Comparison of two exploratory data analysis methods for fMRI: fuzzy clustering vs. principal component analysis

Baumgartner, Richard N.; Ryner, Lawrence; Richter, Wolfgang; Summers, Randy; Jarmasz, M.; Somorjai, R. L.

doi:10.1016/s0730-725x(99)00102-2

Cited by 159 publications

(117 citation statements)

References 18 publications

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“…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%

“…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]. Zhao et al [2004] reported that TCA and ICA methods performed similarly in generating activation maps from event-related fMRI experiments, but that TCA was more computationally efficient, repeatable, and easy to adapt to multi-subject data.…”

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: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Morgan¹,

Li²,

Abou‐Khalil³

et al. 2007

Human Brain Mapping

View full text Add to dashboard Cite

show abstract

“…1C and 2C) with closest neighbors using K-means approach (Baumgartner et al, 2000;Hanson et al, 2007) to give 20 to 200 cortical parcels (N p = 20, 40, 60, 80, 100, 120, 140, 160, 180 and 200). Increase in N p results in a decrease in the parcel size (see Fig.…”

Section: Simulation Of Cortical Parcel Signalsmentioning

confidence: 99%

Analysis of infant cortical synchrony is constrained by the number of recording electrodes and the recording montage

Tokariev

Vanhatalo

Palva

2016

Clinical Neurophysiology

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Dense array EEG Connectivity Brain development h i g h l i g h t sAnalysis of brain connectivity from the neonatal EEG is strongly enhanced by adding the number of electrodes. Sensitivity and specificity of cortical synchrony estimates depend on the analysis montage; average and Laplacian montage have the best performance. The number of electrodes defines the optimal montage and it also sets the limits for the level of analytic details. a b s t r a c tObjective: To assess how the recording montage in the neonatal EEG influences the detection of cortical source signals and their phase interactions. Methods: Scalp EEG was simulated by forward modeling 20-200 simultaneously active sources covering the cortical surface of a realistic neonatal head model. We assessed systematically how the number of scalp electrodes (11-85), analysis montage, or the size of cortical sources affect the detection of cortical phase synchrony. Statistical metrics were developed for quantifying the resolution and reliability of the montages. Results: The findings converge to show that an increase in the number of recording electrodes leads to a systematic improvement in the detection of true cortical phase synchrony. While there is always a ceiling effect with respect to discernible cortical details, we show that the average and Laplacian montages exhibit superior specificity and sensitivity as compared to other conventional montages. Conclusions: Reliability in assessing true neonatal cortical synchrony is directly related to the choice of EEG recording and analysis configurations. Because of the high conductivity of the neonatal skull, the conventional neonatal EEG recordings are spatially far too sparse for pertinent studies, and this loss of information cannot be recovered by re-montaging during analysis. Significance: Future neonatal EEG studies will need prospective planning of recording configuration to allow analysis of spatial details required by each study question. Our findings also advice about the level of details in brain synchrony that can be studied with existing datasets or by using conventional EEG recordings.

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“…Since there is no a priori temporal model in rs-fMRI, datadriven methods have been adopted including seed correlation analysis [6], clustering analysis [7], [8] principal and independent component analysis (ICA) [9], [10], canonical correlation analysis [11]. Recent studies of functional connectivity have presented evidence towards the non-stationary brain dynamics [12], [13].…”

Section: Introductionmentioning

confidence: 99%

Total-activation regularized deconvolution of resting-state fMRI leads to reproducible networks with spatial overlap

Karahanoğlu

Ville

2016

2016 24th European Signal Processing Conference (EUSIPCO)

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Abstract-Spontaneous activations in resting-state fMRI have been shown to corroborate recurrent intrinsic functional networks. Recent studies have explored integration of brain function in terms of spatially overlapping networks. We have proposed a method to recover not only spatially but also temporally overlapping networks, which we named innovation-driven coactivation patterns (iCAPs). These networks are driven by the sparse innovation signals recovered from Total Activation (TA), a spatiotemporal regularization framework for fMRI deconvolution. The fMRI data is processed with TA, which uses the inverse of the hemodynamic response function-as a linear differential operator-combined with the derivative in the regularization with`1-norm. As a result, sparse innovation signals are reconstructed as the deconvolved fMRI time series. Temporal clustering of innovation signals lead to iCAPs. In this work, we investigate the reproducible iCAPs in individuals with relapsingremitting multiple sclerosis and healthy volunteers.

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Comparison of two exploratory data analysis methods for fMRI: fuzzy clustering vs. principal component analysis

Cited by 159 publications

References 18 publications

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

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

Analysis of infant cortical synchrony is constrained by the number of recording electrodes and the recording montage

Total-activation regularized deconvolution of resting-state fMRI leads to reproducible networks with spatial overlap

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