Neurobiology of Aging 36 (2015) S1eS2
Contents lists available at ScienceDirect
Neurobiology of Aging journal homepage: www.elsevier.com/locate/neuaging
Guest editorial Recent years have seen an explosion of research on imaging biomarkers for Alzheimer’s disease (AD) and related neurodegenerative disorders, fueled by the realization that the enormous cost of conducting clinical trials for disease-modifying treatments stands as a major obstacle on the path to finding cures. Imaging biomarkers promise to reduce this cost by allowing more accurate detection of prodromal disease and more accurate monitoring of the effects of disease and treatment on the brain. Much of the progress in improving imaging biomarkers for neurodegenerative disease has been driven by the development of novel computational imaging analysis methodology, particularly in the areas of segmentation, registration, classification, and highdimensional statistics. Progress has also been aided by the availability of large imaging data sets like the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and its European and Asian counterparts. This special issue on Novel Imaging Biomarkers for Alzheimer’s Disease and Related Disorders features 22 research articles that specifically focus on the computational methodology aspects of imaging biomarker development in AD. Of the 22 articles, 9 were focused on morphometry, 6 on network and connectivity, 4 on imaging-functioning relationships, and 14 used the ADNI data for their analyses. Many of the articles in the special issue introduce new techniques for large-scale imaging data analysis, evaluate recently developed techniques in the context of AD biomarker development or adapt techniques from other fields to AD neuroimaging analysis. Innovations are in the areas of machine learning (e.g., Eskildsen et al., Raamana et al.), nonlinear regression (e.g., Hibar et al., Jahanshad et al., Shen et al.), unbiased longitudinal analysis (e.g., Prados et al., Gutman et al.), diffusion MRI connectomics (Nir et al., Prasad et al.), and fusion of diverse biomarkers into more predictive compound biomarkers (e.g., Ming et al., Khan et al., Duchesne et al., Jedynak et al.). Reflective of the field at large, the special issue is predominantly morphometric (9 articles): Two articles describe the development of MTL markers for AD (Miller et al. presents a template-based highdimensional mapping of the amygdala and statistical shape measures; Duchesne et al. use tissue and deformation maps within an MTL volume of interest to define and track disease evaluation factor). Eskildsen et al. use hippocampal grading score and cortical thickness to predict clinical progression. Madsen et al. present a longitudinal ventricular marker that can be related to atrophy of cortical regions. This is followed by Lorenzi et al. that describes a registration method that can separately model the aging and AD disease process. Several articles present methods for integrating multiple types of information. Two articles integrate different types of morphometric measures to improve biomarkers (Jing e al. describe an integrated 0197-4580/$ e see front matter Ó 2015 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.neurobiolaging.2014.09.021
measure of cortical thickness and cortical geometry, and Khan et al. present a unified approach for voxel- and tensor-based morphometry using registration confidence). Gutman et al. combine tensorbased morphometry and ventricular surface measures, and Prados present a generalized boundary shift interval method, both to improve clinical trial power. Network and connectivity is represented by 6 articles. Raamana et al. present a novel anatomic network constructed from measures of thickness similarity and show that thus derivednetwork features can achieve improved classification. This is followed by 4 diffusion tensor imaging (DTI)-based connectivity articles from the Thompson group (Jahanshad et al., Nir et al., Nir et al., Prasad et al., all). Jahanshad et al. use a regression technique adapted from econometrics to identify brain connectivities that are related to clinical decline. Nir et al. use DTI to construct and identify network properties that predict longitudinal volumetric reduction. Nir et al. measure white-matter properties derived from maximum density paths to improve classification performance. Prasad et al. define a novel, flow-based network measure from DTI for classification. Last, Das et al. use functional connectivity to demonstrate that anterior and poster MTL networks are both vulnerable to AD pathology. The next 3 articles describe methods for studying the effects of SNP-SNP interactions on brain regional volumes (Hibar et al.), PiB scan registration without structural MRI (Bourgeat et al.), and evaluation of accelerated T1-weighted imaging (Ching et