1
|
Gao N, Ye C, Chen H, Hao X, Ma T. MRI-based axis-referenced morphometric model corresponding to lamellar organization for assessing hippocampal atrophy in dementia. Hum Brain Mapp 2024; 45:e26715. [PMID: 38994693 PMCID: PMC11240145 DOI: 10.1002/hbm.26715] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 03/21/2024] [Accepted: 05/04/2024] [Indexed: 07/13/2024] Open
Abstract
Research on the local hippocampal atrophy for early detection of dementia has gained considerable attention. However, accurately quantifying subtle atrophy remains challenging in existing morphological methods due to the lack of consistent biological correspondence with the complex curving regions like the hippocampal head. Thereby, this article presents an innovative axis-referenced morphometric model (ARMM) that follows the anatomical lamellar organization of the hippocampus, which capture its precise and consistent longitudinal curving trajectory. Specifically, we establish an "axis-referenced coordinate system" based on a 7 T ex vivo hippocampal atlas following its entire curving longitudinal axis and orthogonal distributed lamellae. We then align individual hippocampi by deforming this template coordinate system to target spaces using boundary-guided diffeomorphic transformation, while ensuring that the lamellar vectors adhere to the constraint of medial-axis geometry. Finally, we measure local thickness and curvatures based on the coordinate system and boundary surface reconstructed from vector tips. The morphometric accuracy is evaluated by comparing reconstructed surfaces with those directly extracted from 7 T and 3 T MRI hippocampi. The results demonstrate that ARMM achieves the best performance, particularly in the curving head, surpassing the state-of-the-art morphological models. Additionally, morphological measurements from ARMM exhibit higher discriminatory power in distinguishing early Alzheimer's disease from mild cognitive impairment compared to volume-based measurements. Overall, the ARMM offers a precise morphometric assessment of hippocampal morphology on MR images, and sheds light on discovering potential image markers for neurodegeneration associated with hippocampal impairment.
Collapse
Affiliation(s)
- Na Gao
- Electronic & Information Engineering SchoolHarbin Institute of Technology (Shenzhen)ShenzhenChina
| | - Chenfei Ye
- International Research Institute for Artificial Intelligence, Harbin Institute of Technology at ShenzhenShenzhenChina
| | - Hantao Chen
- Electronic & Information Engineering SchoolHarbin Institute of Technology (Shenzhen)ShenzhenChina
| | - Xingyu Hao
- Electronic & Information Engineering SchoolHarbin Institute of Technology (Shenzhen)ShenzhenChina
| | - Ting Ma
- Electronic & Information Engineering SchoolHarbin Institute of Technology (Shenzhen)ShenzhenChina
- International Research Institute for Artificial Intelligence, Harbin Institute of Technology at ShenzhenShenzhenChina
- Peng Cheng LaboratoryShenzhenChina
| |
Collapse
|
2
|
Zhong T, Wang Y, Xu X, Wu X, Liang S, Ning Z, Wang L, Niu Y, Li G, Zhang Y. A brain subcortical segmentation tool based on anatomy attentional fusion network for developing macaques. Comput Med Imaging Graph 2024; 116:102404. [PMID: 38870599 DOI: 10.1016/j.compmedimag.2024.102404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 05/21/2024] [Accepted: 05/22/2024] [Indexed: 06/15/2024]
Abstract
Magnetic Resonance Imaging (MRI) plays a pivotal role in the accurate measurement of brain subcortical structures in macaques, which is crucial for unraveling the complexities of brain structure and function, thereby enhancing our understanding of neurodegenerative diseases and brain development. However, due to significant differences in brain size, structure, and imaging characteristics between humans and macaques, computational tools developed for human neuroimaging studies often encounter obstacles when applied to macaques. In this context, we propose an Anatomy Attentional Fusion Network (AAF-Net), which integrates multimodal MRI data with anatomical constraints in a multi-scale framework to address the challenges posed by the dynamic development, regional heterogeneity, and age-related size variations of the juvenile macaque brain, thus achieving precise subcortical segmentation. Specifically, we generate a Signed Distance Map (SDM) based on the initial rough segmentation of the subcortical region by a network as an anatomical constraint, providing comprehensive information on positions, structures, and morphology. Then we construct AAF-Net to fully fuse the SDM anatomical constraints and multimodal images for refined segmentation. To thoroughly evaluate the performance of our proposed tool, over 700 macaque MRIs from 19 datasets were used in this study. Specifically, we employed two manually labeled longitudinal macaque datasets to develop the tool and complete four-fold cross-validations. Furthermore, we incorporated various external datasets to demonstrate the proposed tool's generalization capabilities and promise in brain development research. We have made this tool available as an open-source resource at https://github.com/TaoZhong11/Macaque_subcortical_segmentation for direct application.
