Dietmar Cordes

Ryerson University, Toronto, Ontario, Canada

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Publications (43)143.89 Total impact

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    ABSTRACT: It has recently been shown that both high-frequency and low-frequency cardiac and respiratory noise sources exist throughout the entire brain and can cause significant signal changes in fMRI data. It is also known that the brainstem, basal forebrain and spinal cord area are problematic for fMRI because of the magnitude of cardiac-induced pulsations at these locations. In this study, the physiological noise contributions in the lower brain areas (covering the brainstem and adjacent regions) are investigated and a novel method is presented for computing both low-frequency and high-frequency physiological regressors accurately for each subject. In particular, using a novel optimization algorithm that penalizes curvature (i.e. the second derivative) of the physiological hemodynamic response functions, the cardiac -and respiratory-related response functions are computed. The physiological noise variance is determined for each voxel and the frequency-aliasing property of the high-frequency cardiac waveform as a function of the repetition time (TR) is investigated. It is shown that for the brainstem and other brain areas associated with large pulsations of the cardiac rate, the temporal SNR associated with the low-frequency range of the BOLD response has maxima at subject-specific TRs. At these values, the high-frequency aliased cardiac rate can be eliminated by digital filtering without affecting the BOLD-related signal.
    NeuroImage 12/2013; · 6.25 Impact Factor
  • Dietmar Cordes, Mingwu Jin, Tim Curran, Rajesh Nandy
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    ABSTRACT: The benefits of locally adaptive statistical methods for fMRI research have been shown in recent years, as these methods are more proficient in detecting brain activations in a noisy environment. One such method is local canonical correlation analysis (CCA), which investigates a group of neighboring voxels instead of looking at the single voxel time course. The value of a suitable test statistic is used as a measure of activation. It is customary to assign the value to the center voxel for convenience. The method without constraints is prone to artifacts, especially in a region of localized strong activation. To compensate for these deficiencies, the impact of different spatial constraints in CCA on sensitivity and specificity are investigated. The ability of constrained CCA (cCCA) to detect activation patterns in an episodic memory task has been studied. This research shows how any arbitrary contrast of interest can be analyzed by cCCA and how accurate P-values optimized for the contrast of interest can be computed using nonparametric methods. Results indicate an increase of up to 20% in detecting activation patterns for some of the advanced cCCA methods, as measured by ROC curves derived from simulated and real fMRI data. Hum Brain Mapp, 2012. © 2011 Wiley Periodicals, Inc.
    Human Brain Mapping 11/2012; 33(11):2611-26. · 6.88 Impact Factor
  • Grit Herzmann, Mingwu Jin, Dietmar Cordes, Tim Curran
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    ABSTRACT: Despite a large body of recognition memory research, its temporal, measured with ERPs, and spatial, measured with fMRI, substrates have never been investigated in the same subjects. In the present study, we obtained this information in parallel sessions, in which subjects studied and recognized images of visual objects and their orientation. The results showed that ERP-familiarity processes between 240 and 440 ms temporally preceded recollection processes and were structurally associated with prefrontal brain regions. Recollection processes were most prominent from 440 to 600 ms and correlated with activation in temporal, parietal, and occipital brain regions. Post-retrieval monitoring, which occurred in the ERP between 600 and 1000 ms as a long-lasting slow-wave over frontal channel groups, showed correlations with activation in the prefrontal and parietal cortex. These ERP/fMRI relationships showed some correspondences to source localizations of the investigated ERP memory effects.
    Cognitive neuroscience 09/2012; 3(3-4):174-192. · 2.19 Impact Factor
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    ABSTRACT: Functional magnetic resonance imaging (fMRI) is an important imaging modality to understand the neurodegenerative course of mild cognitive impairment (MCI) and early Alzheimer's disease (AD), because the memory dysfunction may occur before structural degeneration is obvious. In this research, we investigated the functional abnormalities of subjects with amnestic MCI (aMCI) using three episodic memory paradigms that are relevant to different memory domains in both encoding and recognition phases. Both whole-brain analysis and region-of-interest (ROI) analysis of the medial temporal lobes (MTL), which are central to the memory formation and retrieval, were used to compare the efficiency of the different memory paradigms and the functional difference between aMCI subjects and normal control subjects. We also investigated the impact of using different functional activation measurements in ROI analysis. This pilot study could facilitate the use of fMRI activations in the MTL as a marker for early detection and monitoring progression of AD.
