Biomedical subjects

Dietmar Cordes

Publications and source records attributed to Dietmar Cordes.

9 recordsLinked to original sources

PubMed2004

Improving the spatial specificity of canonical correlation analysis in fMRI.

The contrast-to-noise ratio (CNR) is often very low in fMRI data, and standard univariate methods suffer from a loss of sensitivity in the context of noise. The increased power of a multivariate statistical analysis method known as canonical correlation analysis (CCA) in fMRI studies with low CNR was established previously. However, CCA in its conventional form has weak spatial specificity. In this work we propose a new assignment scheme to rectify this problem. It is shown that the new method has improved spatial specificity as well as sensitivity compared to conventional CCA for detecting activation patterns in fMRI.

Adult↗

PubMed2003

Cluster analysis of fMRI data using dendrogram sharpening.

The major disadvantage of hierarchical clustering in fMRI data analysis is that an appropriate clustering threshold needs to be specified. Upon grouping data into a hierarchical tree, clusters are identified either by specifying their number or by choosing an appropriate inconsistency coefficient. Since the number of clusters present in the data is not known beforehand, even a slight variation of the inconsistency coefficient can significantly affect the results. To address these limitations, the dendrogram sharpening method, combined with a hierarchical clustering algorithm, is used in this work to identify modality regions, which are, in essence, areas of activation in the human brain during an fMRI experiment. The objective of the algorithm is to remove data from the low-density regions in order to obtain a clearer representation of the data structure. Once cluster cores are identified, the classification algorithm is run on voxels, set aside during sharpening, attempting to reassign them to the detected groups. When applied to a paced motor paradigm, task-related activations in the motor cortex are detected. In order to evaluate the performance of the algorithm, the obtained clusters are compared to standard activation maps where the expected hemodynamic response function is specified as a regressor. The obtained patterns of both methods have a high concordance (correlation coefficient = 0.91). Furthermore, the dependence of the clustering results on the sharpening parameters is investigated and recommendations on the appropriate choice of these variables are offered. Hum. Brain Mapping 20:201-219, 2003.

Algorithms↗

PubMed2003

Novel ROC-type method for testing the efficiency of multivariate statistical methods in fMRI.

The receiver operating characteristic (ROC) method is a useful and popular tool for testing the efficiency of various diagnostic tests applicable to functional MRI (fMRI) data. Typically, the diagnostic tests are applied on simulated and pseudo-human fMRI data, and the area under the ROC curve is used as a measure of the efficiency of the diagnostic test. The effectiveness of such a method depends on how well the simulated data approximate the real data. For multivariate statistical methods, however, this technique is usually inadequate, as the spatial dependence among voxels is ignored for simulated data. In this work a modified ROC method using real fMRI data with a broader scope is proposed. This method can be applied to most fMRI postprocessing techniques, including multivariate analyses such as canonical correlation analysis (CCA). Also, the relationship of the modified ROC method with the conventional ROC method is discussed in detail.

Algorithms↗

PubMed2003

Novel nonparametric approach to canonical correlation analysis with applications to low CNR functional MRI data.

Detection of activation in functional MRI (fMRI) is often complicated by the low contrast-to-noise ratio (CNR) in the data. The primary source of the difficulty is the fact that for activities that are subtle the signal can be hidden inside the inherent noise in the data. Classical univariate methods based on t-test or F-test are susceptible to noise, as they fail to harness systematic correlations in evoked responses within neighboring voxels. Here the power of a multivariate statistical analysis tool known as canonical correlation analysis (CCA) in fMRI studies is demonstrated where the CNR is low. As a further illustration of the power of the method, a comparative study of CCA and ordinary correlation analysis using simulated data under various noise levels is also performed. A novel nonparametric approach is introduced to calculate the P-values from the distribution of the complicated test statistic. The circumstances under which CCA is a better performer as well as when it is not the case are discussed. As an example, this method is applied to detect hippocampal activation from memory-related tasks.

Adult↗

PubMed2003

Role of the corpus callosum in functional connectivity.

BACKGROUND AND PURPOSE: Regional cerebral blood flow fluctuates synchronously in corresponding brain regions between the hemispheres. This synchrony implies neuronal connections between brain regions. The synchrony of blood flow changes is defined operationally as functional connectivity. Our purpose was to measure functional connectivity in patients with corpus callosal agenesis, in whom the interhemispheric connectivity is hypothetically diminished. METHODS: In three patients with agenesis of the corpus callosum, functional MR imaging was performed while patients performed text-listening and finger-tapping tasks. Functional images were also acquired while the patients performed no specific task (resting state). Regions of activation temporally correlated with the performance of the tasks were identified by cross-correlating the task data with a reference function. Voxel clusters (seed voxels) that corresponded to regions of activation in the task-activation data set were selected in the resting data set. All the voxels in the resting 3D data set that had a correlation coefficient exceeding 0.4 were identified. The number of these voxels in the ipsilateral and contralateral hemispheres was tabulated. RESULTS: In all patients, technically adequate functional MR and functional connectivity MR maps were obtained. For both tasks, activation was found in both hemispheres. For all of the seed voxels, significantly more functionally connected voxels were found in the ipsilateral hemisphere than in the contralateral hemisphere. For most seed voxels, no functionally connected voxels were found in the contralateral hemisphere. CONCLUSION: Interhemispheric functional connectivity in the motor and auditory cortices is diminished in patients with agenesis of the corpus callosum compared with that of healthy subjects.

