Home About us Contact
|
Home About us Contact | ||
|
|
||
fMRI Data (fmri + data)
Selected AbstractsAltered representation of expected value in the orbitofrontal cortex in maniaHUMAN BRAIN MAPPING, Issue 7 2010Felix Bermpohl Abstract Objective: Increased responsiveness to appetitive and reduced responsiveness to aversive anticipatory cues may be associated with dysfunction of the brain reward system in mania. Here we studied neural correlates of gain and loss expectation in mania using functional magnetic resonance imaging (fMRI). Method: Fifteen manic patients and 26 matched healthy control individuals performed a monetary incentive delay task, during which subjects anticipated to win or lose a varying amount of money. Varying both magnitude and valence (win, loss) of anticipatory cues allowed us to isolate the effects of magnitude, valence and expected value (magnitude-by-valence interaction). Results: Response times and total gain amount did not differ significantly between groups. FMRI data indicated that the ventral striatum responded according to cued incentive magnitude in both groups, and this effect did not significantly differ between groups. However, a significant group difference was observed for expected value representation in the left lateral orbitofrontal cortex (OFC; BA 11 and 47). In this region, patients showed increasing BOLD responses during expectation of increasing gain and decreasing responses during expectation of increasing loss, while healthy subjects tended to show the inverse effect. In seven patients retested after remission OFC responses adapted to the response pattern of healthy controls. Conclusions: The observed alterations are consistent with a state-related affective processing bias during the expectation of gains and losses which may contribute to clinical features of mania, such as the enhanced motivation for seeking rewards and the underestimation of risks and potential punishments. Hum Brain Mapp, 2010. © 2009 Wiley-Liss, Inc. [source] Functional magnetic resonance imaging of the human brain and spinal cord by means of signal enhancement by extravascular protonsCONCEPTS IN MAGNETIC RESONANCE, Issue 1 2003P.W. Stroman Abstract A review of functional magnetic resonance imaging (fMRI) signal changes in spin,echo image data is presented. Spin,echo fMRI data from the human brain and spinal cord show a consistent departure from that expected with blood oxygen level dependent (BOLD) contrast. Studies to investigate this finding demonstrate fMRI signal changes of 2.5% in the spinal cord and 0.7% in the brain at 1.5 T, which is extrapolated to an echo time of zero. Consistent evidence of a non-BOLD contrast mechanism arising from a proton-density change at sites of neuronal activation is demonstrated. A mathematical model and physiological explanation for signal enhancement by extravascular protons is also presented. © 2003 Wiley Periodicals, Inc. Concepts Magn Reson 16A: 28,34, 2003 [source] Reduced parietal and visual cortical activation during global processing in Williams syndromeDEVELOPMENTAL MEDICINE & CHILD NEUROLOGY, Issue 6 2007Dean Mobbs BSc Several lines of investigation suggest that individuals with Williams syndrome (WS), a neurodevelopmental disorder of well-characterized genetic etiology, have selective impairments in integrating local image elements into global configurations. We compared global processing abilities in 10 clinically and genetically diagnosed participants with WS (eight females, two males; mean age 31y 10mo [SD 9y 7mo], range 15y 5mo-48y 4mo) with a typically developed (TD) age- and sex-matched comparison group (seven females, one male; mean age 35y 2mo [SD 10y 10mo], range 24y-54y 7mo) using functional magnetic resonance imaging (fMRI). Behavioral data showed participants with WS to be significantly less accurate (p<0.042) together with a non-significant trend to be slower than the TD comparison group while performing the global processing task. fMRI data showed participants with WS to possess reduced activation in the visual and parietal cortices. Participants with WS also showed relatively normal activation in the ventral occipitotemporal cortex, but elevated activation in several posterior thalamic nuclei. These preliminary results largely confirm previous research findings and neural models implicating neurodevelopmental abnormalities in extended subcortical and cortical visual systems in WS, most notably dorsal-stream pathways. [source] Spatial and temporal analysis of fMRI data on word and sentence readingEUROPEAN JOURNAL OF NEUROSCIENCE, Issue 7 2007Sven Haller Abstract Written language comprehension at the word and the sentence level was analysed by the combination of spatial and temporal analysis of functional magnetic resonance imaging (fMRI). Spatial analysis was performed via general linear modelling (GLM). Concerning the temporal analysis, local differences in neurovascular coupling may confound a direct comparison of blood oxygenation level-dependent (BOLD) response estimates between regions. To avoid this problem, we parametrically varied linguistic task demands and compared only task-induced within-region BOLD response differences across areas. We reasoned that, in a hierarchical processing system, increasing task demands at lower processing levels induce delayed onset of higher-level processes in