Collapse
Affiliation(s)
- Tao Zhong
- School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing and Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, China
| | - Ya Wang
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, USA
| | - Xiaotong Xu
- School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing and Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, China
| | - Xueyang Wu
- School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing and Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, China
| | - Shujun Liang
- School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing and Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, China
| | - Zhenyuan Ning
- School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing and Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, China
| | - Li Wang
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, USA
| | - Yuyu Niu
- Yunnan Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, China
| | - Gang Li
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, USA.
| | - Yu Zhang
- School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing and Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, China.
| |
Collapse
|
3
|
Davidson TL, Stevenson RJ. Vulnerability of the Hippocampus to Insults: Links to Blood-Brain Barrier Dysfunction. Int J Mol Sci 2024; 25:1991. [PMID: 38396670 PMCID: PMC10888241 DOI: 10.3390/ijms25041991] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 01/25/2024] [Accepted: 01/29/2024] [Indexed: 02/25/2024] Open
Abstract
The hippocampus is a critical brain substrate for learning and memory; events that harm the hippocampus can seriously impair mental and behavioral functioning. Hippocampal pathophysiologies have been identified as potential causes and effects of a remarkably diverse array of medical diseases, psychological disorders, and environmental sources of damage. It may be that the hippocampus is more vulnerable than other brain areas to insults that are related to these conditions. One purpose of this review is to assess the vulnerability of the hippocampus to the most prevalent types of insults in multiple biomedical domains (i.e., neuroactive pathogens, neurotoxins, neurological conditions, trauma, aging, neurodegenerative disease, acquired brain injury, mental health conditions, endocrine disorders, developmental disabilities, nutrition) and to evaluate whether these insults affect the hippocampus first and more prominently compared to other brain loci. A second purpose is to consider the role of hippocampal blood-brain barrier (BBB) breakdown in either causing or worsening the harmful effects of each insult. Recent research suggests that the hippocampal BBB is more fragile compared to other brain areas and may also be more prone to the disruption of the transport mechanisms that act to maintain the internal milieu. Moreover, a compromised BBB could be a factor that is common to many different types of insults. Our analysis indicates that the hippocampus is more vulnerable to insults compared to other parts of the brain, and that developing interventions that protect the hippocampal BBB may help to prevent or ameliorate the harmful effects of many insults on memory and cognition.
Collapse
Affiliation(s)
- Terry L. Davidson
- Department of Neuroscience, Center for Neuroscience and Behavior, American University, 4400 Massachusetts Avenue, NW, Washington, DC 20016, USA
| | | |
Collapse
|
4
|
Gao N, Liu Z, Deng Y, Chen H, Ye C, Yang Q, Ma T. MR-based spatiotemporal anisotropic atrophy evaluation of hippocampus in Alzheimer's disease progression by multiscale skeletal representation. Hum Brain Mapp 2023; 44:5180-5197. [PMID: 37608620 PMCID: PMC10502645 DOI: 10.1002/hbm.26460] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 07/14/2023] [Accepted: 08/02/2023] [Indexed: 08/24/2023] Open
Abstract
Increasing evidence has shown a higher sensitivity of Alzheimer's disease (AD) progression by local hippocampal atrophy rather than the whole volume. However, existing morphological methods based on subfield-volume or surface in imaging studies are not capable to describe the comprehensive process of hippocampal atrophy as sensitive as histological findings. To map histological distinctive measurements onto medical magnetic resonance (MR) images, we propose a multiscale skeletal representation (m-s-rep) to quantify focal hippocampal atrophy during AD progression in longitudinal cohorts from the Alzheimer's Disease Neuroimaging Initiative (ADNI). The m-s-rep captures large-to-small-scale hippocampal morphology by spoke interpolation over label projection on skeletal models. To enhance morphological correspondence within subjects, we align the longitudinal m-s-reps by surface-based transformations from baseline to subsequent timepoints. Cross-sectional and longitudinal measurements derived from m-s-rep are statistically analyzed to comprehensively evaluate the bilateral hippocampal atrophy. Our findings reveal that during the early AD progression, atrophy primarily affects the lateral-medial extent of the hippocampus, with a difference of 1.8 mm in lateral-medial width in 2 years preceding conversion (p < .001), and the medial head exhibits a maximum difference of 3.05%/year in local atrophy rate (p = .011) compared to controls. Moreover, progressive mild cognitive impairment (pMCI) exhibits more severe and widespread atrophy in the head and body compared to stable mild cognitive impairment (sMCI), with a maximum difference of 1.21 mm in thickness in the medial head 1 year preceding conversion (p = .012). In summary, our proposed method can quantitatively measure the hippocampal morphological changes on 3T MR images, potentially assisting the pre-diagnosis and prognosis of AD.
