FMRI Signal Analysis Using Empirical Mean Curve Decomposition

Fan, Daidu; Zhu, Dajiang; Lv, Jinglei; Liu, Tianming

doi:10.1109/tbme.2012.2221125

Cited by 22 publications

(11 citation statements)

References 53 publications

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“…2012; Barbé et al 2012; Deng et al 2013). Such approaches may offer an alternative to circumvent some of the aforementioned challenges associated with simultaneous EEG and fMRI.…”

Section: Introductionmentioning

confidence: 99%

Lateralization and localization of epilepsy related hemodynamic foci using presurgical fMRI

Zhang

Brinkmann

et al. 2015

Clinical Neurophysiology

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Objective The aim was to develop a method for the purpose of localizing epilepsy related hemodynamic foci for patients suffering intractable focal epilepsy using task-free fMRI alone. Methods We studied three groups of subjects: patients with intractable focal epilepsy, healthy volunteers performing motor tasks, and healthy volunteers in resting state. We performed spatial independent component analysis (ICA) on the fMRI alone data and developed a set of IC selection criteria to identify epilepsy related ICs. The method was then tested in the two healthy groups. Results In seven out of the nine surgery patients, identified ICs were concordant with surgical resection. Our results were also consistent with presurgical evaluation of the remaining one patient without surgery and may explain why she was not suitable for resection treatment. In the motor task study of ten healthy subjects, our method revealed components with concordant spatial and temporal features as expected from the unilateral motor tasks. In the resting state study of seven healthy subjects, the method successfully rejected all components in four out of seven subjects as non-epilepsy related components. Conclusion These results suggest the lateralization and localization value of fMRI alone in presurgical evaluation for patients with intractable unilateral focal epilepsy. Significance The proposed method is noninvasive in nature and easy to implement. It has the potential to be incorporated in current presurgical workup for treating intractable focal epilepsy patients.

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“…2012; Barbé et al 2012; Deng et al 2013). Such approaches may offer an alternative to circumvent some of the aforementioned challenges associated with simultaneous EEG and fMRI.…”

Section: Introductionmentioning

confidence: 99%

Lateralization and localization of epilepsy related hemodynamic foci using presurgical fMRI

Zhang

Brinkmann

et al. 2015

Clinical Neurophysiology

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“…Voxel Energy Measurement: We first extract the maxima and minima (extrema) points for each voxel's time series using the extrema extraction method [22], which is an effective and efficient technique for single-voxel time series extraction and noise removal [14].…”

Section: Volumetric Time Series Extractionmentioning

confidence: 99%

“…If the time series is averaged, the varying trend in the time series that reflects the brain activity will be lost. Some studies [13,14] have focused on analyzing the fMRI time series of each individual voxel while ignoring structural information in the spatial domain. For instance, the multilinear decomposition model [13] analyzes the time profile of the voxel vector converted from the 3D tensor in the spatial domain.…”

Section: Introductionmentioning

confidence: 99%

Spatio-Temporal Tensor Analysis for Whole-Brain fMRI Classification

Ma¹,

He²,

Lu³

et al. 2016

Proceedings of the 2016 SIAM International Conference on Data Mining

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Owing to prominence as a research and diagnostic tool in human brain mapping, whole-brain fMRI image analysis has been the focus of intense investigation. Conventionally, input fMRI brain images are converted into vectors or matrices and adapted in kernel based classifiers. fMRI data, however, are inherently coupled with sophisticated spatio-temporal tensor structure (i.e., 3D space × time). Valuable structural information will be lost if the tensors are converted into vectors. Furthermore, time series fMRI data are noisy, involving time shift and low temporal resolution. To address these analytic challenges, more compact and discriminative representations for kernel modeling are needed. In this paper, we propose a novel spatio-temporal tensor kernel (STTK) approach for whole-brain fMRI image analysis. Specifically, we design a volumetric time series extraction approach to model the temporal data, and propose a spatio-temporal tensor based factorization for feature extraction. We further leverage the tensor structure to encode prior knowledge in the kernel. Extensive experiments using real-world datasets demonstrate that our proposed approach effectively boosts the fMRI classification performance in diverse brain disorders (i.e., Alzheimer's disease, ADHD and HIV).

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“…2) Opportunities and Challenges in Mapping Multiple TimeSeries Feature Spaces: Regarding the fusion and transfer learning of time-series feature curves from two spaces, the frequencies of fMRI signals and low-level multimedia features are typically at quite different scales [112], e.g., 0.5 versus 30 Hz. Hence, we need to extract the low-frequency components of low-level feature curves, and find the corresponding components from the fMRI signals or fMRI-derived feature curves.…”

Section: ) Opportunities and Challenges In Mapping Multiple Static Fmentioning

confidence: 99%

“…The problem of frequency discrepancy is not applied in EEG-and MEG-based studies, since EEG and MEG have much higher temporal resolution. In the signal processing literature, a variety of multiscale signal decomposition approaches have been developed such as the empirical mode decomposition algorithm [112], [113] and wavelet transform method [114]. These signal decomposition methods can be potentially modified or improved to analyze the correlative structures between time-series curves of highlevel neuroimaging features and low-level multimedia features at multiple scales.…”

Section: ) Opportunities and Challenges In Mapping Multiple Static Fmentioning

confidence: 99%

Merging Neuroimaging and Multimedia: Methods, Opportunities, and Challenges

Liu

et al. 2014

IEEE Trans. Human-Mach. Syst.

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Neuroimaging and brain mapping can provide meaningful guidance to multimedia analyses. and advanced computational multimedia analysis can be used to better understand the functional mechanisms of the human brain. Essentially, brain imaging and brain mapping techniques can serve as a bridge that links the digital representation of multimedia and the perception and comprehension of its content. This paper summarizes methods that integrate brain imaging with multimedia analysis and discusses the opportunities and challenges in this interdisciplinary field. In general, quantitative modeling of brain responses during multimedia comprehension has advanced content-based multimedia studies such as image and video classification and tagging. Multimedia analysis has promoted functional brain mapping by using naturalistic multimedia as stimuli during neuroimaging. Challenges and opportunities in merging neuroimaging and multimedia include the quantification of the brain's responses, the quantification of multimedia, and the mapping between brain responses and computational multimedia features.

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FMRI Signal Analysis Using Empirical Mean Curve Decomposition

Cited by 22 publications

References 53 publications

Lateralization and localization of epilepsy related hemodynamic foci using presurgical fMRI

Lateralization and localization of epilepsy related hemodynamic foci using presurgical fMRI

Spatio-Temporal Tensor Analysis for Whole-Brain fMRI Classification

Merging Neuroimaging and Multimedia: Methods, Opportunities, and Challenges

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