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Functional MRI Preprocessing in Lesioned Brains: Manual Versus Automated Region of Interest Analysis.

Garrison KA, Rogalsky C, Sheng T, Liu B, Damasio H, Winstein CJ, Aziz-Zadeh LS - Front Neurol (2015)

Bottom Line: Significant differences were identified in task-related effect size and percent-activated voxels in this ROI between the automated and manual approaches to ROI analysis.Task interactions, however, were consistent across ROI analysis approaches.These findings support the use of automated approaches to ROI analysis in studies of lesioned brains, provided they employ a task interaction design.

View Article: PubMed Central - PubMed

Affiliation: Department of Psychiatry, Yale School of Medicine , New Haven, CT , USA ; Division of Biokinesiology and Physical Therapy, University of Southern California , Los Angeles, CA , USA ; Brain and Creativity Institute, University of Southern California , Los Angeles, CA , USA.

ABSTRACT
Functional magnetic resonance imaging (fMRI) has significant potential in the study and treatment of neurological disorders and stroke. Region of interest (ROI) analysis in such studies allows for testing of strong a priori clinical hypotheses with improved statistical power. A commonly used automated approach to ROI analysis is to spatially normalize each participant's structural brain image to a template brain image and define ROIs using an atlas. However, in studies of individuals with structural brain lesions, such as stroke, the gold standard approach may be to manually hand-draw ROIs on each participant's non-normalized structural brain image. Automated approaches to ROI analysis are faster and more standardized, yet are susceptible to preprocessing error (e.g., normalization error) that can be greater in lesioned brains. The manual approach to ROI analysis has high demand for time and expertise, but may provide a more accurate estimate of brain response. In this study, commonly used automated and manual approaches to ROI analysis were directly compared by reanalyzing data from a previously published hypothesis-driven cognitive fMRI study, involving individuals with stroke. The ROI evaluated is the pars opercularis of the inferior frontal gyrus. Significant differences were identified in task-related effect size and percent-activated voxels in this ROI between the automated and manual approaches to ROI analysis. Task interactions, however, were consistent across ROI analysis approaches. These findings support the use of automated approaches to ROI analysis in studies of lesioned brains, provided they employ a task interaction design.

No MeSH data available.


Related in: MedlinePlus

Brain activation associated with right hand action observation in the left hemisphere of a participant with stroke involving the cortex and internal capsule, as evaluated by: (A) a commonly used automated approach to ROI analysis, normalized and overlaid onto the MNI brain image; and (B) a manual approach to ROI analysis, overlaid onto the participant’s non-normalized brain image. ROI masks for left BA 44 are displayed in gray. In this example, the automated ROI in (A) does not contain the intact tissue from left BA 44, whereas the experimenter was able to demarcate the displaced tissue in the manually defined ROI in (B). For display, activation maps are shown at T = 1.67–10 corresponding to p < 0.05 uncorrected.
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Figure 5: Brain activation associated with right hand action observation in the left hemisphere of a participant with stroke involving the cortex and internal capsule, as evaluated by: (A) a commonly used automated approach to ROI analysis, normalized and overlaid onto the MNI brain image; and (B) a manual approach to ROI analysis, overlaid onto the participant’s non-normalized brain image. ROI masks for left BA 44 are displayed in gray. In this example, the automated ROI in (A) does not contain the intact tissue from left BA 44, whereas the experimenter was able to demarcate the displaced tissue in the manually defined ROI in (B). For display, activation maps are shown at T = 1.67–10 corresponding to p < 0.05 uncorrected.

Mentions: These findings demonstrate significant differences between manual and automated approaches to ROI analysis that are consistent in both lesioned and control brains, indicating that the findings cannot be exclusively attributed to error in spatial normalization of the lesioned brains. The automated method used SPM12’s unified segmentation normalization algorithm, which has been optimized for lesioned brains (16). This approach combines bias correction, tissue segmentation, and spatial normalization in an iterative process to better fit an individual’s brain image to the template brain image (18). Here, there is an overall good fit between individual participant’s brain images and the template brain image (as indicated by a visual check of registration between the images). However, normalization error is always greater in lesioned brains due to intensity changes and/or tissue displacement, and this error is especially problematic when the lesion involves the ROI, as is the case for a number of participants with stroke involving left BA 44. In some cases, experimenter bias (i.e., neuroanatomical expertise) may be necessary to localize an ROI after sulcal changes due to brain injury. An example is provided in Figure 5 for a participant with stroke for whom the experimenter was able to manually define left BA 44, whereas the automated map does not contain the intact tissue from this brain region after tissue displacement due to stroke.