Collapse
Affiliation(s)
- Na Gao
- Department of Electronic & Information EngineeringHarbin Institute of Technology (Shenzhen)ShenzhenChina
| | - Zhiyuan Liu
- Department of Computer ScienceUniversity of North Carolina atChapel HillNorth CarolinaUSA
| | - Yuesheng Deng
- Department of Electronic & Information EngineeringHarbin Institute of Technology (Shenzhen)ShenzhenChina
| | - Hantao Chen
- Department of Electronic & Information EngineeringHarbin Institute of Technology (Shenzhen)ShenzhenChina
| | - Chenfei Ye
- International Research Institute for Artificial IntelligenceHarbin Institute of Technology at ShenzhenShenzhenChina
- Peng Cheng LaboratoryShenzhenChina
| | - Qi Yang
- Department of Radiology, Beijing Chaoyang HospitalCapital Medical UniversityBeijingChina
- Key Lab of Medical Engineering for Cardiovascular DiseaseMinistry of EducationBeijingChina
- Beijing Advanced Innovation Center for Big Data‐Based Precision MedicineBeijingChina
| | - Ting Ma
- Department of Electronic & Information EngineeringHarbin Institute of Technology (Shenzhen)ShenzhenChina
- International Research Institute for Artificial IntelligenceHarbin Institute of Technology at ShenzhenShenzhenChina
- Peng Cheng LaboratoryShenzhenChina
- Guangdong Provincial Key Laboratory of Aerospace Communication and Networking TechnologyHarbin Institute of Technology (Shenzhen)ShenzhenChina
| |
Collapse
|
5
|
Stephenson D, Belfiore-Oshan R, Karten Y, Keavney J, Kwok DK, Martinez T, Montminy J, Müller MLTM, Romero K, Sivakumaran S. Transforming Drug Development for Neurological Disorders: Proceedings from a Multidisease Area Workshop. Neurotherapeutics 2023; 20:1682-1691. [PMID: 37823970 PMCID: PMC10684834 DOI: 10.1007/s13311-023-01440-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/06/2023] [Indexed: 10/13/2023] Open
Abstract
Neurological disorders represent some of the most challenging therapeutic areas for successful drug approvals. The escalating global burden of death and disability for such diseases represents a significant worldwide public health challenge, and the rate of failure of new therapies for chronic progressive disorders of the nervous system is higher relative to other non-neurological conditions. However, progress is emerging rapidly in advancing the drug development landscape in both rare and common neurodegenerative diseases. In October 2022, the Critical Path Institute (C-Path) and the US Food and Drug Administration (FDA) organized a Neuroscience Annual Workshop convening representatives from the drug development industry, academia, the patient community, government agencies, and regulatory agencies regarding the future development of tools and therapies for neurological disorders. This workshop focused on five chronic progressive diseases: Alzheimer's disease, Parkinson's disease, Huntington's disease, Duchenne muscular dystrophy, and inherited ataxias. This special conference report reviews the key points discussed during the three-day dynamic workshop, including shared learnings, and recommendations that promise to catalyze future advancement of novel therapies and drug development tools.
Collapse
|
6
|
Yearley AG, Goedmakers CMW, Panahi A, Doucette J, Rana A, Ranganathan K, Smith TR. FDA-approved machine learning algorithms in neuroradiology: A systematic review of the current evidence for approval. Artif Intell Med 2023; 143:102607. [PMID: 37673576 DOI: 10.1016/j.artmed.2023.102607] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 05/30/2023] [Accepted: 06/05/2023] [Indexed: 09/08/2023]
Abstract
Over the past decade, machine learning (ML) and artificial intelligence (AI) have become increasingly prevalent in the medical field. In the United States, the Food and Drug Administration (FDA) is responsible for regulating AI algorithms as "medical devices" to ensure patient safety. However, recent work has shown that the FDA approval process may be deficient. In this study, we evaluate the evidence supporting FDA-approved neuroalgorithms, the subset of machine learning algorithms with applications in the central nervous system (CNS), through a systematic review of the primary literature. Articles covering the 53 FDA-approved algorithms with applications in the CNS published in PubMed, EMBASE, Google Scholar and Scopus between database inception and January 25, 2022 were queried. Initial searches identified 1505 studies, of which 92 articles met the criteria for extraction and inclusion. Studies were identified for 26 of the 53 neuroalgorithms, of which 10 algorithms had only a single peer-reviewed publication. Performance metrics were available for 15 algorithms, external validation studies were available for 24 algorithms, and studies exploring the use of algorithms in clinical practice were available for 7 algorithms. Papers studying the clinical utility of these algorithms focused on three domains: workflow efficiency, cost savings, and clinical outcomes. Our analysis suggests that there is a meaningful gap between the FDA approval of machine learning algorithms and their clinical utilization. There appears to be room for process improvement by implementation of the following recommendations: the provision of compelling evidence that algorithms perform as intended, mandating minimum sample sizes, reporting of a predefined set of performance metrics for all algorithms and clinical application of algorithms prior to widespread use. This work will serve as a baseline for future research into the ideal regulatory framework for AI applications worldwide.
