No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Functional MR Imaging in Real-Time using a sliding-window correlation technique
... During recent years the sensitivity of fMRI data acquisition methods has been continuously improved and changes in brain activity (e.g., in motor and visual cortex) can now be detected within a few minutes of data acquisition. These advances stimulated the development of "real-time fMRI" [Cox et al., 1995;Goddard et al., 1997;Lee et al., 1998;Gering and Weber 1998;Cohen et al., 1998;Frank et al., 1999;Voyvodic 1999;Yoo et al., 1999;Gembris et al., 2000], which is characterized by steady state data acquisition, image reconstruction, motion correction, and statistical image analysis during the ongoing scan, preferably with a time delay of less than a single TR cycle. With realtime fMRI the delay between task initiation and clear display of involved cortical areas is no longer determined by computation time. ...
... In contrast to most previous approaches for real-time fMRI, which used cumulative statistical methods (Fig. 1a) and modified hardware, we have developed data processing methods for Functional Imaging in REal time (FIRE) on a standard clinical whole-body scanner, which enables continuous monitoring of fMRI signal changes during the ongoing scan with constant sensitivity. These methods include real-time image transfer and display [Schor et al., 1998], "sliding-window" correlation analysis ( Fig. 1b) [Gembris et al., 2000], reference vector optimization [Gembris et al., 2000], and real-time motion correction [Mathiak and Posse, 2000]. ...
... In contrast to most previous approaches for real-time fMRI, which used cumulative statistical methods (Fig. 1a) and modified hardware, we have developed data processing methods for Functional Imaging in REal time (FIRE) on a standard clinical whole-body scanner, which enables continuous monitoring of fMRI signal changes during the ongoing scan with constant sensitivity. These methods include real-time image transfer and display [Schor et al., 1998], "sliding-window" correlation analysis ( Fig. 1b) [Gembris et al., 2000], reference vector optimization [Gembris et al., 2000], and real-time motion correction [Mathiak and Posse, 2000]. ...
Real-time fMRI is a rapidly emerging methodology that enables monitoring changes in brain activity during an ongoing experiment. In this article we demonstrate the feasibility of performing single-event sensory, motor, and higher cognitive tasks in real-time on a clinical whole-body scanner. This approach requires sensitivity optimized fMRI methods: Using statistical parametric mapping we quantified the spatial extent of BOLD contrast signal changes as a function of voxel size and demonstrate that sacrificing spatial resolution and readout bandwidth improves the detection of signal changes in real time. Further increases in BOLD contrast sensitivity were obtained by using real-time multi-echo EPI. Real-time image analysis was performed using our previously described Functional Imaging in REal time (FIRE) software package, which features real-time motion compensation, sliding window correlation analysis, and automatic reference vector optimization. This new fMRI methodology was validated using single-block design paradigms of standard visual, motor, and auditory tasks. Further, we demonstrate the sensitivity of this method for online detection of higher cognitive functions during a language task using single-block design paradigms. Finally, we used single-event fMRI to characterize the variability of the hemodynamic impulse response in primary and supplementary motor cortex in consecutive trials using single movements. Real-time fMRI can improve reliability of clinical and research studies and offers new opportunities for studying higher cognitive functions. Hum. Brain Mapping 12:25–41, 2001. © 2001 Wiley-Liss, Inc.
... During recent years the sensitivity of fMRI data acquisition methods has been continuously improved and changes in brain activity (e.g., in motor and visual cortex) can now be detected within a few minutes of data acquisition. These advances stimulated the development of "real-time fMRI" [Cox et al., 1995;Goddard et al., 1997;Lee et al., 1998;Gering and Weber 1998;Cohen et al., 1998;Frank et al., 1999;Voyvodic 1999;Yoo et al., 1999;Gembris et al., 2000], which is characterized by steady state data acquisition, image reconstruction, motion correction, and statistical image analysis during the ongoing scan, preferably with a time delay of less than a single TR cycle. With realtime fMRI the delay between task initiation and clear display of involved cortical areas is no longer determined by computation time. ...
