- Email: [email protected]
Development of a cerebral amyloid angiopathy–specific amyloid imaging tracer
Alzheimer’s Imaging Consortium Posters: IC-P
IC-P-101
WHITE MATTER CORRELATES OF A NEW SCORING METRIC FOR TRAIL-MAKING TEST B
Stephen Correia1, Amanda Rabinowitz1, David Ahern1, Christina D’Angelo2, Stephen Salloway1, Paul Malloy1, Sean Deoni1, 1 Brown University, Providence, Rhode Island, United States; 2Providence College, Providence, Rhode Island, United States. Background: The Trail Making Test part B (TMT-B) is a neuropsychological test of executive function commonly used in research and clinical practice. However, the traditional TMT-B scoring metric has limited research utility among individuals with dementia who score at the floor (i.e., 300 seconds) because this score masks performance variability that could be used in statistical analyses. For example, a person who completes 75% of the items in 300s presumably has somewhat better executive function than one who completes only 15%. We developed a new TMT-B efficiency score (TMTBe) to capture this variability. TMT-Be takes into account move-efficiency [ratio of correct moves (Mc) to commission errors (Ec)], time efficiency [time (T) per correct move], and omission errors (Eo). The formula is as follows: TMT-Be ¼ [(Mc/(24-Ec))*(T/Mc)]+Eo (derivation) TMT-Be ¼ [T/ (24-Ec)]+Eo (computation)where: 24Mc>0, 24T300s, 0Eo 23 We conducted an initial test of convergent validity of our new TMT-Be score against the standard TMT-B raw score (TMT-Bs) by correlating them with MRI indices of anterior and posterior white matter integrity in a group of elderly participants with and without cognitive impairment. Methods: Ten participants (mean age ¼ 79.9, mean education ¼ 13.3 years, 70% female, CDR ¼ 0.0-0.5) completed TMT-B administered according to standard procedures, and underwent MRI with diffusion-tensor imaging (DTI). We computed quantitative DTI tractography metrics in the genu and splenium using Analyze 10.0 and then correlated TMT-Bs and TMT-Be scores with mean fractional anisotropy (FA) for both fiber bundles. Results: The correlation between TMT-Be and mean FA was significant in genu (r ¼ .66, P <.05) but only a trend in the splenium (r ¼ .54, P ¼ .11). In contrast, the correlation between TMT-Bs and mean FA was significant in splenium (r ¼ .82, P <.005) but only a trend in genu (r ¼ .52, P ¼ .12). Conclusions: Both TMT-B scores correlated with white matter integrity in the corpus callosum with TMT-Be showing a significant association with frontal (genu) fibers. These results support the validity of TMT-Be as a measure of executive functioning in the context of age-related brain changes. Analyses of TMT-Be in individuals who fail to complete TMT-B are underway. IC-P-102
THE EFFECTS OF SEEDING RESOLUTION ON DIFFUSION TENSOR IMAGING STREAMTUBE VISUALIZATION COMPREHENSION
Jianmin Chen1, Haipeng Cai1, David Laidlaw2, Alexander Auchus3, University of Southern Mississippi, Hattiesburg, Mississippi, United States; 2Brown University, Providence, Rhode Island, United States; 3 University of Mississippi Medical Center, Jackson, Mississippi, United States. 1
Background: The optimal seeding resolution to depict legible streamtube visualizations of human brain diffusion tensor magnetic resonance imaging (DTI) data has not been determined. This has impeded the development of streamtube technologies for studying human brain aging, Alzheimer’s disease, and related neurodegenerative diseases. Methods: We examined data legibility in the context of five display resolutions in a user study with 10 participants to compare the impact of density on task performance. The tractography data were computed from source MRI images captured from a normal human brain at the resolution of 0.9375mm x 0.9375mm x 4.52mm. A “step wedge” method was used employing a step size of one pixel sampling along each axis to produce five seeding schemas of progressive densities (1x1x1 to 5x5x5). The hypotheses were that: (1) reducing data density would improve task accuracy and completion time, and (2) participants would prefer higher-density displays despite the accompanying reduction in diagnosis accuracy. Participants conducted six representative tasks: (1) determine regions of higher average fractional anisotropy, (2) find the endpoints of selected fiber tracts, (3) identify anatomical fiber bundles,
P59
(4) detect a brain lesion, (5) judge if tracts belong to the same bundle, and (6) determine if two datasets are the same. Results: We found that optimal seeding resolution ranged from 2x2x2 to 4x4x4 for the tractography used in this study. The 1x1x1 (highest density) resolution was most preferred by users, yet led to the highest error rates. Lower resolutions were associated with higher task accuracy over all tasks studied. Conclusions: Our work contributes to the growing literature on evaluation and design of visualization for DTI datasets. It describes new results in which experimental evidence on legible displays were systematically collected. This knowledge should assist the advancement of streamtube technologies toward applications in human brain science.