Collapse
Affiliation(s)
- Alexander G Yearley
- Harvard Medical School, 25 Shattuck St, Boston, MA 02115, USA; Computational Neuroscience Outcomes Center (CNOC), Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115, USA.
| | - Caroline M W Goedmakers
- Computational Neuroscience Outcomes Center (CNOC), Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115, USA; Department of Neurosurgery, Leiden University Medical Center, Albinusdreef 2, 2333 ZA Leiden, Netherlands
| | - Armon Panahi
- The George Washington University School of Medicine and Health Sciences, 2300 I St NW, Washington, DC 20052, USA
| | - Joanne Doucette
- Computational Neuroscience Outcomes Center (CNOC), Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115, USA; School of Pharmacy, MCPHS University, 179 Longwood Ave, Boston, MA 02115, USA
| | - Aakanksha Rana
- Computational Neuroscience Outcomes Center (CNOC), Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115, USA; Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, USA
| | - Kavitha Ranganathan
- Division of Plastic Surgery, Brigham and Women's Hospital, 75 Francis St, Boston, MA 02115, USA
| | - Timothy R Smith
- Harvard Medical School, 25 Shattuck St, Boston, MA 02115, USA; Computational Neuroscience Outcomes Center (CNOC), Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115, USA
| |
Collapse
|
7
|
Hu X, Meier M, Pruessner J. Challenges and opportunities of diagnostic markers of Alzheimer's disease based on structural magnetic resonance imaging. Brain Behav 2023; 13:e2925. [PMID: 36795041 PMCID: PMC10013953 DOI: 10.1002/brb3.2925] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Accepted: 02/04/2023] [Indexed: 02/17/2023] Open
Abstract
OBJECTIVES This article aimed to carry out a narrative literature review of early diagnostic markers of Alzheimer's disease (AD) based on both micro and macro levels of pathology, indicating the shortcomings of current biomarkers and proposing a novel biomarker of structural integrity that associates the hippocampus and adjacent ventricle together. This could help to reduce the influence of individual variety and improve the accuracy and validity of structural biomarker. METHODS This review was based on presenting comprehensive background of early diagnostic markers of AD. We have compiled those markers into micro level and macro level, and discussed the advantages and disadvantages of them. Eventually the ratio of gray matter volume to ventricle volume was put forward. RESULTS The costly methodologies and related high patient burden of "micro" biomarkers (cerebrospinal fluid biomarkers) hinder the implementation in routine clinical examination. In terms of "macro" biomarkers- hippocampal volume (HV), there is a large variation of it among population, which undermines its validity Considering the gray matter atrophies while the adjacent ventricular volume enlarges, we assume the hippocampal to ventricle ratio (HVR) is a more reliable marker than HV alone the emerging evidence showed hippocampal to ventricle ratio predicts memory functions better than HV alone in elderly sample. CONCLUSIONS The ratio between gray matter structures and adjacent ventricular volumes counts as a promising superior diagnostic marker of early neurodegeneration.
Collapse
Affiliation(s)
- Xiang Hu
- Department of Psychology, University of Konstanz, Konstanz, Germany
| | - Maria Meier
- Department of Psychology, University of Konstanz, Konstanz, Germany
| | - Jens Pruessner
- Department of Psychology, University of Konstanz, Konstanz, Germany
| |
Collapse
|
8
|
Izmailova ES, Maguire RP, McCarthy TJ, Müller MLTM, Murphy P, Stephenson D. Empowering drug development: Leveraging insights from imaging technologies to enable the advancement of digital health technologies. Clin Transl Sci 2023; 16:383-397. [PMID: 36382716 PMCID: PMC10014695 DOI: 10.1111/cts.13461] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 09/27/2022] [Accepted: 11/03/2022] [Indexed: 11/17/2022] Open
Abstract
The US Food and Drug Administration (FDA) has publicly recognized the importance of improving drug development efficiency, deeming translational biomarkers a top priority. The use of imaging biomarkers has been associated with increased rates of drug approvals. An appropriate level of validation provides a pragmatic way to choose and implement these biomarkers. Standardizing imaging modality selection, data acquisition protocols, and image analysis (in ways that are agnostic to equipment and algorithms) have been key to imaging biomarker deployment. The best known examples come from studies done via precompetitive collaboration efforts, which enable input from multiple stakeholders and data sharing. Digital health technologies (DHTs) provide an opportunity to measure meaningful aspects of patient health, including patient function, for extended periods of time outside of the hospital walls, with objective, sensor-based measures. We identified the areas where learnings from the imaging biomarker field can accelerate the adoption and widespread use of DHTs to develop novel treatments. As with imaging, technical validation parameters and performance acceptance thresholds need to be established. Approaches amenable to multiple hardware options and data processing algorithms can be enabled by sharing DHT data and by cross-validating algorithms. Data standardization and creation of shared databases will be vital. Pre-competitive consortia (public-private partnerships and professional societies that bring together all stakeholders, including patient organizations, industry, academic experts, and regulators) will advance the regulatory maturity of DHTs in clinical trials.
