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Mapping causal functional contributions derived from the clinical assessment of brain damage after stroke.

Zavaglia M, Forkert ND, Cheng B, Gerloff C, Thomalla G, Hilgetag CC - Neuroimage Clin (2015)

Bottom Line: The results revealed regional functional contributions to essential behavioral and cognitive functions as reflected in the NIHSS, particularly by subcortical structures.There were also side specific differences of functional contributions between the right and left hemispheric brain regions which may reflect the dominance of the left hemispheric syndrome aphasia in the NIHSS.Comparison of MSA to established lesion inference methods demonstrated the feasibility of the approach for analyzing clinical data and indicated its capability for objectively inferring functional contributions from multiple injured, potentially interacting sites, at the cost of having to predict the outcome of unknown lesion configurations.

View Article: PubMed Central - PubMed

Affiliation: Department of Computational Neuroscience, University Medical Center Eppendorf, Hamburg University, Martinistraße 52, Hamburg 20246, Germany ; School of Engineering and Science, Jacobs University Bremen, Campus Ring 1, Bremen 28759, Germany.

ABSTRACT
Lesion analysis reveals causal contributions of brain regions to mental functions, aiding the understanding of normal brain function as well as rehabilitation of brain-damaged patients. We applied a novel lesion inference technique based on game theory, Multi-perturbation Shapley value Analysis (MSA), to a large clinical lesion dataset. We used MSA to analyze the lesion patterns of 148 acute stroke patients together with their neurological deficits, as assessed by the National Institutes of Health Stroke Scale (NIHSS). The results revealed regional functional contributions to essential behavioral and cognitive functions as reflected in the NIHSS, particularly by subcortical structures. There were also side specific differences of functional contributions between the right and left hemispheric brain regions which may reflect the dominance of the left hemispheric syndrome aphasia in the NIHSS. Comparison of MSA to established lesion inference methods demonstrated the feasibility of the approach for analyzing clinical data and indicated its capability for objectively inferring functional contributions from multiple injured, potentially interacting sites, at the cost of having to predict the outcome of unknown lesion configurations. The analysis of regional functional contributions to neurological symptoms measured by the NIHSS contributes to the interpretation of this widely used standardized stroke scale in clinical practice as well as clinical trials and provides a first approximation of a 'map of stroke'.

No MeSH data available.


Related in: MedlinePlus

Pipeline for registration and quantitative lesion image processing. Left: the brain tissue is automatically segmented in the DWI dataset and used to generate a 3D surface model. A corresponding 3D surface model is also generated based on the atlas brain segmentation, which is then used to calculate the optimal transformation to the DWI dataset using an iterative closest point algorithm (ICP). Right: the resulting transformation is employed to align the structural regions defined in the atlas with the patient-specific DWI dataset. After semi-automatic segmentation of the lesion in the DWI dataset, the transformed structural brain regions can be used to calculate the individual lesion overlap values. The lesion overlap visualization also depicts the eight bilateral VOIs used in the present study.
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f0005: Pipeline for registration and quantitative lesion image processing. Left: the brain tissue is automatically segmented in the DWI dataset and used to generate a 3D surface model. A corresponding 3D surface model is also generated based on the atlas brain segmentation, which is then used to calculate the optimal transformation to the DWI dataset using an iterative closest point algorithm (ICP). Right: the resulting transformation is employed to align the structural regions defined in the atlas with the patient-specific DWI dataset. After semi-automatic segmentation of the lesion in the DWI dataset, the transformed structural brain regions can be used to calculate the individual lesion overlap values. The lesion overlap visualization also depicts the eight bilateral VOIs used in the present study.

Mentions: Due to different positions of the acute stroke patients within the MR scanner, different inter-subject head anatomies and variations regarding the spatial resolution of the DWI image sequences, a registration of the datasets into a reference space was necessary to quantify the number of lesioned voxels in different brain regions of interest that are defined in the reference space. Therefore, the 1 mm3 MNI ICBM152 brain atlas, which has been designated as the standard template by the International Consortium for Brain Mapping, was used for definition of the reference space (Collins et al., 1995). To overcome the problem of differences regarding the signal intensities and visible tissues in the MNI brain atlas, which was constructed based on T1-weighted image sequences from 148 healthy subjects, and in the T2-weighted DWI image sequences, an iterative closest point (ICP) registration approach (Besl and McKay, 1992) was used in this work, which is illustrated in Fig. 1. Particularly, an adapted version of the brain segmentation method described in Forkert et al. (2009) was used to extract the brain tissue from each DWI dataset with strong diffusion weighting of the acute stroke patients. The resulting brain segmentations were employed for generation of the corresponding 3D surface models using the Marching Cubes algorithm (Lorensen and Cline, 1987). The Marching Cubes algorithm was also used for generation of a surface model from the brain segmentation of the MNI brain atlas. After this step, the brain surface model of the MNI brain atlas was registered to each patient brain surface model employing the ICP algorithm using an affine transformation. After surface-based ICP registration, the resulting affine transformation was used to adapt the brain regions defined in the MNI brain atlas to each patient.