corresponding areas. The flow of activation is thus reflected in the size of task-induced delay increases. We estimated BOLD response delay and duration for each voxel and each participant by fitting a model function to the event-related average BOLD response. The GLM showed increasing activations with increasing linguistic demands dominantly in the left inferior frontal gyrus (IFG) and the left superior temporal gyrus (STG). The combination of spatial and temporal analysis allowed a functional differentiation of IFG subregions involved in written language comprehension. Ventral IFG region (BA 47) and STG subserve earlier processing stages than two dorsal IFG regions (BA 44 and 45). This is in accordance with the assumed early lexical semantic and late syntactic processing of these regions and illustrates the complementary information provided by spatial and temporal fMRI data analysis of the same data set. [source] Comparing MEG and fMRI views to naming actions and objectsHUMAN BRAIN MAPPING, Issue 6 2009Mia Liljeström Abstract Most neuroimaging studies are performed using one imaging method only, either functional magnetic resonance imaging (fMRI), electroencephalography (EEG), or magnetoencephalography (MEG). Information on both location and timing has been sought by recording fMRI and EEG, simultaneously, or MEG and fMRI in separate sessions. Such approaches assume similar active areas whether detected via hemodynamic or electrophysiological signatures. Direct comparisons, after independent analysis of data from each imaging modality, have been conducted primarily on low-level sensory processing. Here, we report MEG (timing and location) and fMRI (location) results in 11 subjects when they named pictures that depicted an action or an object. The experimental design was exactly the same for the two imaging modalities. The MEG data were analyzed with two standard approaches: a set of equivalent current dipoles and a distributed minimum norm estimate. The fMRI blood-oxygen-level dependent (BOLD) data were subjected to the usual random-effect contrast analysis. At the group level, MEG and fMRI data showed fairly good convergence, with both overall activation patterns and task effects localizing to comparable cortical regions. There were some systematic discrepancies, however, and the correspondence was less compelling in the individual subjects. The present analysis should be helpful in reconciling results of fMRI and MEG studies on high-level cognitive functions. Hum Brain Mapp, 2009. © 2009 Wiley-Liss, Inc. [source] Integrated local correlation: A new measure of local coherence in fMRI dataHUMAN BRAIN MAPPING, Issue 1 2009Gopikrishna Deshpande Abstract This article introduces the measure of integrated local correlation (ILC) for assessing local coherence in the brain using functional magnetic resonance imaging (fMRI) data and characterizes the measure in terms of reproducibility, the effect of physiological noise, and the dependence on image resolution. The coupling of local neuronal processes influences coherence in a voxel's neighborhood. ILC is defined, for each voxel, as the integration of its spatial correlation function. This integrated measure does not require the specification of a neighborhood and, as demonstrated by experimental data, is effectively independent of image resolution. Respiratory and cardiac fluctuations do not considerably alter the ILC value except in isolated areas in and surrounding large vessels. With resting-state fMRI data, ILC was demonstrated to be tissue-specific, higher in gray matter than white matter, and reproducible across consecutive runs in healthy individuals. Within the gray matter, ILC was found to be higher in the default mode network, particularly the posterior and anterior cingulate cortices. Comparing ILC maps obtained from resting state and continuous motor task data, we observed reduced local coherence in the default mode network during the task. Finally, we compared ILC and regional homogeneity by examining their ability to discriminate between gray and white matters in resting state data and found ILC to be more sensitive. Hum Brain Mapp 2009. © 2007 Wiley-Liss, Inc. [source] Discrete dynamic Bayesian network analysis of fMRI dataHUMAN BRAIN MAPPING, Issue 1 2009John Burge Abstract We examine the efficacy of using discrete Dynamic Bayesian Networks (dDBNs), a data-driven modeling technique employed in machine learning, to identify functional correlations among neuroanatomical regions of interest. Unlike many neuroimaging analysis techniques, this method is not limited by linear and/or Gaussian noise assumptions. It achieves this by modeling the time series of neuroanatomical regions as discrete, as opposed to continuous, random variables with multinomial distributions. We demonstrated this method using an fMRI dataset collected from healthy and demented elderly subjects (Buckner, et al., 2000: J Cogn Neurosci 12:24-34) and identify correlates based on a diagnosis of dementia. The results are validated in three ways. First, the elicited correlates are shown to be robust over leave-one-out cross-validation and, via a Fourier bootstrapping method, that they were not likely due to random chance. Second, the dDBNs identified correlates that would be expected given the experimental paradigm. Third, the dDBN's ability to predict dementia is