... In contrast to most previous approaches for real-time fMRI, which used cumulative statistical methods (Fig. 1a) and modified hardware, we have developed data processing methods for Functional Imaging in REal time (FIRE) on a standard clinical whole-body scanner, which enables continuous monitoring of fMRI signal changes during the ongoing scan with constant sensitivity. These methods include real-time image transfer and display [Schor et al., 1998], "sliding-window" correlation analysis ( Fig. 1b) [Gembris et al., 2000], reference vector optimization [Gembris et al., 2000], and real-time motion correction [Mathiak and Posse, 2000]. ...
... In contrast to most previous approaches for real-time fMRI, which used cumulative statistical methods (Fig. 1a) and modified hardware, we have developed data processing methods for Functional Imaging in REal time (FIRE) on a standard clinical whole-body scanner, which enables continuous monitoring of fMRI signal changes during the ongoing scan with constant sensitivity. These methods include real-time image transfer and display [Schor et al., 1998], "sliding-window" correlation analysis ( Fig. 1b) [Gembris et al., 2000], reference vector optimization [Gembris et al., 2000], and real-time motion correction [Mathiak and Posse, 2000]. ...
Real-time fMRI is a rapidly emerging methodology that enables monitoring changes in brain activity during an ongoing experiment. In this article we demonstrate the feasibility of performing single-event sensory, motor, and higher cognitive tasks in real-time on a clinical whole-body scanner. This approach requires sensitivity optimized fMRI methods: Using statistical parametric mapping we quantified the spatial extent of BOLD contrast signal changes as a function of voxel size and demonstrate that sacrificing spatial resolution and readout bandwidth improves the detection of signal changes in real time. Further increases in BOLD contrast sensitivity were obtained by using real-time multi-echo EPI. Real-time image analysis was performed using our previously described Functional Imaging in REal time (FIRE) software package, which features real-time motion compensation, sliding window correlation analysis, and automatic reference vector optimization. This new fMRI methodology was validated using single-block design paradigms of standard visual, motor, and auditory tasks. Further, we demonstrate the sensitivity of this method for online detection of higher cognitive functions during a language task using single-block design paradigms. Finally, we used single-event fMRI to characterize the variability of the hemodynamic impulse response in primary and supplementary motor cortex in consecutive trials using single movements. Real-time fMRI can improve reliability of clinical and research studies and offers new opportunities for studying higher cognitive functions.
... Real-time functional magnetic resonance imaging (fMRI) is a promising tool for the noninvasive monitoring of brain activity during an ongoing imaging session. In the recent past, various efforts have been made to develop favorable acquisition strategies (Yoo et al., 1999) and to reformulate conventional off-line analysis techniques (Cox et al., 1995; Gembris et al., 2000; Posse et al., 2001) to permit the highly computationally demanding real-time ap- plications. ...
... So far, only standard univariate statistics like correlation (Bandettini et al., 1993) and multiple regression analyses (Friston et al., 1995a) have been successfully employed for real-time analysis of fMRI data. They utilized two different approaches: cumulative (Cox et al., 1995) and sliding window (Gembris et al., 2000; Posse et al., 2001). ...
... On the other hand, due to the limited signal-to-noise ratios, the overall specificity will be strongly affected by the reduced observation and collection times. For both the approaches the accuracy can be improved by using a real-time motion correction technique (Mathiak and Posse, 2000) and an optimized modeling of the reference and trend signals (Gembris et al., 2000; Posse et al., 2001). Further developments in real-time analysis and representation of fMRI data may comply better with the complexity of neural responses. ...
Real-time functional magnetic resonance imaging (fMRI) enables one to monitor a subject's brain activity during an ongoing session. The availability of online information about brain activity is essential for developing and refining interactive fMRI paradigms in research and clinical trials and for neurofeedback applications. Data analysis for real-time fMRI has traditionally been based on hypothesis-driven processing methods. Off-line data analysis, conversely, may be usefully complemented by data-driven approaches, such as independent component analysis (ICA), which can identify brain activity without a priori temporal assumptions on brain activity. However, ICA is commonly considered a time-consuming procedure and thus unsuitable to process the high flux of fMRI data while they are acquired. Here, by specific choices regarding the implementation, we exported the ICA framework and implemented it into real-time fMRI data analysis. We show that, reducing the ICA input to a few points within a time-series in a sliding-window approach, computational times become compatible with real-time settings. Our technique produced accurate dynamic readouts of brain activity as well as a precise spatiotemporal history of quasistationary patterns in the form of cumulative activation maps and time courses. Results from real and simulated motor activation data show comparable performances for the proposed ICA implementation and standard linear regression analysis applied either in a sliding-window or in a cumulative mode. Furthermore, we demonstrate the possibility of monitoring transient or unexpected neural activities and suggest that real-time ICA may provide the fMRI researcher with a better understanding and control of subjects' behaviors and performances.