Mapping causal functional contributions derived from the clinical assessment of brain damage after stroke.

Zavaglia M, Forkert ND, Cheng B, Gerloff C, Thomalla G, Hilgetag CC - Neuroimage Clin (2015)

Pipeline for registration and quantitative lesion image processing. Left: the brain tissue is automatically segmented in the DWI dataset and used to generate a 3D surface model. A corresponding 3D surface model is also generated based on the atlas brain segmentation, which is then used to calculate the optimal transformation to the DWI dataset using an iterative closest point algorithm (ICP). Right: the resulting transformation is employed to align the structural regions defined in the atlas with the patient-specific DWI dataset. After semi-automatic segmentation of the lesion in the DWI dataset, the transformed structural brain regions can be used to calculate the individual lesion overlap values. The lesion overlap visualization also depicts the eight bilateral VOIs used in the present study.
© Copyright Policy - CC BY-NC-ND
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4544394&req=5

f0005: Pipeline for registration and quantitative lesion image processing. Left: the brain tissue is automatically segmented in the DWI dataset and used to generate a 3D surface model. A corresponding 3D surface model is also generated based on the atlas brain segmentation, which is then used to calculate the optimal transformation to the DWI dataset using an iterative closest point algorithm (ICP). Right: the resulting transformation is employed to align the structural regions defined in the atlas with the patient-specific DWI dataset. After semi-automatic segmentation of the lesion in the DWI dataset, the transformed structural brain regions can be used to calculate the individual lesion overlap values. The lesion overlap visualization also depicts the eight bilateral VOIs used in the present study.
Mentions: Due to different positions of the acute stroke patients within the MR scanner, different inter-subject head anatomies and variations regarding the spatial resolution of the DWI image sequences, a registration of the datasets into a reference space was necessary to quantify the number of lesioned voxels in different brain regions of interest that are defined in the reference space. Therefore, the 1 mm3 MNI ICBM152 brain atlas, which has been designated as the standard template by the International Consortium for Brain Mapping, was used for definition of the reference space (Collins et al., 1995). To overcome the problem of differences regarding the signal intensities and visible tissues in the MNI brain atlas, which was constructed based on T1-weighted image sequences from 148 healthy subjects, and in the T2-weighted DWI image sequences, an iterative closest point (ICP) registration approach (Besl and McKay, 1992) was used in this work, which is illustrated in Fig. 1. Particularly, an adapted version of the brain segmentation method described in Forkert et al. (2009) was used to extract the brain tissue from each DWI dataset with strong diffusion weighting of the acute stroke patients. The resulting brain segmentations were employed for generation of the corresponding 3D surface models using the Marching Cubes algorithm (Lorensen and Cline, 1987). The Marching Cubes algorithm was also used for generation of a surface model from the brain segmentation of the MNI brain atlas. After this step, the brain surface model of the MNI brain atlas was registered to each patient brain surface model employing the ICP algorithm using an affine transformation. After surface-based ICP registration, the resulting affine transformation was used to adapt the brain regions defined in the MNI brain atlas to each patient.

Bottom Line: The results revealed regional functional contributions to essential behavioral and cognitive functions as reflected in the NIHSS, particularly by subcortical structures.There were also side specific differences of functional contributions between the right and left hemispheric brain regions which may reflect the dominance of the left hemispheric syndrome aphasia in the NIHSS.Comparison of MSA to established lesion inference methods demonstrated the feasibility of the approach for analyzing clinical data and indicated its capability for objectively inferring functional contributions from multiple injured, potentially interacting sites, at the cost of having to predict the outcome of unknown lesion configurations.

View Article: PubMed Central - PubMed

Affiliation: Department of Computational Neuroscience, University Medical Center Eppendorf, Hamburg University, Martinistraße 52, Hamburg 20246, Germany ; School of Engineering and Science, Jacobs University Bremen, Campus Ring 1, Bremen 28759, Germany.

ABSTRACT
Lesion analysis reveals causal contributions of brain regions to mental functions, aiding the understanding of normal brain function as well as rehabilitation of brain-damaged patients. We applied a novel lesion inference technique based on game theory, Multi-perturbation Shapley value Analysis (MSA), to a large clinical lesion dataset. We used MSA to analyze the lesion patterns of 148 acute stroke patients together with their neurological deficits, as assessed by the National Institutes of Health Stroke Scale (NIHSS). The results revealed regional functional contributions to essential behavioral and cognitive functions as reflected in the NIHSS, particularly by subcortical structures. There were also side specific differences of functional contributions between the right and left hemispheric brain regions which may reflect the dominance of the left hemispheric syndrome aphasia in the NIHSS. Comparison of MSA to established lesion inference methods demonstrated the feasibility of the approach for analyzing clinical data and indicated its capability for objectively inferring functional contributions from multiple injured, potentially interacting sites, at the cost of having to predict the outcome of unknown lesion configurations. The analysis of regional functional contributions to neurological symptoms measured by the NIHSS contributes to the interpretation of this widely used standardized stroke scale in clinical practice as well as clinical trials and provides a first approximation of a 'map of stroke'.

No MeSH data available.


Related in: MedlinePlus