al.). The special issue is rounded out with a series of 4 articles that use neuroimaging markers to relate to the life style and cognitive functioning of an individual. Jedynak et al. present a computational method that relates combinations of cognitive measures and structural volumes to clinical progression. Yan et al. uses a refined regression method to relate structural neuroimaging measures to cognitive outcomes. Boyle et al. establish an association between higher body mass index and lower brain volumes, and physical activity can help preserve brain volumes. Madsen et al. find that higher homocysteine levels are associated with cortical thinning. This special issue clearly demonstrates the profound impact that open studies such as ADNI and OASIS have had on the field. More than half of the articles in the special issue use ADNI, OASIS, or both for analysis and evaluation. Notably, several studies use multimodality MRI data from ADNI2 (e.g., Nir et al., Jahanshad et al., Prasad et al.). Overall, the work included in the current issue provides an excellent snapshot of the state of the art in the use of computational analysis techniques to derive neuroimaging AD biomarkers. Most of these methodological advances are not limited to the study of AD and dementia and have good potential to impact other
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Guest Editorial / Neurobiology of Aging 36 (2015) S1eS2
neuroimaging applications. The next frontier may very well lie in finding ways to compare, in a comprehensive and unbiased way, the power and reliability of the many proposed biomarkers. With large clinical trials of disease-modifying agents underway, it may be necessary for the field to come up with a set of “standard” biomarkers to use for disease progression and cohort stratification purposes, and doing so would require further comparative analysis of the techniques presented in this special issue and elsewhere. Lei Wang* Departments of Psychiatry and Behavioral Sciences and Radiology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA Paul Yushkevich Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA Sebastien Ourselin Centre for Medical Image Computing, University College London, London, UK * Corresponding
author at: Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, 710 N. Lake Shore Drive, Abbott Hall 1322, Chicago, IL 60611, USA. E-mail address: [email protected] (L. Wang)
Table of content: I. High-dimensional morphometry: 1. Miller, MI et al., Amygdalar Atrophy in Symptomatic AD Based on Diffeomorphometry: The BIOCARD Cohort. 2. Duchesne, S et al., Single Timepoint High-Dimensional Morphometry In Alzheimer’s Disease: Group Statistics on Longitudinally Acquired Data. 3. Eskildsen, SF et al., Structural imaging biomarkers of Alzheimer’s disease: predicting disease progression 4. Madsen, SK et al., Mapping ventricular expansion onto cortical gray matter in older adults 5. Lorenzi, M et al., Disentangling the Normal Aging from the Pathological Alzheimer’s Disease Progression on Crosssectional Structural MR Images
6. Ming, J et al., Integrated Cortical Structural Marker for Alzheimer’s Disease 7. Khan, AR et al., Unified Voxel and Tensor-based Morphometry (UVTBM) using Registration Confidence 8. Gutman, BA et al., Empowering Imaging Biomarkers of Alzheimer’s Disease 9. Prados, F et al., Measuring brain atrophy with a generalised formulation of the boundary shift integral II. Network and connectivity: 10. Raamana, PR et al., Thickness Network (ThickNet) Features for Prognostic Applications in Dementia 11. Jahanshad, N et al., Seemingly Unrelated Regression empowers detection of network failure in dementia 12. Nir, TM et al., Connectivity network measures predict volumetric atrophy in mild cognitive impairment 13. Prasad, G et al., Brain connectivity and novel network measures for Alzheimer’s disease classification 14. Nir, TM et al., DTI-based maximum density path analysis and classification of Alzheimer’s disease. 15. Das, S et al., Anterior and Posterior MTL Networks in Aging and MCI III. Imaging genetics: 16. Hibar, DP et al., Genome-wide interaction analysis reveals replicated epistatic effects on brain structure. IV. Functional/Multimodal imaging: 17. Bourgeat, P et al., Comparison of MR-less PiB SUVR quantification methods V. Imaging technology: 18. Ching, et al., Does MRI scan acceleration affect power to track brain change? VI. Functioning: 19. Jedynak, BM et al., A computational method for computing an Alzheimer’s Disease Progression Score; experiments and validation with the ADNI dataset 20. Yan, J et al., Cortical surface biomarkers for predicting cognitive outcomes using group L21 norm 21. Boyle, CP et al., Physical Activity, Body Mass Index, and Brain Atrophy in Alzheimer’s Disease 22. Madsen, SK et al., Higher homocysteine associated with thinner cortical gray matter in 803 ADNI subjects