    Magnetic Resonance Imaging 03/2012; 30(4):459-70. · 2.06 Impact Factor
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    ABSTRACT: Recent progress in the experimental design for event-related fMRI experiments made it possible to find the optimal stimulus sequence for maximum contrast detection power using a genetic algorithm. In this study, a novel algorithm is proposed for optimization of contrast detection power by including probabilistic behavioral information, based on pilot data, in the genetic algorithm. As a particular application, a recognition memory task is studied and the design matrix optimized for contrasts involving the familiarity of individual items (pictures of objects) and the recollection of qualitative information associated with the items (left/right orientation). Optimization of contrast efficiency is a complicated issue whenever subjects' responses are not deterministic but probabilistic. Contrast efficiencies are not predictable unless behavioral responses are included in the design optimization. However, available software for design optimization does not include options for probabilistic behavioral constraints. If the anticipated behavioral responses are included in the optimization algorithm, the design is optimal for the assumed behavioral responses, and the resulting contrast efficiency is greater than what either a block design or a random design can achieve. Furthermore, improvements of contrast detection power depend strongly on the behavioral probabilities, the perceived randomness, and the contrast of interest. The present genetic algorithm can be applied to any case in which fMRI contrasts are dependent on probabilistic responses that can be estimated from pilot data.
    NeuroImage 02/2012; 60(3):1788-99. · 6.25 Impact Factor
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    Mingwu Jin, Victoria S Pelak, Dietmar Cordes
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    ABSTRACT: Amnestic mild cognitive impairment (aMCI) is a syndrome associated with faster memory decline than normal aging and frequently represents the prodromal phase of Alzheimer's disease. When a person is not actively engaged in a goal-directed task, spontaneous functional magnetic resonance imaging (fMRI) signals can reveal functionally connected brain networks, including the so-called default mode network (DMN). To date, only a few studies have investigated DMN functions in aMCI populations. In this study, group-independent component analysis was conducted for resting-state fMRI data, with slices acquired perpendicular to the long axis of the hippocampus, from eight subjects with aMCI and eight normal control subjects. Subjects with aMCI showed an increased DMN activity in middle cingulate cortex, medial prefrontal cortex and left inferior parietal cortex compared to the normal control group. Decreased DMN activity for the aMCI group compared to the normal control group was noted in lateral prefrontal cortex, left medial temporal lobe (MTL), left medial temporal gyrus, posterior cingulate cortex/retrosplenial cortex/precuneus and right angular gyrus. Although MTL volume difference between the two groups was not statistically significant, a decreased activity in left MTL was observed for the aMCI group. Positive correlations between the DMN activity and memory scores were noted for left lateral prefrontal cortex, left medial temporal gyrus and right angular gyrus. These findings support the premise that alterations of the DMN occur in aMCI and may indicate deficiencies in functional, intrinsic brain architecture that correlate with memory function, even before significant MTL atrophy is detectable by structural MRI.
    Magnetic Resonance Imaging 01/2012; 30(1):48-61. · 2.06 Impact Factor
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    Dietmar Cordes, Mingwu Jin, Tim Curran, Rajesh Nandy
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    ABSTRACT: A wide range of studies show the capacity of multivariate statistical methods for fMRI to improve mapping of brain activations in a noisy environment. An advanced method uses local canonical correlation analysis (CCA) to encompass a group of neighboring voxels instead of looking at the single voxel time course. The value of a suitable test statistic is used as a measure of activation. It is customary to assign the value to the center voxel; however, this is a choice of convenience and without constraints introduces artifacts, especially in regions of strong localized activation. To compensate for these deficiencies, different spatial constraints in CCA have been introduced to enforce dominance of the center voxel. However, even if the dominance condition for the center voxel is satisfied, constrained CCA can still lead to a smoothing artifact, often called the "bleeding artifact of CCA", in fMRI activation patterns. In this paper a new method is introduced to measure and correct for the smoothing artifact for constrained CCA methods. It is shown that constrained CCA methods corrected for the smoothing artifact lead to more plausible activation patterns in fMRI as shown using data from a motor task and a memory task.
    International Journal of Biomedical Imaging 01/2012; 2012:738283.