IC-P-103
INTERACTIVE VISUAL ANALYSIS OF DIFFUSIONTENSOR MRI DATA USING THE EXPECTATION MAXIMIZATION ALGORITHM
Jianmin Chen1, Andrew Maxwell1, Haipeng Cai1, Alexander Auchus2, University of Southern Mississippi, Hattiesburg, Mississippi, United States; 2University of Mississippi Medical Center, Jackson, Mississippi, United States. 1
Background: Three-dimensional visualization of dense streamtube from DT-MRI tractography often produces visual clutter and poor legibility. This limits the ability to reveal valuable clinical information effectively and efficiently to the end-user. Methods: We designed an analysis toolkit which makes use of tube clustering to facilitate visual data mining from human brain DT-MRI images. An expectation maximization (EM) algorithm provides the clustering information to represent the likelihood of a line belonging to an anatomical fiber bundle. We tested the algorithm with several artificial line sets. Results: We found that the EM algorithms could provide a principal way to cluster lines with different lengths and orientations. The algorithm is also computationally efficient. We are currently comparing usage of the algorithm on normal and pathological cases for automatic detection of pathology. This also can be applied to Alzheimer’s disease and related neurodegenerative disorders. Conclusions: Our work contributes to the design of an automatic toolkit for facilitating clinical diagnosis of brain disorders.
IC-P-105
DEVELOPMENT OF A CEREBRAL AMYLOID ANGIOPATHY–SPECIFIC AMYLOID IMAGING TRACER
Byung-Hee Han, Wenhua Chu, Jinbin Xu, Robert Mach, Gregory Zipfel, Washington University School of Medicine, St. Louis, Missouri, United States. Background: Cerebral amyloid angiopathy (CAA) is a well recognized cause of lobar cerebral hemorrhage, ischemic stroke, and cognitive dysfunction both in AD and non-AD patient populations. Yet to date only “possible” or “probable” diagnosis of CAA has been achievable without obtaining pathological tissue via brain biopsy or at autopsy. Though amyloid tracers labeled with positron-emitting radioligands have shown promise for non-invasive amyloid imaging in AD patients, they have been unable to clarify whether the observed amyloid load represents neuritic plaques vs. CAA due to the low resolution of PET imaging and the almost equal affinity of these tracers for both vascular and parenchymal amyloid. Recently, we demonstrated that phenoxazine analogs preferentially bind CAA over neuritic plaques in postmortem AD brain tissues as well as in aged Tg2576 mice (Han et al, Molecular Neurodegeneration 6:86, 2011). Though the molecular basis underlying their selective binding to CAA remains elusive, this unique binding specificity suggests that this type of compound has great potential as a CAA-specific amyloid tracer that will permit non-invasive diagnosis of CAA, quantitation of CAA severity, and monitoring of CAA progression over time. Methods: To further explore this possibility, we synthesized a series of new phenoxazine analogs based on our preliminary structure-activity relationship data. We determined the binding affinity (Ki values) of these novel compounds on CAA isolated from Tg2576 mice utilizing a competitive binding assay system with a [3H]phenoxazine
P60
Alzheimer’s Imaging Consortium Posters: IC-P
ligand. We next performed an in situ competitive binding assay to determine their selective binding affinity for CAA over neuritic plaques in brain tissues of aged Tg2576 mice having both CAA deposits and neuritic plaques. The lipophilicity of phenoxazine derivatives was determined by octanol-water coefficient to predict their brain accessibility. Results: We found that phenoxazine derivatives, in particular 5-5, revealed enhanced binding affinity for CAA as compared with the parental compound. More importantly, synthesized phenoxazine analogs preserved the preferential binding affinity for CAA over neuritic plaques. Conclusions: These results strongly suggest that phenoxazine analogs provide great potential for development of a CAA-specific amyloid tracer that would be a major diagnostic step forward for this frequent (but often under-diagnosed) condition.