Collapse
|
9
|
Müller MLTM, Stephenson DT. Leveraging the regulatory framework to facilitate drug development in Parkinson's disease. HANDBOOK OF CLINICAL NEUROLOGY 2023; 193:347-360. [PMID: 36803822 DOI: 10.1016/b978-0-323-85555-6.00015-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/18/2023]
Abstract
There is an exigent need for disease-modifying and symptomatic treatment approaches for Parkinson's disease. A better understanding of Parkinson's disease pathophysiology and new insights in genetics has opened exciting new venues for pharmacological treatment targets. There are, however, many challenges on the path from discovery to drug approval. These challenges revolve around appropriate endpoint selection, the lack of accurate biomarkers, challenges with diagnostic accuracy, and other challenges commonly encountered by drug developers. The regulatory health authorities, however, have provided tools to provide guidance for drug development and to assist with these challenges. The main goal of the Critical Path for Parkinson's Consortium, a nonprofit public-private partnership part of the Critical Path Institute, is to advance these so-called drug development tools for Parkinson's disease trials. The focus of this chapter will be on how the health regulators' tools were successfully leveraged to facilitate drug development in Parkinson's disease and other neurodegenerative diseases.
Collapse
Affiliation(s)
- Martijn L T M Müller
- Critical Path for Parkinson's Consortium - Critical Path Institute, Tucson, AZ, United States.
| | - Diane T Stephenson
- Critical Path for Parkinson's Consortium - Critical Path Institute, Tucson, AZ, United States
| |
Collapse
|
10
|
Loftus JR, Puri S, Meyers SP. Multimodality imaging of neurodegenerative disorders with a focus on multiparametric magnetic resonance and molecular imaging. Insights Imaging 2023; 14:8. [PMID: 36645560 PMCID: PMC9842851 DOI: 10.1186/s13244-022-01358-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Accepted: 12/13/2022] [Indexed: 01/17/2023] Open
Abstract
Neurodegenerative diseases afflict a large number of persons worldwide, with the prevalence and incidence of dementia rapidly increasing. Despite their prevalence, clinical diagnosis of dementia syndromes remains imperfect with limited specificity. Conventional structural-based imaging techniques also lack the accuracy necessary for confident diagnosis. Multiparametric magnetic resonance imaging and molecular imaging provide the promise of improving specificity and sensitivity in the diagnosis of neurodegenerative disease as well as therapeutic monitoring of monoclonal antibody therapy. This educational review will briefly focus on the epidemiology, clinical presentation, and pathologic findings of common and uncommon neurodegenerative diseases. Imaging features of each disease spanning from conventional magnetic resonance sequences to advanced multiparametric methods such as resting-state functional magnetic resonance imaging and arterial spin labeling imaging will be described in detail. Additionally, the review will explore the findings of each diagnosis on molecular imaging including single-photon emission computed tomography and positron emission tomography with a variety of clinically used and experimental radiotracers. The literature and clinical cases provided demonstrate the power of advanced magnetic resonance imaging and molecular techniques in the diagnosis of neurodegenerative diseases and areas of future and ongoing research. With the advent of combined positron emission tomography/magnetic resonance imaging scanners, hybrid protocols utilizing both techniques are an attractive option for improving the evaluation of neurodegenerative diseases.