Functional MRI Preprocessing in Lesioned Brains: Manual Versus Automated Region of Interest Analysis.

Garrison KA, Rogalsky C, Sheng T, Liu B, Damasio H, Winstein CJ, Aziz-Zadeh LS - Front Neurol (2015)

Brain activation associated with right hand action observation in the left hemisphere of a participant with stroke involving the cortex and internal capsule, as evaluated by: (A) a commonly used automated approach to ROI analysis, normalized and overlaid onto the MNI brain image; and (B) a manual approach to ROI analysis, overlaid onto the participant’s non-normalized brain image. ROI masks for left BA 44 are displayed in gray. In this example, the automated ROI in (A) does not contain the intact tissue from left BA 44, whereas the experimenter was able to demarcate the displaced tissue in the manually defined ROI in (B). For display, activation maps are shown at T = 1.67–10 corresponding to p < 0.05 uncorrected.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4585177&req=5

Figure 5: Brain activation associated with right hand action observation in the left hemisphere of a participant with stroke involving the cortex and internal capsule, as evaluated by: (A) a commonly used automated approach to ROI analysis, normalized and overlaid onto the MNI brain image; and (B) a manual approach to ROI analysis, overlaid onto the participant’s non-normalized brain image. ROI masks for left BA 44 are displayed in gray. In this example, the automated ROI in (A) does not contain the intact tissue from left BA 44, whereas the experimenter was able to demarcate the displaced tissue in the manually defined ROI in (B). For display, activation maps are shown at T = 1.67–10 corresponding to p < 0.05 uncorrected.
Mentions: These findings demonstrate significant differences between manual and automated approaches to ROI analysis that are consistent in both lesioned and control brains, indicating that the findings cannot be exclusively attributed to error in spatial normalization of the lesioned brains. The automated method used SPM12’s unified segmentation normalization algorithm, which has been optimized for lesioned brains (16). This approach combines bias correction, tissue segmentation, and spatial normalization in an iterative process to better fit an individual’s brain image to the template brain image (18). Here, there is an overall good fit between individual participant’s brain images and the template brain image (as indicated by a visual check of registration between the images). However, normalization error is always greater in lesioned brains due to intensity changes and/or tissue displacement, and this error is especially problematic when the lesion involves the ROI, as is the case for a number of participants with stroke involving left BA 44. In some cases, experimenter bias (i.e., neuroanatomical expertise) may be necessary to localize an ROI after sulcal changes due to brain injury. An example is provided in Figure 5 for a participant with stroke for whom the experimenter was able to manually define left BA 44, whereas the automated map does not contain the intact tissue from this brain region after tissue displacement due to stroke.

Bottom Line: Significant differences were identified in task-related effect size and percent-activated voxels in this ROI between the automated and manual approaches to ROI analysis.Task interactions, however, were consistent across ROI analysis approaches.These findings support the use of automated approaches to ROI analysis in studies of lesioned brains, provided they employ a task interaction design.

View Article: PubMed Central - PubMed

Affiliation: Department of Psychiatry, Yale School of Medicine , New Haven, CT , USA ; Division of Biokinesiology and Physical Therapy, University of Southern California , Los Angeles, CA , USA ; Brain and Creativity Institute, University of Southern California , Los Angeles, CA , USA.

ABSTRACT
Functional magnetic resonance imaging (fMRI) has significant potential in the study and treatment of neurological disorders and stroke. Region of interest (ROI) analysis in such studies allows for testing of strong a priori clinical hypotheses with improved statistical power. A commonly used automated approach to ROI analysis is to spatially normalize each participant's structural brain image to a template brain image and define ROIs using an atlas. However, in studies of individuals with structural brain lesions, such as stroke, the gold standard approach may be to manually hand-draw ROIs on each participant's non-normalized structural brain image. Automated approaches to ROI analysis are faster and more standardized, yet are susceptible to preprocessing error (e.g., normalization error) that can be greater in lesioned brains. The manual approach to ROI analysis has high demand for time and expertise, but may provide a more accurate estimate of brain response. In this study, commonly used automated and manual approaches to ROI analysis were directly compared by reanalyzing data from a previously published hypothesis-driven cognitive fMRI study, involving individuals with stroke. The ROI evaluated is the pars opercularis of the inferior frontal gyrus. Significant differences were identified in task-related effect size and percent-activated voxels in this ROI between the automated and manual approaches to ROI analysis. Task interactions, however, were consistent across ROI analysis approaches. These findings support the use of automated approaches to ROI analysis in studies of lesioned brains, provided they employ a task interaction design.

No MeSH data available.


Related in: MedlinePlus