input were in excellent agreement and highly correlated (r = 0.811, P < 10(-5)) when calculated with RS-ESA and less so (r = 0.507, P < 0.005) when SRTM was used. In the production of parametric maps, SRTM was used with the new reference extraction, resulting in test-retest variability (10.6%; mean ICC = 0.878) that was superior to that obtained using the previous unsupervised clustering approach (mean ICC = 0.596). CONCLUSION: Reference region modeling combined with supervised reference tissue extraction produces a robust and reproducible quantitative assessment of [(11)C]-(R)-PK11195 studies in the human brain.

Algorithms↗
PubMed2006

Chromosomal patterns of gene expression from microarray data: methodology, validation and clinical relevance in gliomas.

BACKGROUND: Expression microarrays represent a powerful technique for the simultaneous investigation of thousands of genes. The evidence that genes are not randomly distributed in the genome and that their coordinated expression depends on their position on chromosomes has highlighted the need for mathematical approaches to exploit this dependency for the analysis of expression data-sets. RESULTS: We have devised a novel mathematical technique (CHROMOWAVE) based on the Haar wavelet transform and applied it to a dataset obtained with the Affymetrix HG-U133_Plus_2 array in 27 gliomas. CHROMOWAVE generated multi-chromosomal pattern featuring low expression in chromosomes 1p, 4, 9q, 13, 18, and 19q. This pattern was not only statistically robust but also clinically relevant as it was predictive of favourable outcome. This finding was replicated on a data-set independently acquired by another laboratory. FISH analysis indicated that monosomy 1p and 19q was a frequent feature of tumours displaying the CHROMOWAVE pattern but that allelic loss on chromosomes 4, 9q, 13 and 18 was much less common. CONCLUSION: The ability to detect expression changes of spatially related genes and to map their position on chromosomes makes CHROMOWAVE a valuable screening method for the identification and display of regional gene expression changes of clinical relevance. In this study, FISH data showed that monosomy was frequently associated with diffuse low gene expression on chromosome 1p and 19q but not on chromosomes 4, 9q, 13 and 18. Comparative genomic hybridisation, allelic polymorphism analysis and methylation studies are in progress in order to identify the various mechanisms involved in this multi-chromosomal expression pattern.

Biomarkers, Tumor↗
PubMed2006

Converging language streams in the human temporal lobe.

There is general agreement that, after initial processing in unimodal sensory cortex, the processing pathways for spoken and written language converge to access verbal meaning. However, the existing literature provides conflicting accounts of the cortical location of this convergence. Most aphasic stroke studies localize verbal comprehension to posterior temporal and inferior parietal cortex (Wernicke's area), whereas evidence from focal cortical neurodegenerative syndromes instead implicates anterior temporal cortex. Previous functional imaging studies in normal subjects have failed to reconcile these opposing positions. Using a functional imaging paradigm in normal subjects that used spoken and written narratives and multiple baselines, we demonstrated common activation during implicit comprehension of spoken and written language in inferior and lateral regions of the left anterior temporal cortex and at the junction of temporal, occipital, and parietal cortex. These results indicate that verbal comprehension uses unimodal processing streams that converge in both anterior and posterior heteromodal cortical regions in the left temporal lobe.

Adult↗
PubMed2006

HBM functional imaging analysis contest data analysis in wavelet space.

An analysis of the Functional Imaging Analysis Contest (FIAC) data is presented using spatial wavelet processing. This technique allows the image to be filtered adaptively according to the data itself, rather than relying on a predetermined filter. This adaptive filtering leads to better estimation of the parameters and contrasts in terms of mean squared error. It will be shown that by introducing a slight bias into the estimation, a large reduction in the variance can be achieved, leading to better overall mean squared error estimates. As no single filter needs to be preselected, results containing many scales of information can be found. In the FIAC data, it is shown that both small-scale and large-scale (smoother, more dispersed) effects occur. The combination of small- and large-scale effects detected in the FIAC data would be easy to miss using conventional single filter analysis.

Algorithms↗
PubMed2005

Wavelet variance components in image space for spatiotemporal neuroimaging data.

Neuroimaging studies place great emphasis on not only the estimation but also the standard error estimates of underlying parameters derived from a temporal model. This allows inferences to be made about the signal estimates and resulting conclusions to be drawn about the underlying data. It can often be advantageous to interrogate temporal models after spatial transformation of the data into the wavelet domain. Wavelet bases provide a multiresolution decomposition of the spatial data dimension and an ensuing reduction in spatial correlation. However, widespread acceptance of these wavelet techniques has been hampered by the limited ability to reconstruct both parametric and error estimates into the image domain after analysis of temporal models in the wavelet domain. This paper introduces a derivation and a fast implementation of a method for the calculation of the variance of the parametric images obtained from wavelet filters. The technique is proposed for a class of estimators that have been shown to be useful in neuroimaging studies. The techniques are demonstrated for both functional magnetic resonance imaging (fMRI) and positron emission tomography (PET) data sets.

Analysis of Variance↗
PubMed2004

On the logic of hypothesis testing in functional imaging.