alized into separate right and left modules that contain additional ipsilateral temporal lobe and insular structures (Figure 1). Conclusions: This novel voxel-wise approach for constructing disease-specific brain networks using an anatomical T1-weighted sequence yielded important information about AD, identifying network connectivity-based abnormalities in areas known to be affected by AD, such as the hippocampus and default mode network, but also disease-specific anatomical lateralization and loss of structural complexity. The application of graph theoretical analysis methods to voxel-wise structural MRI data may lead to the development of new disease-specific imaging biomarkers that can be incorporated in future studies of AD.
IC-P-107 IC-P-106
ALZHEIMER’S DISEASE–SPECIFIC CHANGES IN CEREBRAL GRAY MATTER REVEALED USING A NOVEL VOXEL-WISE APPROACH FOR CONSTRUCTING STRUCTURAL BRAIN NETWORKS
Christopher Whitlow, Joseph Maldjian, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States. Background: Graph theoretical analysis applied to MRI has begun to shed light upon neurodegenerative diseases at the level of whole-brain distributed network connectivity. In the present study, we applied graph theory methods to voxel-wise data from a T1-weighted MRI sequence, hypothesizing that the construction of group-specific structural brain networks from conventional anatomical imaging would yield important disease-associated patterns of change in cerebral gray matter. Methods: T1-weighted MRI data from 102 cognitively normal (CN) and 92 AD patients were obtained from the Alzheimer’s Disease Neuroimaging Initiative database. All data were motion-corrected, segmented and normalized to a standard template using VBM8 toolbox within SPM8. Modulated grey matter maps from all subjects in each group were concatenated into a 4D image data file to construct group-specific voxel-based 159,844 x 159,844 correlation matrices. Binarized adjacency matrices were then generated at a cost of 0.1, from which AD- and CN-specific clustering coefficient and modularity maps were constructed. Results: Serial t-tests corrected for multiple comparisons demonstrated between-group AD-associated changes in clustering coefficient for hippocampus and default mode structures (P <.0008). Figure 1 presents modularity maps that demonstrate the distribution of individual voxels into a mosaic of regional clusters that reflect the known distributed anatomical organization of the brain. AD modularity maps demonstrate a pattern of lateralization and loss of anatomical complexity. This is characterized by the collapse of two intra-hippocampal and -frontal lobe modules in CN into single respective modules for AD, as well as hippocampal later-
WHITHER THE HIPPOCAMPUS?: FDG-PET HIPPOCAMPAL HYPOMETABOLISM IN ALZHEIMER’S DISEASE REVISITED
Christopher Whitlow, Joseph Maldjian, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States. Background: The hippocampus is a widely recognized area of early change in Alzheimer’s disease (AD), yet voxel-wise analyses of FDG PET activity differences between AD and cognitively normal (CN) controls have consistently failed to identify hippocampal hypometabolism. In this paper we propose a high dimensional PET-specific analysis framework to determine if important hippocampal metabolic FDG PET activity differences between AD and CN subjects are embedded in the Jacobian information generated during spatial normalization. Methods: Resting FDG PET data was obtained from 102 CN and 92 AD participants from the Alzheimer’s disease Neuroimaging Initiative database. A PET study-specific template was constructed using symmetric diffeomorphic registration. Spatially normalized
IC-P-101
WHITE MATTER CORRELATES OF A NEW SCORING METRIC FOR TRAIL-MAKING TEST B