Collapse
Affiliation(s)
- James Ryan Loftus
- grid.412750.50000 0004 1936 9166Department of Imaging Sciences, University of Rochester Medical Center, 601 Elmwood Ave, Rochester, NY 14642 USA
| | - Savita Puri
- grid.412750.50000 0004 1936 9166Department of Imaging Sciences, University of Rochester Medical Center, 601 Elmwood Ave, Rochester, NY 14642 USA
| | - Steven P. Meyers
- grid.412750.50000 0004 1936 9166Department of Imaging Sciences, University of Rochester Medical Center, 601 Elmwood Ave, Rochester, NY 14642 USA
| |
Collapse
|
11
|
Arnold TC, Freeman CW, Litt B, Stein JM. Low-field MRI: Clinical promise and challenges. J Magn Reson Imaging 2023; 57:25-44. [PMID: 36120962 PMCID: PMC9771987 DOI: 10.1002/jmri.28408] [Citation(s) in RCA: 50] [Impact Index Per Article: 50.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Revised: 08/11/2022] [Accepted: 08/12/2022] [Indexed: 02/03/2023] Open
Abstract
Modern MRI scanners have trended toward higher field strengths to maximize signal and resolution while minimizing scan time. However, high-field devices remain expensive to install and operate, making them scarce outside of high-income countries and major population centers. Low-field strength scanners have drawn renewed academic, industry, and philanthropic interest due to advantages that could dramatically increase imaging access, including lower cost and portability. Nevertheless, low-field MRI still faces inherent limitations in image quality that come with decreased signal. In this article, we review advantages and disadvantages of low-field MRI scanners, describe hardware and software innovations that accentuate advantages and mitigate disadvantages, and consider clinical applications for a new generation of low-field devices. In our review, we explore how these devices are being or could be used for high acuity brain imaging, outpatient neuroimaging, MRI-guided procedures, pediatric imaging, and musculoskeletal imaging. Challenges for their successful clinical translation include selecting and validating appropriate use cases, integrating with standards of care in high resource settings, expanding options with actionable information in low resource settings, and facilitating health care providers and clinical practice in new ways. By embracing both the promise and challenges of low-field MRI, clinicians and researchers have an opportunity to transform medical care for patients around the world. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 6.
Collapse
Affiliation(s)
- Thomas Campbell Arnold
- Department of Bioengineering, School of Engineering & Applied ScienceUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Center for Neuroengineering and TherapeuticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Colbey W. Freeman
- Department of Radiology, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Brian Litt
- Center for Neuroengineering and TherapeuticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Neurology, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Joel M. Stein
- Center for Neuroengineering and TherapeuticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Radiology, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| |
Collapse
|
12
|
Park C, Jang JW, Joo G, Kim Y, Kim S, Byeon G, Park SW, Kasani PH, Yum S, Pyun JM, Park YH, Lim JS, Youn YC, Choi HS, Park C, Im H, Kim S. Predicting progression to dementia with “comprehensive visual rating scale” and machine learning algorithms. Front Neurol 2022; 13:906257. [PMID: 36071894 PMCID: PMC9443667 DOI: 10.3389/fneur.2022.906257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 07/14/2022] [Indexed: 11/17/2022] Open
Abstract
Background and Objective Identifying biomarkers for predicting progression to dementia in patients with mild cognitive impairment (MCI) is crucial. To this end, the comprehensive visual rating scale (CVRS), which is based on magnetic resonance imaging (MRI), was developed for the assessment of structural changes in the brains of patients with MCI. This study aimed to investigate the use of the CVRS score for predicting dementia in patients with MCI over a 2-year follow-up period using various machine learning (ML) algorithms. Methods We included 197 patients with MCI who were followed up more than once. The data used for this study were obtained from the Japanese-Alzheimer's Disease Neuroimaging Initiative study. We assessed all the patients using their CVRS scores, cortical thickness data, and clinical data to determine their progression to dementia during a follow-up period of over 2 years. ML algorithms, such as logistic regression, random forest (RF), XGBoost, and LightGBM, were applied to the combination of the dataset. Further, feature importance that contributed to the progression from MCI to dementia was analyzed to confirm the risk predictors among the various variables evaluated. Results Of the 197 patients, 108 (54.8%) showed progression from MCI to dementia. Tree-based classifiers, such as XGBoost, LightGBM, and RF, achieved relatively high performance. In addition, the prediction models showed better performance when clinical data and CVRS score (accuracy 0.701–0.711) were used than when clinical data and cortical thickness (accuracy 0.650–0.685) were used. The features related to CVRS helped predict progression to dementia using the tree-based models compared to logistic regression. Conclusions Tree-based ML algorithms can predict progression from MCI to dementia using baseline CVRS scores combined with clinical data.