... Indeed, the dynamic brain activation patterns could be viewed in real time on a monitor in the control room using the custom real-time fMRI software TurboFIRE [31], [32], [33], [34]. During pilot experiments a large increase in brain activation of one participant was noted by the experimenters at the same time as the video showed passing an 18-wheel truck (Fig. 3, bottom panel). ...
How do in-vehicle telematics devices influence mind-on-the- drive? We determined the spatio-temporal properties of the brain mechanisms during a simple visual event detection and motor response in a validated driving-like protocol. We used the safe and non-invasive brain imaging methods of functional magnetic resonance imaging (fMRI) and Magnetoencephalography (MEG) to locate the essential brain activated structures and their corresponding temporal dynamics. This study sets the foundation for determining the fundamental brain mechanisms by which secondary tasks (such as cell phone use) may affect the responses to visual events in a laboratory setting. Improved knowledge of the brain mechanisms underlying selective attention in such driving-like situations may give rise to methods for improving mind-on-the-drive.
... Indeed, the dynamic brain activation patterns could be viewed in real time on a monitor in the control room using the custom real-time fMRI software TurboFIRE [31], [32], [33], [34]. During pilot experiments a large increase in brain activation of one participant was noted by the experimenters at the same time as the video showed passing an 18-wheel truck (Fig. 3, bottom panel). ...
How do in-vehicle telematics devices influence mind-on-the-drive? We determined the spatio-temporal properties of the brain mechanisms during a simple visual event detection and motor response in a validated driving-like protocol. We used the safe and non-invasive brain imaging methods of functional magnetic resonance imaging (fMRI) and Magnetoencephalography (MEG) to locate the essential brain activated structures and their corresponding temporal dynamics. This study sets the foundation for determining the fundamental brain mechanisms by which secondary tasks (such as cell phone use) may affect the responses to visual events in a laboratory setting. Improved knowledge of the brain mechanisms underlying selective attention in such driving-like situations may give rise to methods for improving mind-on-the-drive.
... Correction for temporal correlations was not available. Cummulative correlation analysis (Gembris et al 2000) was performed for comparison. Activation maps were spatially smoothed using a 3×3 median filter. ...
In this study, a new approach to high-speed fMRI using multi-slab echo-volumar imaging (EVI) is developed that minimizes geometrical image distortion and spatial blurring, and enables nonaliased sampling of physiological signal fluctuation to increase BOLD sensitivity compared to conventional echo-planar imaging (EPI). Real-time fMRI using whole brain 4-slab EVI with 286 ms temporal resolution (4mm isotropic voxel size) and partial brain 2-slab EVI with 136 ms temporal resolution (4×4×6 mm(3) voxel size) was performed on a clinical 3 Tesla MRI scanner equipped with 12-channel head coil. Four-slab EVI of visual and motor tasks significantly increased mean (visual: 96%, motor: 66%) and maximum t-score (visual: 263%, motor: 124%) and mean (visual: 59%, motor: 131%) and maximum (visual: 29%, motor: 67%) BOLD signal amplitude compared with EPI. Time domain moving average filtering (2s width) to suppress physiological noise from cardiac and respiratory fluctuations further improved mean (visual: 196%, motor: 140%) and maximum (visual: 384%, motor: 200%) t-scores and increased extents of activation (visual: 73%, motor: 70%) compared to EPI. Similar sensitivity enhancement, which is attributed to high sampling rate at only moderately reduced temporal signal-to-noise ratio (mean: -52%) and longer sampling of the BOLD effect in the echo-time domain compared to EPI, was measured in auditory cortex. Two-slab EVI further improved temporal resolution for measuring task-related activation and enabled mapping of five major resting state networks (RSNs) in individual subjects in 5 min scans. The bilateral sensorimotor, the default mode and the occipital RSNs were detectable in time frames as short as 75 s. In conclusion, the high sampling rate of real-time multi-slab EVI significantly improves sensitivity for studying the temporal dynamics of hemodynamic responses and for characterizing functional networks at high field strength in short measurement times.