Stephen Correia1, Amanda Rabinowitz1, David Ahern1, Christina D’Angelo2, Stephen Salloway1, Paul Malloy1, Sean Deoni1, 1 Brown University, Providence, Rhode Island, United States; 2Providence College, Providence, Rhode Island, United States. Background: The Trail Making Test part B (TMT-B) is a neuropsychological test of executive function commonly used in research and clinical practice. However, the traditional TMT-B scoring metric has limited research utility among individuals with dementia who score at the floor (i.e., 300 seconds) because this score masks performance variability that could be used in statistical analyses. For example, a person who completes 75% of the items in 300s presumably has somewhat better executive function than one who completes only 15%. We developed a new TMT-B efficiency score (TMTBe) to capture this variability. TMT-Be takes into account move-efficiency [ratio of correct moves (Mc) to commission errors (Ec)], time efficiency [time (T) per correct move], and omission errors (Eo). The formula is as follows: TMT-Be ¼ [(Mc/(24-Ec))*(T/Mc)]+Eo (derivation) TMT-Be ¼ [T/ (24-Ec)]+Eo (computation)where: 24Mc>0, 24T300s, 0Eo 23 We conducted an initial test of convergent validity of our new TMT-Be score against the standard TMT-B raw score (TMT-Bs) by correlating them with MRI indices of anterior and posterior white matter integrity in a group of elderly participants with and without cognitive impairment. Methods: Ten participants (mean age ¼ 79.9, mean education ¼ 13.3 years, 70% female, CDR ¼ 0.0-0.5) completed TMT-B administered according to standard procedures, and underwent MRI with diffusion-tensor imaging (DTI). We computed quantitative DTI tractography metrics in the genu and splenium using Analyze 10.0 and then correlated TMT-Bs and TMT-Be scores with mean fractional anisotropy (FA) for both fiber bundles. Results: The correlation between TMT-Be and mean FA was significant in genu (r ¼ .66, P <.05) but only a trend in the splenium (r ¼ .54, P ¼ .11). In contrast, the correlation between TMT-Bs and mean FA was significant in splenium (r ¼ .82, P <.005) but only a trend in genu (r ¼ .52, P ¼ .12). Conclusions: Both TMT-B scores correlated with white matter integrity in the corpus callosum with TMT-Be showing a significant association with frontal (genu) fibers. These results support the validity of TMT-Be as a measure of executive functioning in the context of age-related brain changes. Analyses of TMT-Be in individuals who fail to complete TMT-B are underway. IC-P-102
THE EFFECTS OF SEEDING RESOLUTION ON DIFFUSION TENSOR IMAGING STREAMTUBE VISUALIZATION COMPREHENSION
Jianmin Chen1, Haipeng Cai1, David Laidlaw2, Alexander Auchus3, University of Southern Mississippi, Hattiesburg, Mississippi, United States; 2Brown University, Providence, Rhode Island, United States; 3 University of Mississippi Medical Center, Jackson, Mississippi, United States. 1
Background: The optimal seeding resolution to depict legible streamtube visualizations of human brain diffusion tensor magnetic resonance imaging (DTI) data has not been determined. This has impeded the development of streamtube technologies for studying human brain aging, Alzheimer’s disease, and related neurodegenerative diseases. Methods: We examined data legibility in the context of five display resolutions in a user study with 10 participants to compare the impact of density on task performance. The tractography data were computed from source MRI images captured from a normal human brain at the resolution of 0.9375mm x 0.9375mm x 4.52mm. A “step wedge” method was used employing a step size of one pixel sampling along each axis to produce five seeding schemas of progressive densities (1x1x1 to 5x5x5). The hypotheses were that: (1) reducing data density would improve task accuracy and completion time, and (2) participants would prefer higher-density displays despite the accompanying reduction in diagnosis accuracy. Participants conducted six representative tasks: (1) determine regions of higher average fractional anisotropy, (2) find the endpoints of selected fiber tracts, (3) identify anatomical fiber bundles,
P59
(4) detect a brain lesion, (5) judge if tracts belong to the same bundle, and (6) determine if two datasets are the same. Results: We found that optimal seeding resolution ranged from 2x2x2 to 4x4x4 for the tractography used in this study. The 1x1x1 (highest density) resolution was most preferred by users, yet led to the highest error rates. Lower resolutions were associated with higher task accuracy over all tasks studied. Conclusions: Our work contributes to the growing literature on evaluation and design of visualization for DTI datasets. It describes new results in which experimental evidence on legible displays were systematically collected. This knowledge should assist the advancement of streamtube technologies toward applications in human brain science.