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    Mingwu Jin, Rajesh Nandy, Tim Curran, Dietmar Cordes
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    ABSTRACT: Local canonical correlation analysis (CCA) is a multivariate method that has been proposed to more accurately determine activation patterns in fMRI data. In its conventional formulation, CCA has several drawbacks that limit its usefulness in fMRI. A major drawback is that, unlike the general linear model (GLM), a test of general linear contrasts of the temporal regressors has not been incorporated into the CCA formalism. To overcome this drawback, a novel directional test statistic was derived using the equivalence of multivariate multiple regression (MVMR) and CCA. This extension will allow CCA to be used for inference of general linear contrasts in more complicated fMRI designs without reparameterization of the design matrix and without reestimating the CCA solutions for each particular contrast of interest. With the proper constraints on the spatial coefficients of CCA, this test statistic can yield a more powerful test on the inference of evoked brain regional activations from noisy fMRI data than the conventional t-test in the GLM. The quantitative results from simulated and pseudoreal data and activation maps from fMRI data were used to demonstrate the advantage of this novel test statistic.
    International Journal of Biomedical Imaging 01/2012; 2012:574971.
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    ABSTRACT: The regulation of energy intake is a complex process involving the integration of homeostatic signals and both internal and external sensory inputs. To better understand the neurobiology of this process and how it may be dysfunctional in obesity, this study examined activity of the brain's "default network" in reduced-obese (RO) as compared to lean individuals. The default network is a group of functionally connected brain regions thought to play an important role in internally directed cognitive activity and the interplay between external and internal sensory processing. Functional magnetic resonance imaging was performed in 24 lean and 18 RO individuals in the fasted state after 2 days of eucaloric energy intake and after 2 days of 30% overfeeding in a counterbalanced design. Scanning was performed while subjects passively viewed images of food and nonfood objects. Independent component analysis was used to identify the default network component. In the eucaloric state, greater default network activity was observed in RO compared to lean individuals in the lateral inferior parietal and posterior cingulate cortices. Activity was positively correlated with appetite. Overfeeding resulted in increased default network activity in lean but not RO individuals. These findings suggest that the function of the default network, a major contributor to intrinsic neuronal activity, is altered in obesity and/or obese-prone individuals. Future studies of the network's function and its relationship to other brain networks may improve our understanding of the mechanisms and treatment of obesity.
    Obesity 06/2011; 19(12):2316-21. · 3.92 Impact Factor
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    ABSTRACT: The default mode network (DMN), one of several resting-state networks (RSN) in the brain, is thought to be involved in self-referential thought, awareness, and episodic memories. Nicotine improves cognitive performance, in part by improving attention. Nicotinic agonists have been shown to decrease activity in regions within DMN and increase activity in regions involved in visual attention during effortful processing of external stimuli. It is unknown if these pharmacological effects also occur in the absence of effortful processing. This study aims to determine if nicotine suppresses activity in default mode and enhances activity in extra-striate RSNs in the absence of an external visual task. Within-subject, single-blinded, counterbalanced study of 19 non-smoking subjects who had resting functional MRI scans after 7 mg nicotine or placebo patch. Group independent component analysis was performed. The DMN component was identified by spatial correlation with a reference DMN mask. A visual attention component was identified by spatial correlation with an extra-striate mask. Analyses were conducted using statistical parametric mapping. Nicotine was associated with decreased activity in regions within the DMN and increased activity in extra-striate regions. Suppression of DMN and enhancement of extra-striate resting-state activity in the absence of visual stimuli or effortful processing suggest that nicotine's cognitive effects may involve a shift in activity from networks that process internal to those that process external information. This is a potential mechanism by which cholinergic agonists may have a beneficial effect in diseases associated with altered resting-state activity.
    Psychopharmacology 02/2011; 216(2):287-95. · 4.06 Impact Factor
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    ABSTRACT: An unsupervised stochastic clustering method based on the ferromagnetic Potts spin model is introduced as a powerful tool to determine functionally connected regions. The method provides an intuitively simple approach to clustering and makes no assumptions of the number of clusters in the data or their underlying distribution. The performance of the method and its dependence on the intrinsic parameters (size of the neighborhood, form of the interaction term, etc.) is investigated on the simulated data and real fMRI data acquired during a conventional periodic finger tapping task. The merits of incorporating Euclidean information into the connectivity analysis are discussed. The ability of the Potts model clustering to uncover the hidden structure in the complex data is demonstrated through its application to the resting-state data to determine functional connectivity networks of the anterior and posterior cingulate cortices for the group of nine healthy male subjects.