... It is well suited for realtime applications, as it has a fast classification response and a fast training. We have recently interfaced the classifier to our real-time fMRI analysis software (TurboFIRE) (Gembris et al., 2000; Gao and Posse, 2004; Posse et al., 2001 Posse et al., , 2003) (see also http://www.mic.health.unm.edu/turbofire) and are currently evaluating training and testing performance with cognitive paradigms . The limitations of the method are related to the sparsity of the information in the brain. ...
Pattern classification in functional MRI (fMRI) is a novel methodology to automatically identify differences in distributed neural substrates resulting from cognitive tasks. Reliable pattern classification is challenging due to the high dimensionality of fMRI data, the small number of available data sets, interindividual differences, and dependence on the acquisition methodology. Thus, most previous fMRI classification methods were applied in individual subjects. In this study, we developed a novel approach to improve multiclass classification across groups of subjects, field strengths, and fMRI methods. Spatially normalized activation maps were segmented into functional areas using a neuroanatomical atlas and each map was classified separately using local classifiers. A single multiclass output was applied using a weighted aggregation of the classifier's outputs. An Adaboost technique was applied, modified to find the optimal aggregation of a set of spatially distributed classifiers. This Adaboost combined the region-specific classifiers to achieve improved classification accuracy with respect to conventional techniques. Multiclass classification accuracy was assessed in an fMRI group study with interleaved motor, visual, auditory, and cognitive task design. Data were acquired across 18 subjects at different field strengths (1.5 T, 4 T), with different pulse sequence parameters (voxel size and readout bandwidth). Misclassification rates of the boosted classifier were between 3.5% and 10%, whereas for the single classifier, these were between 15% and 23%, suggesting that the boosted classifier provides a better generalization ability together with better robustness. The high computational speed of boosting classification makes it attractive for real-time fMRI to facilitate online interpretation of dynamically changing activation patterns.
This paper proposes an online framework to dynamically model the impulse-response function (IRF) of a system having shared-band noises, to facilitate the output prediction in real-time. The online independent-component analysis is performed to un-mix the measured signal. The automatic recognition of the anticipated IRF, amongst the unmixed signals, is achieved by proposing a peak-detection-&-correlation technique. A mathematical model of the acquired IRF is, then, dynamically identified by the subspace-based state-space method. The validity of the proposed methodology is demonstrated by a simulation study where the anticipated IRF is blindly identified and then modelled with an accuracy of 92%. The framework has the potential of online modelling of a system having shared-band noises.
Macroscopic magnetic field inhomogeneities severely limit sensitivity of blood oxygenation level-dependent (BOLD) functional MRI (fMRI) in frontal and central brain regions close to brain stem. A single-shot multiecho echo-planar imaging method (TurboPEPSI) was developed that combines quantitative T(2)* mapping with gradient compensation of local susceptibility inhomogeneities in multiple volumes of interest (VOIs). Gradient compensation was optimized in individual subjects based on magnetic field mapping and applied at selected echo times, interleaved with acquisition of uncompensated echoes. Intrinsic T(2)* values from uncompensated echoes were obtained in real-time simultaneously with effective T(2)* values from gradient compensated echoes. It is demonstrated that up to three VOIs can be compensated in a single excitation, in addition to collecting uncompensated data, using 8-echo acquisition on a clinical 1.5 Tesla scanner. A theory was developed to optimize the sequence of uncompensated and compensated echoes to achieve maximum BOLD sensitivity. Gradient compensation increased effective T(2)* values in left and right amygdala on average by 18.8 +/- 7.5 ms, while maintaining sensitivity in uncompensated brain areas. In orbitofrontal cortex effective T(2)* values increased by 22.2 +/- 5.3 ms. A CO(2) challenge paradigm was used to demonstrate that this gradient compensation method significantly enhances BOLD signal changes in amygdala as compared to conventional echo-planar imaging (EPI) and uncompensated TurboPEPSI.
ResearchGate has not been able to resolve any references for this publication.