Collapse
Affiliation(s)
- Chaeyoon Park
- Department of Convergence Security, Kangwon National University, Chuncheon, South Korea
| | - Jae-Won Jang
- Department of Convergence Security, Kangwon National University, Chuncheon, South Korea
- Department of Neurology, Kangwon National University Hospital, Kangwon National University College of Medicine, Chuncheon, South Korea
- Interdisciplinary Graduate Program in Medical Bigdata Convergence, Chuncheon, South Korea
| | - Gihun Joo
- Interdisciplinary Graduate Program in Medical Bigdata Convergence, Chuncheon, South Korea
| | - Yeshin Kim
- Department of Neurology, Kangwon National University Hospital, Kangwon National University College of Medicine, Chuncheon, South Korea
| | - Seongheon Kim
- Department of Neurology, Kangwon National University Hospital, Kangwon National University College of Medicine, Chuncheon, South Korea
| | - Gihwan Byeon
- Department of Psychiatry, Kangwon National University Hospital, Kangwon National University College of Medicine, Chuncheon, South Korea
| | - Sang Won Park
- Interdisciplinary Graduate Program in Medical Bigdata Convergence, Chuncheon, South Korea
| | | | - Sujin Yum
- Interdisciplinary Graduate Program in Medical Bigdata Convergence, Chuncheon, South Korea
| | - Jung-Min Pyun
- Department of Neurology, Soonchunhyang University Seoul Hospital, Soonchunhyang University College of Medicine, Seoul, South Korea
| | - Young Ho Park
- Department of Neurology, Seoul National University Bundang Hospital, Seongnam, South Korea
- Department of Neurology, Seoul National University College of Medicine, Seoul, South Korea
| | - Jae-Sung Lim
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
| | - Young Chul Youn
- Department of Neurology, Chung-Ang University Hospital, Chung-Ang University College of Medicine, Seoul, South Korea
| | - Hyun-Soo Choi
- Interdisciplinary Graduate Program in Medical Bigdata Convergence, Chuncheon, South Korea
- Department of Computer Science and Engineering, Kangwon National University, Chuncheon, South Korea
| | - Chihyun Park
- Interdisciplinary Graduate Program in Medical Bigdata Convergence, Chuncheon, South Korea
- Department of Computer Science and Engineering, Kangwon National University, Chuncheon, South Korea
| | - Hyeonseung Im
- Department of Convergence Security, Kangwon National University, Chuncheon, South Korea
- Interdisciplinary Graduate Program in Medical Bigdata Convergence, Chuncheon, South Korea
- Department of Computer Science and Engineering, Kangwon National University, Chuncheon, South Korea
- *Correspondence: Hyeonseung Im
| | - SangYun Kim
- Department of Neurology, Seoul National University Bundang Hospital, Seongnam, South Korea
- Department of Neurology, Seoul National University College of Medicine, Seoul, South Korea
- SangYun Kim
| |
Collapse
|
13
|
Cobigo Y, Goh MS, Wolf A, Staffaroni AM, Kornak J, Miller BL, Rabinovici GD, Seeley WW, Spina S, Boxer AL, Boeve BF, Wang L, Allegri R, Farlow M, Mori H, Perrin RJ, Kramer J, Rosen HJ. Detection of emerging neurodegeneration using Bayesian linear mixed-effect modeling. Neuroimage Clin 2022; 36:103144. [PMID: 36030718 PMCID: PMC9428846 DOI: 10.1016/j.nicl.2022.103144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Revised: 07/20/2022] [Accepted: 08/02/2022] [Indexed: 01/18/2023]
Abstract
Early detection of neurodegeneration, and prediction of when neurodegenerative diseases will lead to symptoms, are critical for developing and initiating disease modifying treatments for these disorders. While each neurodegenerative disease has a typical pattern of early changes in the brain, these disorders are heterogeneous, and early manifestations can vary greatly across people. Methods for detecting emerging neurodegeneration in any part of the brain are therefore needed. Prior publications have described the use of Bayesian linear mixed-effects (BLME) modeling for characterizing the trajectory of change across the brain in healthy controls and patients with neurodegenerative disease. Here, we use an extension of such a model to detect emerging neurodegeneration in cognitively healthy individuals at risk for dementia. We use BLME to quantify individualized rates of volume loss across the cerebral cortex from the first two MRIs in each person and then extend the BLME model to predict future values for each voxel. We then compare observed values at subsequent time points with the values that were expected from the initial rates of change and identify voxels that are lower than the expected values, indicating accelerated volume loss and neurodegeneration. We apply the model to longitudinal imaging data from cognitively normal participants in the Alzheimer's Disease Neuroimaging Initiative (ADNI), some of whom subsequently developed dementia, and two cognitively normal cases who developed pathology-proven frontotemporal lobar degeneration (FTLD). These analyses identified regions of accelerated volume loss prior to or accompanying the earliest symptoms, and expanding across the brain over time, in all cases. The changes were detected in regions that are typical for the likely diseases affecting each patient, including medial temporal regions in patients at risk for Alzheimer's disease, and insular, frontal, and/or anterior/inferior temporal regions in patients with likely or proven FTLD. In the cases where detailed histories were available, the first regions identified were consistent with early symptoms. Furthermore, survival analysis in the ADNI cases demonstrated that the rate of spread of accelerated volume loss across the brain was a statistically significant predictor of time to conversion to dementia. This method for detection of neurodegeneration is a potentially promising approach for identifying early changes due to a variety of diseases, without prior assumptions about what regions are most likely to be affected first in an individual.