    Human Brain Mapping 05/2008; 29(4):422-40. · 6.88 Impact Factor
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    Dietmar Cordes, Rajesh Nandy
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    ABSTRACT: A noisy version of independent component analysis (noisy ICA) is applied to simulated and real functional magnetic resonance imaging (fMRI) data. The noise covariance is explicitly modeled by an autoregressive (AR) model of order 1. The unmixing matrix of the data is determined using a variant of the FastICA algorithm based on Gaussian moments. The sources are estimated using the principle of maximum likelihood by modeling the source densities as asymmetric exponential functions. Effect of dimensionality reduction on the effective noise covariance used, accuracy of the obtained mixing matrix and degree of improvement in estimating fMRI sources are investigated. The primary conclusions after using this method of evaluation are as follows: (a) weighting matrix estimates are similar for noisy and conventional ICA in the realm of typical fMRI data, and (b) source estimates are improved by 5% (as measured by the correlation coefficient) in realistic simulated data by explicitly modeling the source densities and the noise, even when just a simple white noise model is used.
    Magnetic Resonance Imaging 12/2007; 25(9):1237-48. · 2.06 Impact Factor
  • Rajesh Nandy, Dietmar Cordes
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    ABSTRACT: One of the most important considerations in any hypothesis based fMRI data analysis is to choose the appropriate threshold to construct the activation maps, which is usually based on p-values. However, in fMRI data, there are three factors which necessitate severe corrections in the process of estimating the p-values. First, the fMRI time series at an individual voxel has strong temporal autocorrelation which needs to be estimated to obtain the corrected parametric p-value. The second factor is the multiple comparisons problem arising from simultaneously testing tens of thousands of voxels for activation. A common way in the statistical literature to account for multiple testing is to consider the family-wise error rate (FWE) which is related to the distribution of the maximum observed value over all voxels. The third problem, which is not mentioned frequently in the context of adjusting the p-value, is the effect of inherent low frequency processes present even in resting-state data that may introduce a large number of false positives without proper adjustment. In this article, a novel and efficient semi-parametric method, using resampling of normalized spacings of order statistics, is introduced to address all the three problems mentioned above. The new method makes very few assumptions and demands minimal computational effort, unlike other existing resampling methods in fMRI. Furthermore, it will be demonstrated that the correction for temporal autocorrelation is not critical in implementing the proposed method. Results using the proposed method are compared with SPM2.
    NeuroImage 03/2007; 34(4):1562-76. · 6.25 Impact Factor
  • Bart P Keogh, Dietmar Cordes
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    ABSTRACT: The application of functional magnetic resonance imaging (fMRI) to elucidation of seizures and epilepsy has been built primarily upon a framework derived from cortical responses to periodic sensory (and cognitive) stimuli. This analytical approach relies upon assumptions that may be less applicable to the problem of seizure origination. Because of the heterogeneous and complex nature of seizures, a number of quantitative methodologies have been derived to understand fMRI changes that are associated with epileptiform neural activity. Separated broadly, these can be divided into those making some set of assumptions about the form of the MRI signal response to neural activation (the general linear model), and those that are data driven. It is likely that a combination of methodologies, where data driven methods are "informed" by knowledge of the underlying neurobiological process will provide the greatest insight into the underlying neurobiological basis of seizure origination.
    Epilepsia 02/2007; 48 Suppl 4:27-36. · 3.91 Impact Factor
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    ABSTRACT: The current fMRI study investigated correlations of low-frequency signal changes in the left inferior frontal gyrus, right inferior frontal gyrus and cerebellum in 13 adult dyslexic and 10 normal readers to examine functional networks associated with these regions. The extent of these networks to regions associated with phonological processing (frontal gyrus, occipital gyrus, angular gyrus, inferior temporal gyrus, fusiform gyrus, supramarginal gyrus and cerebellum) was compared between good and dyslexic readers. Analysis of correlations in low-frequency range showed that regions known to activate during an "on-off" phoneme-mapping task exhibit synchronous signal changes when the task is administered continuously (without any "off" periods). Results showed that three functional networks, which were defined on the basis of documented structural deficits in dyslexics and included regions associated with phonological processing, differed significantly in spatial extent between good readers and dyslexics. The methodological, theoretical and clinical significance of the findings for advancing fMRI research and knowledge of dyslexia are discussed.