competitive with two commonly employed machine-learning classifiers: the support vector machine and the Gaussian naïve Bayesian network. We also verify that the dDBN selects correlates based on non-linear criteria. Finally, we provide a brief analysis of the correlates elicited from Buckner et al.'s data that suggests that demented elderly subjects have reduced involvement of entorhinal and occipital cortex and greater involvement of the parietal lobe and amygdala in brain activity compared with healthy elderly (as measured via functional correlations among BOLD measurements). Limitations and extensions to the dDBN method are discussed. Hum Brain Mapp, 2009. © 2007 Wiley-Liss, Inc. [source] Estimating the number of independent components for functional magnetic resonance imaging dataHUMAN BRAIN MAPPING, Issue 11 2007Yi-Ou Li Abstract Multivariate analysis methods such as independent component analysis (ICA) have been applied to the analysis of functional magnetic resonance imaging (fMRI) data to study brain function. Because of the high dimensionality and high noise level of the fMRI data, order selection, i.e., estimation of the number of informative components, is critical to reduce over/underfitting in such methods. Dependence among fMRI data samples in the spatial and temporal domain limits the usefulness of the practical formulations of information-theoretic criteria (ITC) for order selection, since they are based on likelihood of independent and identically distributed (i.i.d.) data samples. To address this issue, we propose a subsampling scheme to obtain a set of effectively i.i.d. samples from the dependent data samples and apply the ITC formulas to the effectively i.i.d. sample set for order selection. We apply the proposed method on the simulated data and show that it significantly improves the accuracy of order selection from dependent data. We also perform order selection on fMRI data from a visuomotor task and show that the proposed method alleviates the over-estimation on the number of brain sources due to the intrinsic smoothness and the smooth preprocessing of fMRI data. We use the software package ICASSO (Himberg et al. [ 2004]: Neuroimage 22:1214,1222) to analyze the independent component (IC) estimates at different orders and show that, when ICA is performed at overestimated orders, the stability of the IC estimates decreases and the estimation of task related brain activations show degradation. Hum Brain Mapp, 2007. © 2007 Wiley-Liss, Inc. [source] Source density-driven independent component analysis approach for fMRI dataHUMAN BRAIN MAPPING, Issue 3 2005Baoming Hong Abstract Independent component analysis (ICA) has become a popular tool for functional magnetic resonance imaging (fMRI) data analysis. Conventional ICA algorithms including Infomax and FAST-ICA algorithms employ the underlying assumption that data can be decomposed into statistically independent sources and implicitly model the probability density functions of the underlying sources as highly kurtotic or symmetric. When source data violate these assumptions (e.g., are asymmetric), however, conventional ICA methods might not work well. As a result, modeling of the underlying sources becomes an important issue for ICA applications. We propose a source density-driven ICA (SD-ICA) method. The SD-ICA algorithm involves a two-step procedure. It uses a conventional ICA algorithm to obtain initial independent source estimates for the first-step and then, using a kernel estimator technique, the source density is calculated. A refitted nonlinear function is used for each source at the second step. We show that the proposed SD-ICA algorithm provides flexible source adaptivity and improves ICA performance. On SD-ICA application to fMRI signals, the physiologic meaningful components (e.g., activated regions) of fMRI signals are governed typically by a small percentage of the whole-brain map on a task-related activation. Extra prior information (using a skewed-weighted distribution transformation) is thus additionally applied to the algorithm for the regions of interest of data (e.g., visual activated regions) to emphasize the importance of the tail part of the distribution. Our experimental results show that the source density-driven ICA method can improve performance further by incorporating some a priori information into ICA analysis of fMRI signals. Hum Brain Mapping, 2005. © 2005 Wiley-Liss, Inc. [source] Characterizing instantaneous phase relationships in whole-brain fMRI activation dataHUMAN BRAIN MAPPING, Issue 2 2002Angela R. Laird Abstract Typically, fMRI data is processed in the time domain with linear methods such as regression and correlation analysis. We propose that the theory of phase synchronization may be used to more completely understand the dynamics of interacting systems, and can be applied to fMRI data as a novel method of detecting activation. Generalized synchronization is a phenomenon that occurs when there is a nonlinear functional relationship present between two or more coupled, oscillatory systems, whereas phase synchronization is defined as the locking of the phases while the amplitudes may vary. In this study, we developed an application of phase synchronization analysis that is appropriate for fMRI data, in which the phase locking condition is investigated between a voxel time