IC-P-103
INTERACTIVE VISUAL ANALYSIS OF DIFFUSIONTENSOR MRI DATA USING THE EXPECTATION MAXIMIZATION ALGORITHM
Jianmin Chen1, Andrew Maxwell1, Haipeng Cai1, Alexander Auchus2, University of Southern Mississippi, Hattiesburg, Mississippi, United States; 2University of Mississippi Medical Center, Jackson, Mississippi, United States. 1
Background: Three-dimensional visualization of dense streamtube from DT-MRI tractography often produces visual clutter and poor legibility. This limits the ability to reveal valuable clinical information effectively and efficiently to the end-user. Methods: We designed an analysis toolkit which makes use of tube clustering to facilitate visual data mining from human brain DT-MRI images. An expectation maximization (EM) algorithm provides the clustering information to represent the likelihood of a line belonging to an anatomical fiber bundle. We tested the algorithm with several artificial line sets. Results: We found that the EM algorithms could provide a principal way to cluster lines with different lengths and orientations. The algorithm is also computationally efficient. We are currently comparing usage of the algorithm on normal and pathological cases for automatic detection of pathology. This also can be applied to Alzheimer’s disease and related neurodegenerative disorders. Conclusions: Our work contributes to the design of an automatic toolkit for facilitating clinical diagnosis of brain disorders.
IC-P-105
DEVELOPMENT OF A CEREBRAL AMYLOID ANGIOPATHY–SPECIFIC AMYLOID IMAGING TRACER
Byung-Hee Han, Wenhua Chu, Jinbin Xu, Robert Mach, Gregory Zipfel, Washington University School of Medicine, St. Louis, Missouri, United States. Background: Cerebral amyloid angiopathy (CAA) is a well recognized cause of lobar cerebral hemorrhage, ischemic stroke, and cognitive dysfunction both in AD and non-AD patient populations. Yet to date only “possible” or “probable” diagnosis of CAA has been achievable without obtaining pathological tissue via brain biopsy or at autopsy. Though amyloid tracers labeled with positron-emitting radioligands have shown promise for non-invasive amyloid imaging in AD patients, they have been unable to clarify whether the observed amyloid load represents neuritic plaques vs. CAA due to the low resolution of PET imaging and the almost equal affinity of these tracers for both vascular and parenchymal amyloid. Recently, we demonstrated that phenoxazine analogs preferentially bind CAA over neuritic plaques in postmortem AD brain tissues as well as in aged Tg2576 mice (Han et al, Molecular Neurodegeneration 6:86, 2011). Though the molecular basis underlying their selective binding to CAA remains elusive, this unique binding specificity suggests that this type of compound has great potential as a CAA-specific amyloid tracer that will permit non-invasive diagnosis of CAA, quantitation of CAA severity, and monitoring of CAA progression over time. Methods: To further explore this possibility, we synthesized a series of new phenoxazine analogs based on our preliminary structure-activity relationship data. We determined the binding affinity (Ki values) of these novel compounds on CAA isolated from Tg2576 mice utilizing a competitive binding assay system with a [3H]phenoxazine
P60
Alzheimer’s Imaging Consortium Posters: IC-P
ligand. We next performed an in situ competitive binding assay to determine their selective binding affinity for CAA over neuritic plaques in brain tissues of aged Tg2576 mice having both CAA deposits and neuritic plaques. The lipophilicity of phenoxazine derivatives was determined by octanol-water coefficient to predict their brain accessibility. Results: We found that phenoxazine derivatives, in particular 5-5, revealed enhanced binding affinity for CAA as compared with the parental compound. More importantly, synthesized phenoxazine analogs preserved the preferential binding affinity for CAA over neuritic plaques. Conclusions: These results strongly suggest that phenoxazine analogs provide great potential for development of a CAA-specific amyloid tracer that would be a major diagnostic step forward for this frequent (but often under-diagnosed) condition.