    Magnetic Resonance Imaging 05/2006; 24(3):217-29. · 2.06 Impact Factor
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    Dietmar Cordes, Rajesh R Nandy
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    ABSTRACT: A new method based on an autoregressive noise model of order 1 is introduced to the problem of detecting the number of components in fMRI data. Unlike current information-theoretic criteria like AIC, MDL, and related PPCA, which do not incorporate autocorrelations in the noise, the new method leads to more consistent estimates of the model order, as illustrated in simulated and real fMRI resting-state data.
    NeuroImage 02/2006; 29(1):145-54. · 6.25 Impact Factor
  • Rajesh Ranjan Nandy, Dietmar Cordes
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    ABSTRACT: Receiver operating characteristic (ROC) methods are useful tools for evaluating the sensitivity and specificity of various postprocessing algorithms used in fMRI data analysis. New ROC methods using real fMRI data are proposed that improve a previously introduced method by Le and Hu (Le and Hu, NMR Biomed 1997;10:160-164). The proposed methods provide more accurate means of estimating the true ROC curve from real data and thereby aid in the comparative evaluation of a wide range of postprocessing tools in fMRI. The mathematical relationships between different ROC curves are explored for a comparison of different ROC methods. Examples using real and simulated data are provided to illustrate the ideas involved.
    Magnetic Resonance in Medicine 01/2005; 52(6):1424-31. · 3.27 Impact Factor
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    ABSTRACT: To develop a non-invasive method for exploring seizure initiation and propagation in the brain of intact experimental animals. We have developed and applied a model-independent statistical method--Hierarchical Cluster Analysis (HCA)--for analyzing BOLD-fMRI data following administration of pentylenetetrazol (PTZ) to intact rats. HCA clusters voxels into groups that share similar time courses and magnitudes of signal change, without any assumptions about when and/or where the seizure begins. Epileptiform spiking activity was monitored by EEG (outside the magnet) following intravenous PTZ (IV-PTZ; n=4) or intraperitoneal PTZ administration (IP-PTZ; n=5). Onset of cortical spiking first occurred at 29+/-16 s (IV-PTZ) and 147+/-29 s (IP-PTZ) following drug delivery. HCA of fMRI data following IV-PTZ (n=4) demonstrated a single dominant cluster, involving the majority of the brain and first activating at 27+/-23s. In contrast, IP-PTZ produced multiple, relatively small, clusters with heterogeneous time courses that varied markedly across animals (n=5); activation of the first cluster (involving cortex) occurred at 130+/-59 s. With both routes of PTZ administration, the timing of the fMRI signal increase correlated with onset of EEG spiking. These experiments demonstrate that fMRI activity associated with seizure activity can be analyzed with a model-independent statistical method. HCA indicated that seizure initiation in the IV- and IP-PTZ models involves multiple regions of sensitivity that vary with route of drug administration and that show significant variability across animal subjects. Even given this heterogeneity, fMRI shows clear differences that are not apparent with typical EEG monitoring procedures, in the activation patterns between IV and IP-PTZ models. These results suggest that fMRI can be used to assess different models and patterns of seizure activation.
    Epilepsy Research 01/2005; 66(1-3):75-90. · 2.24 Impact Factor
  • Rajesh Nandy, Dietmar Cordes
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    ABSTRACT: 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.
    Magnetic Resonance in Medicine 11/2004; 52(4):947-52. · 3.27 Impact Factor
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    Larissa Stanberry, Rajesh Nandy, Dietmar Cordes
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    ABSTRACT: 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.
    Human Brain Mapping 01/2004; 20(4):201-19. · 6.88 Impact Factor

Publication Stats

2k Citations
143.89 Total Impact Points

Institutions

  • 2012–2013
    • Ryerson University
      • Department of Physics
      Toronto, Ontario, Canada
    • University of Texas at Arlington
      • Department of Physics
      Arlington, TX, United States
  • 2007–2012
    • University of Colorado
      • Department of Radiology
      Denver, CO, United States
    • University of California, Los Angeles
      • Department of Psychology
      Los Angeles, CA, United States
  • 2002–2008
    • University of Washington Seattle
      • • Department of Statistics
      • • Department of Radiology
      Seattle, WA, United States
  • 2000–2003
    • University of Wisconsin, Madison
      • Department of Medical Physics
      Madison, MS, United States