series and the reference function of the task performed. A synchronization index is calculated to quantify the level of phase locking, and a nonparametric permutation test is used to determine the statistical significance of the results. We performed the phase synchronization analysis on the data from five volunteers for an event-related finger-tapping task. Functional maps were created that provide information on the interrelations between the instantaneous phases of the reference function and the voxel time series in a whole-brain fMRI activation data set. We conclude that this method of analysis is useful for revealing additional information on the complex nature of the fMRI time series. Hum. Brain Mapping 16:71,80, 2002. © 2002 Wiley-Liss, Inc. [source] Differential transient MEG and fMRI responses to visual stimulation onset rateINTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY, Issue 1 2008August S. Tuan Abstract While recent analysis of functional magnetic resonance imaging (fMRI) data utilize a generalized nonlinear convolution model (e.g., dynamic causal modeling), most conventional analyses of local responses utilize a linear convolution model (e.g., the general linear model). These models assume a linear relationship between the blood oxygenated level dependent (BOLD) signal and the underlying neuronal response. While previous studies have shown that this "neurovascular coupling" process is approximately linear, short stimulus durations are known to produce a larger fMRI response than expected from a linear system. This divergence from linearity between the stimulus time-course and BOLD signal could be caused by neuronal onset and offset transients, rather than a nonlinearity in the hemodynamics related to BOLD contrast. We tested this hypothesis by measuring MEG and fMRI responses to stimuli with ramped contrast onsets and offsets in place of abrupt transitions. MEG results show that the ramp successfully reduced the transient onset of neural activity. However, the nonlinearity in the fMRI response, while also reduced, remained. Predictions of fMRI responses from MEG signals show a weaker nonlinearity than observed in the actual fMRI data. These results suggest that the fMRI BOLD nonlinearity seen with short duration stimuli is not solely due to transient neuronal activity. © 2008 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 18, 17,28, 2008 [source] Minimization of Nyquist ghosting for echo-planar imaging at ultra-high fields based on a "negative readout gradient" strategyJOURNAL OF MAGNETIC RESONANCE IMAGING, Issue 5 2009Wietske van der Zwaag PhD Abstract Purpose: To improve the traditional Nyquist ghost correction approach in echo planar imaging (EPI) at high fields, via schemes based on the reversal of the EPI readout gradient polarity for every other volume throughout a functional magnetic resonance imaging (fMRI) acquisition train. Materials and Methods: An EPI sequence in which the readout gradient was inverted every other volume was implemented on two ultrahigh-field systems. Phantom images and fMRI data were acquired to evaluate ghost intensities and the presence of false-positive blood oxygenation level-dependent (BOLD) signal with and without ghost correction. Three different algorithms for ghost correction of alternating readout EPI were compared. Results: Irrespective of the chosen processing approach, ghosting was significantly reduced (up to 70% lower intensity) in both rat brain images acquired on a 9.4T animal scanner and human brain images acquired at 7T, resulting in a reduction of sources of false-positive activation in fMRI data. Conclusion: It is concluded that at high B0 fields, substantial gains in Nyquist ghost correction of echo planar time series are possible by alternating the readout gradient every other volume. J. Magn. Reson. Imaging 2009;30:1171,1178. © 2009 Wiley-Liss, Inc. [source] Characterization of BOLD activation in multi-echo fMRI data using fuzzy cluster analysis and a comparison with quantitative modelingNMR IN BIOMEDICINE, Issue 7-8 2001Markus Barth Abstract A combination of multiple gradient-echo imaging and exploratory data analysis (EDA), i.e. fuzzy cluster analysis (FCA), is proposed for separation and characterization of BOLD activation in single-shot spiral functional magnetic resonance imaging (fMRI) experiments at 3 T. Differentiation of functional activation using FCA is performed by clustering pixel signal changes (,S) as a function of echo time (TE). Further vascular classification is supported by the localization of activation and the comparison with a single-exponential decay model. In some subjects, an additional indication for large vessels within a voxel was found as oscillation of the fMRI signal difference vs echo time (TE). Such large vessels may be separated from small vessel activation and, therefore, our proposed procedure might prove useful if a more specific functional localization is desired in fMRI. In addition to the signal change ,S, ,T2*/T2* is significantly different between activated regions. Averaged over all eight subjects ,T2* is 1.7,±,0.2,ms in ROIs with the highest signal change characterized as containing large vessels, whereas in ROIs corresponding to microvascular environment average ,T2* values are 0.8,±,0.1,ms. Copyright © 2001 John Wiley & Sons, Ltd. [source] | |||||||||||||