Statistics is nowadays the customary language of functional imaging. It is common to express an experimental setting as a set of null hypotheses over complex models and to present results as maps of p-values derived from sophisticated probability distributions. However, the growing interest in the development of advanced statistical algorithms is not always paralleled by similar attention to how these techniques may regiment the ways in which users draw inferences from their data. This article investigates the logical bases of current statistical approaches in functional imaging and probes their suitability to inductive inference in neuroscience. The frequentist approach to statistical inference is reviewed with attention to its two main constituents: Fisherian "significance testing" and Neyman-Pearson "hypothesis testing". It is shown that these conceptual systems, which are similar in the univariate testing case, dissociate into two quite different methods of inference when applied to the multiple testing problem, the typical framework of functional imaging. This difference is explained with reference to specific issues, like small volume correction, which are most likely to generate confusion in the practitioner. Further insight into this problem is achieved by recasting the multiple comparison problem into a multivariate Bayesian formulation. This formulation introduces a new perspective where the inferential process is more clearly defined in two distinct steps. The first one, inductive in form, uses exploratory techniques to acquire preliminary notions on the spatial patterns and the signal and noise characteristics. The (smaller) set of likely spatial patterns generated is then tested with newer data and a more rigorous multiple hypothesis testing technique (deductive step).

Algorithms↗
PubMed2003

Rank-shaping regularization of exponential spectral analysis for application to functional parametric mapping.

Compartmental models are widely used for the mathematical modelling of dynamic studies acquired with positron emission tomography (PET). The numerical problem involves the estimation of a sum of decaying real exponentials convolved with an input function. In exponential spectral analysis (SA), the nonlinear estimation of the exponential functions is replaced by the linear estimation of the coefficients of a predefined set of exponential basis functions. This set-up guarantees fast estimation and attainment of the global optimum. SA, however, is hampered by high sensitivity to noise and, because of the positivity constraints implemented in the algorithm, cannot be extended to reference region modelling. In this paper, SA limitations are addressed by a new rank-shaping (RS) estimator that defines an appropriate regularization over an unconstrained least-squares solution obtained through singular value decomposition of the exponential base. Shrinkage parameters are conditioned on the expected signal-to-noise ratio. Through application to simulated and real datasets, it is shown that RS ameliorates and extends SA properties in the case of the production of functional parametric maps from PET studies.

Algorithms↗
PubMed2003

On the undecidability among kinetic models: from model selection to model averaging.

This article deals with the problem of model selection for the mathematical description of tracer kinetics in nuclear medicine. It stems from the consideration of some specific data sets where different models have similar performances. In these situations, it is shown that considerate averaging of a parameter's estimates over the entire model set is better than obtaining the estimates from one model only. Furthermore, it is also shown that the procedure of averaging over a small number of "good" models reduces the "generalization error," the error introduced when the model selected over a particular data set is applied to different conditions, such as subject populations with altered physiologic parameters, modified acquisition protocols, and different signal-to-noise ratios. The method of averaging over the entire model set uses Akaike coefficients as measures of an individual model's likelihood. To facilitate the understanding of these statistical tools, the authors provide an introduction to model selection criteria and a short technical treatment of Akaike's information-theoretic approach. The new method is illustrated and epitomized by a case example on the modeling of [11C]flumazenil kinetics in the brain, containing both real and simulated data.

Carbon Radioisotopes↗
PubMed2003

A linear wavelet filter for parametric imaging with dynamic PET.

This paper describes a new filter for parametric images obtained from dynamic positron emission tomography (PET) studies. The filter is based on the wavelet transform following the heuristics of a previously published method that are here developed into a rigorous theoretical framework. It is shown that the space-time problem of modeling a dynamic PET sequence reduces to the classical one of estimation of a normal multivariate vector of independent wavelet coefficients that, under least-squares risk, can be solved by straightforward application of well established theory. From the study of the distribution of wavelet coefficients of PET images, it is inferred that a James-Stein linear estimator is more suitable for the problem than traditional nonlinear procedures that are incorporated in standard wavelet filters. This is confirmed by the superior performance of the James-Stein filter in simulation studies compared to a state-of-the-art nonlinear wavelet filter and a nonstationary filter selected from literature. Finally, the formal framework is interpreted for the practitioner's point of view and advantages and limitations of the method are discussed.

Aged↗
PubMed2002

Positron emission tomography compartmental models: a basis pursuit strategy for kinetic modeling.

A kinetic modeling approach for the quantification of in vivo tracer studies with dynamic positron emission tomography (PET) is presented. The approach is based on a general compartmental description of the tracer's fate in vivo and determines a parsimonious model consistent with the measured data. The technique involves the determination of a sparse selection of kinetic basis functions from an overcomplete dictionary using the method of basis pursuit denoising. This enables the characterization of the systems impulse response function from which values of the systems macro parameters can be estimated. These parameter estimates can be obtained from a region of interest analysis or as parametric images from a voxel-based analysis. In addition, model order estimates are returned that correspond to the number of compartments in the estimated compartmental model. Validation studies evaluate the methods performance against two preexisting data led techniques, namely, graphical analysis and spectral analysis. Application of this technique to measured PET data is demonstrated using [11C]diprenorphine (opiate receptor) and [11C]WAY-100635 (5-HT1A receptor). Although the method is presented in the context of PET neuroreceptor binding studies, it has general applicability to the quantification of PET/SPECT radiotracer studies in neurology, oncology, and cardiology.

Artifacts↗