Contents lists available at ScienceDirect
Neurobiology of Aging journal homepage: www.elsevier.com/locate/neuaging
Guest editorial Recent years have seen an explosion of research on imaging biomarkers for Alzheimer’s disease (AD) and related neurodegenerative disorders, fueled by the realization that the enormous cost of conducting clinical trials for disease-modifying treatments stands as a major obstacle on the path to finding cures. Imaging biomarkers promise to reduce this cost by allowing more accurate detection of prodromal disease and more accurate monitoring of the effects of disease and treatment on the brain. Much of the progress in improving imaging biomarkers for neurodegenerative disease has been driven by the development of novel computational imaging analysis methodology, particularly in the areas of segmentation, registration, classification, and highdimensional statistics. Progress has also been aided by the availability of large imaging data sets like the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and its European and Asian counterparts. This special issue on Novel Imaging Biomarkers for Alzheimer’s Disease and Related Disorders features 22 research articles that specifically focus on the computational methodology aspects of imaging biomarker development in AD. Of the 22 articles, 9 were focused on morphometry, 6 on network and connectivity, 4 on imaging-functioning relationships, and 14 used the ADNI data for their analyses. Many of the articles in the special issue introduce new techniques for large-scale imaging data analysis, evaluate recently developed techniques in the context of AD biomarker development or adapt techniques from other fields to AD neuroimaging analysis. Innovations are in the areas of machine learning (e.g., Eskildsen et al., Raamana et al.), nonlinear regression (e.g., Hibar et al., Jahanshad et al., Shen et al.), unbiased longitudinal analysis (e.g., Prados et al., Gutman et al.), diffusion MRI connectomics (Nir et al., Prasad et al.), and fusion of diverse biomarkers into more predictive compound biomarkers (e.g., Ming et al., Khan et al., Duchesne et al., Jedynak et al.). Reflective of the field at large, the special issue is predominantly morphometric (9 articles): Two articles describe the development of MTL markers for AD (Miller et al. presents a template-based highdimensional mapping of the amygdala and statistical shape measures; Duchesne et al. use tissue and deformation maps within an MTL volume of interest to define and track disease evaluation factor). Eskildsen et al. use hippocampal grading score and cortical thickness to predict clinical progression. Madsen et al. present a longitudinal ventricular marker that can be related to atrophy of cortical regions. This is followed by Lorenzi et al. that describes a registration method that can separately model the aging and AD disease process. Several articles present methods for integrating multiple types of information. Two articles integrate different types of morphometric measures to improve biomarkers (Jing e al. describe an integrated 0197-4580/$ e see front matter Ó 2015 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.neurobiolaging.2014.09.021
measure of cortical thickness and cortical geometry, and Khan et al. present a unified approach for voxel- and tensor-based morphometry using registration confidence). Gutman et al. combine tensorbased morphometry and ventricular surface measures, and Prados present a generalized boundary shift interval method, both to improve clinical trial power. Network and connectivity is represented by 6 articles. Raamana et al. present a novel anatomic network constructed from measures of thickness similarity and show that thus derivednetwork features can achieve improved classification. This is followed by 4 diffusion tensor imaging (DTI)-based connectivity articles from the Thompson group (Jahanshad et al., Nir et al., Nir et al., Prasad et al., all). Jahanshad et al. use a regression technique adapted from econometrics to identify brain connectivities that are related to clinical decline. Nir et al. use DTI to construct and identify network properties that predict longitudinal volumetric reduction. Nir et al. measure white-matter properties derived from maximum density paths to improve classification performance. Prasad et al. define a novel, flow-based network measure from DTI for classification. Last, Das et al. use functional connectivity to demonstrate that anterior and poster MTL networks are both vulnerable to AD pathology. The next 3 articles describe methods for studying the effects of SNP-SNP interactions on brain regional volumes (Hibar et al.), PiB scan registration without structural MRI (Bourgeat et al.), and evaluation of accelerated T1-weighted imaging (Ching