Adult↗

PubMed2002

Independent component analysis applied to diffusion tensor MRI.

The accuracy of the outcome in a diffusion tensor imaging (DTI) experiment depends on the acquisition scheme as well as the postprocessing methods used. In the present study, the DTI results acquired after applying different combinations of diffusion-weighted (DW) gradient orientations were initially compared. Then, spatially independent component analysis (ICA) was applied to the T(2) and DW images. In all cases a single component was detected that was similar to the map of the trace of the diffusion tensor, but contained a reduced amount of noise. Furthermore, when no correction for eddy current artifacts was used in the image acquisition scheme, the effects of eddy currents were separated by ICA into independent components. After these components were removed, conventional estimation of the diffusion tensor was performed on the modified data. No artifact was contained in the final rotationally invariant scalar quantities that describe the intrinsic diffusion properties. Additionally, independent components that mapped major white matter fiber tracts in the human brain were identified. Finally, the noise included in the original T(2) and DW images was also separated by ICA into independent components. These components were subsequently removed and a reduction of noise in the final DTI results was achieved.

Adult↗

PubMed2002

Hierarchical clustering to measure connectivity in fMRI resting-state data.

Low frequency oscillations, which are temporally correlated in functionally related brain regions, characterize the mammalian brain, even when no explicit cognitive tasks are performed. Functional connectivity MR imaging is used to map regions of the resting brain showing synchronous, regional and slow fluctuations in cerebral blood flow and oxygenation. In this study, we use a hierarchical clustering method to detect similarities of low-frequency fluctuations. We describe one measure of correlations in the low frequency range for classification of resting-state fMRI data. Furthermore, we investigate the contribution of motion and hardware instabilities to resting-state correlations and provide a method to reduce artifacts. For all cortical regions studied and clusters obtained, we quantify the degree of contamination of functional connectivity maps by the respiratory and cardiac cycle. Results indicate that patterns of functional connectivity can be obtained with hierarchical clustering that resemble known neuronal connections. The corresponding voxel time series do not show significant correlations in the respiratory or cardiac frequency band.

Adult↗

PubMed2002

Comparison of independent component analysis and conventional hypothesis-driven analysis for clinical functional MR image processing.

BACKGROUND AND PURPOSE: With independent component analysis (ICA), regions of activation can be identified on functional MR (fMR) images without a priori knowledge of expected hemodynamic responses. The purpose of this study was to compare the results of fMR imaging data processed with spatial ICA with results obtained with conventional hypothesis-driven analysis. METHODS: Eleven patients with focal cerebral lesions and one with agenesis of the corpus callosum were enrolled. Each patient performed text-listening, finger-tapping, and word-generation tasks. Conventional activation maps were generated by fitting time courses of each voxel to a boxcar reference function. Maps were created from the same image data with ICA techniques. To compare the maps, a concurrence ratio (CR) (number of voxels activated on both maps to number of voxels activated on either map) was calculated. RESULTS: In the ICA analysis, maps with appropriate spatial and temporal features for auditory, sensorimotor, or language cortices were identified in most patients. Images processed with ICA resembled images processed with conventional means. In patients who moved or performed the task incorrectly, ICA produced a map that resembled the expected activation pattern but differed from the conventional image. CRs averaged 70% for all comparisons in the 12 patients. CONCLUSION: fMR imaging maps for auditory, sensorimotor, and language tasks produced with ICA and conventional techniques were similar in most cases. Differences were consistent with the particular characteristics of the method. In data sets corrupted by motion or incorrect task performance, ICA may produce more accurate maps.

Arousal↗

PubMedDate not supplied

Confounding effect of large vessels on MR perfusion images analyzed with independent component analysis.

BACKGROUND AND PURPOSE: First pass contrast-enhanced MR imaging using gradient-echo acquisitions is commonly used to assess cerebral blood flow, despite the confounding signal from large blood vessels. We hypothesized that removal of this unwanted intravascular signal using independent component analysis would result in a more accurate depiction of cerebral blood flow. METHODS: Images of 11 patients, acquired with our acute stroke imaging protocol, were post processed to produce images of relative cerebral blood flow (rCBF). The same images were processed with independent component analysis to identify and remove the signal from large blood vessels, with a second set of rCBF images produced. Both sets of rCBF maps were pooled, randomized in order, and read in a blinded fashion by two neuroradiologists to assess the level of large artery artifact and overall image quality. Significance was determined using a Wilcoxon signed rank test. RESULTS: Results from both readers indicated that the level of large artery artifact was significantly reduced in the images processed using independent component analysis component removal (P <.05). In addition, both readers indicated significantly (P <.05) improved image quality of the images processed using independent component analysis. CONCLUSION: The removal of the signal resulting from large blood vessels before calculation of rCBF resulted in images with significantly less artifact and higher image quality.

Adolescent↗