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Dynamic adjustment of stimuli in real time functional magnetic resonance imaging.

Feng IJ, Jack AI, Tatsuoka C - PLoS ONE (2015)

Bottom Line: We have developed a voxel-wise sequential probability ratio test (SPRT) approach for dynamically determining localization, as well as decision rules for stopping stimulus administration.Moreover, the proposed approach is also able to accurately detect differentially activated areas, and other comparisons between task-related GLM parameters that can be formulated in a hypothesis-testing framework.Finally, we give a demonstration of SPRT being employed in conjunction with a halving algorithm to dynamically adjust stimuli.

View Article: PubMed Central - PubMed

Affiliation: Case Western Reserve University, Department of Epidemiology and Biostatistics, Cleveland, Ohio, United States of America.

ABSTRACT
The conventional fMRI image analysis approach to associating stimuli to brain activation is performed by carrying out a massive number of parallel univariate regression analyses. fMRI blood-oxygen-level dependent (BOLD) signal, the basis of these analyses, is known for its low signal-noise-ratio and high spatial and temporal signal correlation. In order to ensure accurate localization of brain activity, stimulus administration in an fMRI session is often lengthy and repetitive. Real-time fMRI BOLD signal analysis is carried out as the signal is observed. This method allows for dynamic, real-time adjustment of stimuli through sequential experimental designs. We have developed a voxel-wise sequential probability ratio test (SPRT) approach for dynamically determining localization, as well as decision rules for stopping stimulus administration. SPRT methods and general linear model (GLM) approaches are combined to identify brain regions that are activated by specific elements of stimuli. Stimulus administration is dynamically stopped when sufficient statistical evidence is collected to determine activation status across regions of interest, following predetermined statistical error thresholds. Simulation experiments and an example based on real fMRI data show that scan volumes can be substantially reduced when compared with pre-determined, fixed designs while achieving similar or better accuracy in detecting activated voxels. Moreover, the proposed approach is also able to accurately detect differentially activated areas, and other comparisons between task-related GLM parameters that can be formulated in a hypothesis-testing framework. Finally, we give a demonstration of SPRT being employed in conjunction with a halving algorithm to dynamically adjust stimuli.

No MeSH data available.


Related in: MedlinePlus

Differential activation magnitudes, with 0.1 maximum SNR.
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pone.0117942.g006: Differential activation magnitudes, with 0.1 maximum SNR.

Mentions: Voxel-wise SPRT is not only able to detect activated regions but also regions with differential activation. This can be achieved by testing a contrast of the task-related parameters. For instance, in order to detect the areas with higher task A activation magnitude than task B activation magnitude, a one-sided hypothesis can be set as H0: cB= 0 against Ha: cB≥ δ, some δ >0, where c equals [0 1–1]. Suppose a difference greater than 1 is considered practically important here, so that Ha: cB≥ 1. The dataset with 0.1 maximum SNR was analyzed by voxel-wise SPRT and voxel-wise GLM here. The true differential activation structure is displayed in Fig. 6. Only region 1 has a positive difference of magnitude levels of at least 1 showing greater task A activity. Region 2, on the other hand, has a negative difference. There is no activation difference between task A and task B in regions 3 and 4. For the sequential and fixed design approaches, accuracies of identifying the voxels truly having greater than 0.8 differential activation magnitudes were computed, as in Table 4. In addition, the plots showing the voxels with classified differential activation are in Fig. 7. 295 scan volumes are needed by voxel-wise SPRT to achieve around 95% differential activation accuracy in region 1.


Dynamic adjustment of stimuli in real time functional magnetic resonance imaging.

Feng IJ, Jack AI, Tatsuoka C - PLoS ONE (2015)

Differential activation magnitudes, with 0.1 maximum SNR.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4364703&req=5

pone.0117942.g006: Differential activation magnitudes, with 0.1 maximum SNR.
Mentions: Voxel-wise SPRT is not only able to detect activated regions but also regions with differential activation. This can be achieved by testing a contrast of the task-related parameters. For instance, in order to detect the areas with higher task A activation magnitude than task B activation magnitude, a one-sided hypothesis can be set as H0: cB= 0 against Ha: cB≥ δ, some δ >0, where c equals [0 1–1]. Suppose a difference greater than 1 is considered practically important here, so that Ha: cB≥ 1. The dataset with 0.1 maximum SNR was analyzed by voxel-wise SPRT and voxel-wise GLM here. The true differential activation structure is displayed in Fig. 6. Only region 1 has a positive difference of magnitude levels of at least 1 showing greater task A activity. Region 2, on the other hand, has a negative difference. There is no activation difference between task A and task B in regions 3 and 4. For the sequential and fixed design approaches, accuracies of identifying the voxels truly having greater than 0.8 differential activation magnitudes were computed, as in Table 4. In addition, the plots showing the voxels with classified differential activation are in Fig. 7. 295 scan volumes are needed by voxel-wise SPRT to achieve around 95% differential activation accuracy in region 1.

Bottom Line: We have developed a voxel-wise sequential probability ratio test (SPRT) approach for dynamically determining localization, as well as decision rules for stopping stimulus administration.Moreover, the proposed approach is also able to accurately detect differentially activated areas, and other comparisons between task-related GLM parameters that can be formulated in a hypothesis-testing framework.Finally, we give a demonstration of SPRT being employed in conjunction with a halving algorithm to dynamically adjust stimuli.

View Article: PubMed Central - PubMed

Affiliation: Case Western Reserve University, Department of Epidemiology and Biostatistics, Cleveland, Ohio, United States of America.

ABSTRACT
The conventional fMRI image analysis approach to associating stimuli to brain activation is performed by carrying out a massive number of parallel univariate regression analyses. fMRI blood-oxygen-level dependent (BOLD) signal, the basis of these analyses, is known for its low signal-noise-ratio and high spatial and temporal signal correlation. In order to ensure accurate localization of brain activity, stimulus administration in an fMRI session is often lengthy and repetitive. Real-time fMRI BOLD signal analysis is carried out as the signal is observed. This method allows for dynamic, real-time adjustment of stimuli through sequential experimental designs. We have developed a voxel-wise sequential probability ratio test (SPRT) approach for dynamically determining localization, as well as decision rules for stopping stimulus administration. SPRT methods and general linear model (GLM) approaches are combined to identify brain regions that are activated by specific elements of stimuli. Stimulus administration is dynamically stopped when sufficient statistical evidence is collected to determine activation status across regions of interest, following predetermined statistical error thresholds. Simulation experiments and an example based on real fMRI data show that scan volumes can be substantially reduced when compared with pre-determined, fixed designs while achieving similar or better accuracy in detecting activated voxels. Moreover, the proposed approach is also able to accurately detect differentially activated areas, and other comparisons between task-related GLM parameters that can be formulated in a hypothesis-testing framework. Finally, we give a demonstration of SPRT being employed in conjunction with a halving algorithm to dynamically adjust stimuli.

No MeSH data available.


Related in: MedlinePlus