et al.). The special issue is rounded out with a series of 4 articles that use neuroimaging markers to relate to the life style and cognitive functioning of an individual. Jedynak et al. present a computational method that relates combinations of cognitive measures and structural volumes to clinical progression. Yan et al. uses a refined regression method to relate structural neuroimaging measures to cognitive outcomes. Boyle et al. establish an association between higher body mass index and lower brain volumes, and physical activity can help preserve brain volumes. Madsen et al. find that higher homocysteine levels are associated with cortical thinning. This special issue clearly demonstrates the profound impact that open studies such as ADNI and OASIS have had on the field. More than half of the articles in the special issue use ADNI, OASIS, or both for analysis and evaluation. Notably, several studies use multimodality MRI data from ADNI2 (e.g., Nir et al., Jahanshad et al., Prasad et al.). Overall, the work included in the current issue provides an excellent snapshot of the state of the art in the use of computational analysis techniques to derive neuroimaging AD biomarkers. Most of these methodological advances are not limited to the study of AD and dementia and have good potential to impact other
S2
Guest Editorial / Neurobiology of Aging 36 (2015) S1eS2
neuroimaging applications. The next frontier may very well lie in finding ways to compare, in a comprehensive and unbiased way, the power and reliability of the many proposed biomarkers. With large clinical trials of disease-modifying agents underway, it may be necessary for the field to come up with a set of “standard” biomarkers to use for disease progression and cohort stratification purposes, and doing so would require further comparative analysis of the techniques presented in this special issue and elsewhere. Lei Wang* Departments of Psychiatry and Behavioral Sciences and Radiology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA Paul Yushkevich Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA Sebastien Ourselin Centre for Medical Image Computing, University College London, London, UK * Corresponding
author at: Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, 710 N. Lake Shore Drive, Abbott Hall 1322, Chicago, IL 60611, USA. E-mail address: [email protected] (L. Wang)
Table of content: I. High-dimensional morphometry: 1. Miller, MI et al., Amygdalar Atrophy in Symptomatic AD Based on Diffeomorphometry: The BIOCARD Cohort. 2. Duchesne, S et al., Single Timepoint High-Dimensional Morphometry In Alzheimer’s Disease: Group Statistics on Longitudinally Acquired Data. 3. Eskildsen, SF et al., Structural imaging biomarkers of Alzheimer’s disease: predicting disease progression 4. Madsen, SK et al., Mapping ventricular expansion onto cortical gray matter in older adults 5. Lorenzi, M et al., Disentangling the Normal Aging from the Pathological Alzheimer’s Disease Progression on Crosssectional Structural MR Images
6. Ming, J et al., Integrated Cortical Structural Marker for Alzheimer’s Disease 7. Khan, AR et al., Unified Voxel and Tensor-based Morphometry (UVTBM) using Registration Confidence 8. Gutman, BA et al., Empowering Imaging Biomarkers of Alzheimer’s Disease 9. Prados, F et al., Measuring brain atrophy with a generalised formulation of the boundary shift integral II. Network and connectivity: 10. Raamana, PR et al., Thickness Network (ThickNet) Features for Prognostic Applications in Dementia 11. Jahanshad, N et al., Seemingly Unrelated Regression empowers detection of network failure in dementia 12. Nir, TM et al., Connectivity network measures predict volumetric atrophy in mild cognitive impairment 13. Prasad, G et al., Brain connectivity and novel network measures for Alzheimer’s disease classification 14. Nir, TM et al., DTI-based maximum density path analysis and classification of Alzheimer’s disease. 15. Das, S et al., Anterior and Posterior MTL Networks in Aging and MCI III. Imaging genetics: 16. Hibar, DP et al., Genome-wide interaction analysis reveals replicated epistatic effects on brain structure. IV. Functional/Multimodal imaging: 17. Bourgeat, P et al., Comparison of MR-less PiB SUVR quantification methods V. Imaging technology: 18. Ching, et al., Does MRI scan acceleration affect power to track brain change? VI. Functioning: 19. Jedynak, BM et al., A computational method for computing an Alzheimer’s Disease Progression Score; experiments and validation with the ADNI dataset 20. Yan, J et al., Cortical surface biomarkers for predicting cognitive outcomes using group L21 norm 21. Boyle, CP et al., Physical Activity, Body Mass Index, and Brain Atrophy in Alzheimer’s Disease 22. Madsen, SK et al., Higher homocysteine associated with thinner cortical gray matter in 803 ADNI subjects
