Improved detection of time windows of brain responses in fMRI using modified temporal clustering analysis

Yee, S; Gao, Jia‐Hong

doi:10.1016/s0730-725x(02)00484-8

Cited by 29 publications

(31 citation statements)

References 11 publications

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“…After preprocessing, the 2dTCA analysis is performed using programs written in IDL software (ITT Visual Information Solutions, Boulder, CO) based on the modifications of the original TCA methods [Gao and Yee, 2003;Liu et al, 2000;Lu et al, 2006;Morgan et al, 2004;Yee and Gao, 2002;Zhao et al, 2004]. Each voxel time course is treated individually as an array, Z(1:N).…”

Section: Tca Vs 2dtca Methodsmentioning

confidence: 99%

“…In this way, each column of the matrix will consist of the histogram of significant signal increases for those voxels whose first time point exceeding the threshold occurred at time point x. This is in contrast to the onedimensional histogram mapping (hist1d(1:N)) of the original TCA method [Liu et al, 2000;Morgan et al, 2004]: and the weighted one-dimensional mapping used by Yee and Gao [Yee and Gao, 2002] and the use of the extremum mapping of Z proposed by Lu et al [Lu et al, 2006]. In these onedimensional methods, the resulting histogram is the reference signal time course used for fMRI analyses.…”

Section: Tca Vs 2dtca Methodsmentioning

confidence: 99%

“…Other types of data-driven techniques such as hierarchical clustering Keogh et al, 2005;Stanberry et al, 2003], fuzzy clustering [Baumgartner et al, 2000], and temporal clustering analysis (TCA) [Gao and Yee, 2003;Liu et al, 2000;Lu et al, 2006;Makiranta et al, 2005;Yee and Gao, 2002;Zhao et al, 2004] use clustering of similar fMRI signal time courses to group and determine voxel time courses of interest, instead of partitioning into components. Previous studies have reported that these clustering algorithms outperform PCA [Baumgartner et al, 2000] and ICA techniques for fMRI analysis [Meyer-Baese et al, 2004].…”

Section: Introductionmentioning

confidence: 99%

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Development of 2dTCA for the detection of irregular, transient bold activity

Morgan¹,

Li²,

Abou‐Khalil³

et al. 2007

Human Brain Mapping

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The temporal clustering algorithm (TCA) has been developed in order to detect irregular, transient functional MRI (fMRI) activation signals when the timings of the stimuli are unknown. Unfortunately, such methods are also sensitive to signal changes caused by motion and physiological noise. We have developed a modified TCA technique, 2dTCA, which can detect multiple different timing patterns within a dataset so that signals of interest can be separated from artifacts and those of no interest. The objective of this work was to further develop the 2dTCA methods and evaluate their performance in simulated functional MRI datasets. Comparisons were made with TCA and a freely-distributed independent component analysis algorithm (ICA). We created two different sets of six computer-generated phantoms with one and two different simulated activation time courses present in 10 regions of interest. The phantoms also contained real subject rigid and nonrigid body motion and noise. Sensitivity of detection, defined as the true-positive activation rate at false-positive activation rates varying between 0.0001 and 0.01, was compared between methods. Additionally, specificity of detection of the irregular, transient signal of interest was assessed by comparing the number of signal time courses detected by each algorithm. The results suggest that the increased sensitivity of 2dTCA over TCA in detecting this particular signal of interest is comparable to the detection with ICA, but with fewer other signals detected. A few examples of the successful application of 2dTCA to the localization of interictal activity in preliminary studies of temporal lobe epilepsy are also described.

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Section: Tca Vs 2dtca Methodsmentioning

confidence: 99%

Section: Tca Vs 2dtca Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Development of 2dTCA for the detection of irregular, transient bold activity

Morgan¹,

Li²,

Abou‐Khalil³

et al. 2007

Human Brain Mapping

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show abstract

“…One such data-driven analysis technique is temporal clustering analysis (TCA) (Liu et al, 2000;Yee and Gao 2002;Gao and Yee 2003;Zhao et al, 2004;Lu et al, 2006). In a previous study, we developed a technique to localize BOLD interictal epileptiform activity using TCA methods (Morgan et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

Cluster analysis detection of functional MRI activity in temporal lobe epilepsy

2007

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Purpose-The objective of this pilot study was to evaluate the ability of a novel method of cluster analysis, 2dTCA, for identifying and characterizing peak fluctuations in fMRI BOLD signals in the temporal lobes and the default-mode network in temporal lobe epilepsy (TLE) without EEG.Methods-BOLD fMRI images were acquired in seventeen TLE patients and compared to EEG. The timing of significant transient BOLD peaks was estimated by 2dTCA, and activation maps were determined.Results-Sixteen subjects (94%) showed apparent temporal lobe activation. Mesial temporal activation was present in 76.4% (13 patients). Temporal lobe or insula activations were detected ipsilateral to the EEG focus in 64.7% (11 patients), bilaterally with no predominance in 29.4% (5 patients), and exclusively contralateral to the EEG focus in none. Eleven subjects showed activation in the so-called default-mode network including posterior cingulate, bilateral posterior parietal cortex, and sometimes anterior cingulate cortex.Conclusions-These results demonstrate significant positive BOLD fluctuations in the temporal lobes and default-mode regions in a higher percentage of TLE patients than previously reported using other methods. These fluctuations appear physiologically relevant and suggest increased neural activity which may not be detected on scalp EEG, but which may be important in understanding the mechanisms and origins of epileptic discharges.

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“…Liu et al 27 and Gao et al 28 proposed TCA to detect the time windows of the maximal brain responses in fMRI. In this study, TCA was used to identify the pattern of brain activation regardless of anatomical variances because of the great spatiotemporal heterogeneity in the cortical vasculature.…”

Section: Original Temporal Clustering Analysis Methodsmentioning

confidence: 99%

Functional laser speckle imaging of cerebral blood flow under hypothermia

Miao

Zhu

et al. 2011

J. Biomed. Opt.

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Abstract. Hypothermia can unintentionally occur in daily life, e.g., in cardiovascular surgery or applied as therapeutics in the neurosciences critical care unit. So far, the temperature-induced spatiotemporal responses of the neural function have not been fully understood. In this study, we investigated the functional change in cerebral blood flow (CBF), accompanied with neuronal activation, by laser speckle imaging (LSI) during hypothermia. Laser speckle images from Sprague-Dawley rats (n = 8, male) were acquired under normothermia (37• C) and moderate hypothermia (32• C). For each animal, 10 trials of electrical hindpaw stimulation were delivered under both temperatures. Using registered laser speckle contrast analysis and temporal clustering analysis (TCA), we found a delayed response peak and a prolonged response window under hypothermia. Hypothermia also decreased the activation area and the amplitude of the peak CBF. The combination of LSI and TCA is a high-resolution functional imaging method to investigate the spatiotemporal neurovascular coupling in both normal and pathological brain functions. C 2011 Society of Photo-Optical Instrumentation Engineers (SPIE).

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Improved detection of time windows of brain responses in fMRI using modified temporal clustering analysis

Cited by 29 publications

References 11 publications

Development of 2dTCA for the detection of irregular, transient bold activity

Development of 2dTCA for the detection of irregular, transient bold activity

Cluster analysis detection of functional MRI activity in temporal lobe epilepsy

Functional laser speckle imaging of cerebral blood flow under hypothermia

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