Dynamic monitoring of brain activation under visual stimulation using fMRI—The advantage of real-time fMRI with sliding window GLM analysis

Nakai, T.; Bagarinao, Epifanio; Matsuo, Keizo; Ohgami, Yuko; Kato, Chikako

doi:10.1016/j.jneumeth.2006.04.017

Cited by 24 publications

(27 citation statements)

References 28 publications

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“…Our proposed method, using a semi-real simulation with the assumption that fNIRS signals were possibly contaminated with global task-related SBF, achieved a significantly higher estimation accuracy than conventional design matrices of GLM, which have been used in several studies [5,16,[26][27][28][29]. However, brain activity estimations with real fNIRS data are more challenging.…”

Section: Advantages Of Proposed Methodsmentioning

confidence: 98%

“…SWA sets a certain sample range sliding-window, and the most recent data are used for computing statistics [16]. When a new sample is measured, the sliding-window slides to the next sample, which includes the newest and discards the oldest sample ( Figure 1).…”

Section: Sliding-window Analysismentioning

confidence: 99%

“…As the sample window is maintained at the same size, it has the advantage of constant dynamics estimation for the same parameters. In this study, we set a 350-sample wide sliding window and it covered at least a single-trial [5,16]. The sliding-window was applied for each measuring time (i.e., excluding only the oldest sample).…”

Section: Sliding-window Analysismentioning

confidence: 99%

“…and standard (CHM + CHM (1) + CHM (2) + Const.) [5,16,[26][27][28][29]. Both of the comparative design matrices were also applied to SWA for real-time CBF estimation.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…Because the aims of real-time signal processing of fNIRS-based applications are to estimate true brain activation and reduce artifacts from raw signals, we proposed a real-time ShortPCA GLM (rt-ShortPCA GLM) with sliding-window analysis (SWA), which is sometimes used for parameter dynamics estimation in functional magnetic resonance imaging (fMRI) [15,16] and fNIRS analysis [5] or removing motion artifact from fNIRS signals [17]. This simple technique may retain the advantages of off-line ShortPCA GLM, i.e., simplicity and low physical constraint.…”

Section: Introductionmentioning

confidence: 99%

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Real-Time Reduction of Task-Related Scalp-Hemodynamics Artifact in Functional Near-Infrared Spectroscopy with Sliding-Window Analysis

et al. 2018

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Functional near-infrared spectroscopy (fNIRS) is an effective non-invasive neuroimaging technique for measuring hemoglobin concentration in the cerebral cortex. Owing to the nature of fNIRS measurement principles, measured signals can be contaminated with task-related scalp blood flow (SBF), which is distributed over the whole head and masks true brain activity. Aiming for fNIRS-based real-time application, we proposed a real-time task-related SBF artifact reduction method. Using a principal component analysis, we estimated a global temporal pattern of SBF from few short-channels, then we applied a general linear model for removing it from long-channels that were possibly contaminated by SBF. Sliding-window analysis was applied for both signal steps for real-time processing. To assess the performance, a semi-real simulation was executed with measured short-channel signals in a motor-task experiment. Compared with conventional techniques with no elements of SBF, the proposed method showed significantly higher estimation performance for true brain activation under a task-related SBF artifact environment.

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Section: Advantages Of Proposed Methodsmentioning

confidence: 98%

Section: Sliding-window Analysismentioning

confidence: 99%

Section: Sliding-window Analysismentioning

confidence: 99%

“…and standard (CHM + CHM (1) + CHM (2) + Const.) [5,16,[26][27][28][29]. Both of the comparative design matrices were also applied to SWA for real-time CBF estimation.…”

Section: Performance Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Real-Time Reduction of Task-Related Scalp-Hemodynamics Artifact in Functional Near-Infrared Spectroscopy with Sliding-Window Analysis

et al. 2018

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Real‐time functional MRI using pseudo‐continuous arterial spin labeling

Hernandez‐García

Jahanian

Greenwald

et al. 2011

Magnetic Resonance in Med

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The first implementation of real-time acquisition and analysis of arterial spin labeling-based functional magnetic resonance imaging time series is presented in this article. The implementation uses a pseudo-continuous labeling scheme followed by a spiral k-space acquisition trajectory. Real-time reconstruction of the images, preprocessing, and regression analysis of the functional magnetic resonance imaging data were implemented on a laptop computer interfaced with the MRI scanner. The method allows the user to track the current raw data, subtraction images, and the cumulative t-statistic map overlaid on a cumulative subtraction image. The user is also able to track the time course of individual time courses and interactively selects a region of interest as a nuisance covariate. The pulse sequence allows the user to adjust acquisition and labeling parameters while observing their effect on the image within two successive pulse repetition times. This method is demonstrated by two functional imaging experiments: a simultaneous finger-tapping and visual stimulation paradigm, and a bimanual finger-tapping task. Conventional functional magnetic resonance imaging (fMRI) collects blood oxygen level-dependent (BOLD)-contrast MR images of a subject's brain while performing a cognitive task, whereas subsequent image reconstruction and analysis are performed offline (i.e., on a separate computer after the experiment is completed). Real-time fMRI is an exciting extension to conventional fMRI techniques that enables the user to analyze fMRI data as it is being collected. Thus, in real-time fMRI, the results are immediately available as the subject is being scanned, and the results can be used to reveal and guide the subject's cognitive processes. It can also facilitate the experimenter's parameter selections or a clinician's interventions (1).Several real-time analysis methods have been implemented for online processing of BOLD data, including cumulative correlation (2), sliding-window correlations with reference vector optimization (3), online general linear model analysis (4), and combined methods to collect behavioral, physiological, and MRI data while performing near real-time statistical analysis (5). All the above methods can facilitate real-time analysis, e.g., the incremental algorithms are useful in monitoring ongoing activation, and the sliding window approaches can improve localization of dynamic activity in time (4). Given current computer-processing speed, any of these approaches can be used to display realtime activation maps. This allows for several real-time applications: online data quality control, real-time functional activation monitoring, interactive paradigms based on the subject's dynamic functional activity (fMRI biofeedback or brain-computer interface; Ref. 6), and autonomous control of neural activation using real-time fMRI. These techniques have been used to study subjects' modulation of motor-area cortical activation and emotional processing (7-11).However, despite the efficiency of BOLD-contrast...

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Real-Time Scalp-Hemodynamics Artifact Reduction Using a Sliding-Window General Linear Model: A Functional Near-Infrared Spectroscopy Study

Oda

Sato

Nambu

et al. 2017

Neural Information Processing

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Dynamic monitoring of brain activation under visual stimulation using fMRI—The advantage of real-time fMRI with sliding window GLM analysis

Cited by 24 publications

References 28 publications

Real-Time Reduction of Task-Related Scalp-Hemodynamics Artifact in Functional Near-Infrared Spectroscopy with Sliding-Window Analysis

Real-Time Reduction of Task-Related Scalp-Hemodynamics Artifact in Functional Near-Infrared Spectroscopy with Sliding-Window Analysis

Real‐time functional MRI using pseudo‐continuous arterial spin labeling

Real-Time Scalp-Hemodynamics Artifact Reduction Using a Sliding-Window General Linear Model: A Functional Near-Infrared Spectroscopy Study

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