alized into separate right and left modules that contain additional ipsilateral temporal lobe and insular structures (Figure 1). Conclusions: This novel voxel-wise approach for constructing disease-specific brain networks using an anatomical T1-weighted sequence yielded important information about AD, identifying network connectivity-based abnormalities in areas known to be affected by AD, such as the hippocampus and default mode network, but also disease-specific anatomical lateralization and loss of structural complexity. The application of graph theoretical analysis methods to voxel-wise structural MRI data may lead to the development of new disease-specific imaging biomarkers that can be incorporated in future studies of AD.
IC-P-107 IC-P-106
ALZHEIMER’S DISEASE–SPECIFIC CHANGES IN CEREBRAL GRAY MATTER REVEALED USING A NOVEL VOXEL-WISE APPROACH FOR CONSTRUCTING STRUCTURAL BRAIN NETWORKS
Christopher Whitlow, Joseph Maldjian, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States. Background: Graph theoretical analysis applied to MRI has begun to shed light upon neurodegenerative diseases at the level of whole-brain distributed network connectivity. In the present study, we applied graph theory methods to voxel-wise data from a T1-weighted MRI sequence, hypothesizing that the construction of group-specific structural brain networks from conventional anatomical imaging would yield important disease-associated patterns of change in cerebral gray matter. Methods: T1-weighted MRI data from 102 cognitively normal (CN) and 92 AD patients were obtained from the Alzheimer’s Disease Neuroimaging Initiative database. All data were motion-corrected, segmented and normalized to a standard template using VBM8 toolbox within SPM8. Modulated grey matter maps from all subjects in each group were concatenated into a 4D image data file to construct group-specific voxel-based 159,844 x 159,844 correlation matrices. Binarized adjacency matrices were then generated at a cost of 0.1, from which AD- and CN-specific clustering coefficient and modularity maps were constructed. Results: Serial t-tests corrected for multiple comparisons demonstrated between-group AD-associated changes in clustering coefficient for hippocampus and default mode structures (P <.0008). Figure 1 presents modularity maps that demonstrate the distribution of individual voxels into a mosaic of regional clusters that reflect the known distributed anatomical organization of the brain. AD modularity maps demonstrate a pattern of lateralization and loss of anatomical complexity. This is characterized by the collapse of two intra-hippocampal and -frontal lobe modules in CN into single respective modules for AD, as well as hippocampal later-
WHITHER THE HIPPOCAMPUS?: FDG-PET HIPPOCAMPAL HYPOMETABOLISM IN ALZHEIMER’S DISEASE REVISITED
Christopher Whitlow, Joseph Maldjian, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States. Background: The hippocampus is a widely recognized area of early change in Alzheimer’s disease (AD), yet voxel-wise analyses of FDG PET activity differences between AD and cognitively normal (CN) controls have consistently failed to identify hippocampal hypometabolism. In this paper we propose a high dimensional PET-specific analysis framework to determine if important hippocampal metabolic FDG PET activity differences between AD and CN subjects are embedded in the Jacobian information generated during spatial normalization. Methods: Resting FDG PET data was obtained from 102 CN and 92 AD participants from the Alzheimer’s disease Neuroimaging Initiative database. A PET study-specific template was constructed using symmetric diffeomorphic registration. Spatially normalized