Collapse
Affiliation(s)
- Yann Cobigo
- University of California, San Francisco, Department of Neurology, Memory and Aging Center, United States.
| | - Matthew S. Goh
- University of California, San Francisco, Department of Neurology, Memory and Aging Center, United States
| | - Amy Wolf
- University of California, San Francisco, Department of Neurology, Memory and Aging Center, United States
| | - Adam M. Staffaroni
- University of California, San Francisco, Department of Neurology, Memory and Aging Center, United States
| | - John Kornak
- University of California, San Francisco, Department of Epidemiology and Biostatistics, United States
| | - Bruce L. Miller
- University of California, San Francisco, Department of Neurology, Memory and Aging Center, United States
| | - Gil D. Rabinovici
- University of California, San Francisco, Department of Neurology, Memory and Aging Center, United States
| | - William W. Seeley
- University of California, San Francisco, Department of Neurology, Memory and Aging Center, United States
| | - Salvatore Spina
- University of California, San Francisco, Department of Neurology, Memory and Aging Center, United States
| | - Adam L. Boxer
- University of California, San Francisco, Department of Neurology, Memory and Aging Center, United States
| | | | - Lei Wang
- Northwestern University Feinberg School of Medicine, Department of Psychiatry and Behavioral Sciences and Department Radiology, United States
| | - Ricardo Allegri
- FLENI Institute of Neurological Research (Fundacion para la Lucha contra las Enfermedades Neurologicas de la Infancia), Argentina
| | | | - Hiroshi Mori
- Osaka City University Medical School, Department of Neurosciences, Japan
| | | | - Joel Kramer
- University of California, San Francisco, Department of Neurology, Memory and Aging Center, United States
| | - Howard J. Rosen
- University of California, San Francisco, Department of Neurology, Memory and Aging Center, United States
| | | |
Collapse
|
14
|
Fractal dimension of the brain in neurodegenerative disease and dementia: A systematic review. Ageing Res Rev 2022; 79:101651. [PMID: 35643264 DOI: 10.1016/j.arr.2022.101651] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Revised: 04/26/2022] [Accepted: 05/23/2022] [Indexed: 12/25/2022]
Abstract
Sensitive and specific antemortem biomarkers of neurodegenerative disease and dementia are crucial to the pursuit of effective treatments, required both to reliably identify disease and to track its progression. Atrophy is the structural magnetic resonance imaging (MRI) hallmark of neurodegeneration. However in most cases it likely indicates a relatively advanced stage of disease less susceptible to treatment as some disease processes begin decades prior to clinical onset. Among emerging metrics that characterise brain shape rather than volume, fractal dimension (FD) quantifies shape complexity. FD has been applied in diverse fields of science to measure subtle changes in elaborate structures. We review its application thus far to structural MRI of the brain in neurodegenerative disease and dementia. We identified studies involving subjects who met criteria for mild cognitive impairment, Alzheimer's Disease, Vascular Dementia, Lewy Body Dementia, Frontotemporal Dementia, Amyotrophic Lateral Sclerosis, Parkinson's Disease, Huntington's Disease, Multiple Systems Atrophy, Spinocerebellar Ataxia and Multiple Sclerosis. The early literature suggests that neurodegenerative disease processes are usually associated with a decline in FD of the brain. The literature includes examples of disease-related change in FD occurring independently of atrophy, which if substantiated would represent a valuable advantage over other structural imaging metrics. However, it is likely to be non-specific and to exhibit complex spatial and temporal patterns. A more harmonious methodological approach across a larger number of studies as well as careful attention to technical factors associated with image processing and FD measurement will help to better elucidate the metric's utility.
Collapse
|
15
|
Hendrikse NM, Llinares Garcia J, Vetter T, Humphreys AJ, Ehmann F. Biomarkers in Medicines Development-From Discovery to Regulatory Qualification and Beyond. Front Med (Lausanne) 2022; 9:878942. [PMID: 35559349 PMCID: PMC9086587 DOI: 10.3389/fmed.2022.878942] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Accepted: 03/14/2022] [Indexed: 11/25/2022] Open
Abstract
Biomarkers are important tools in medicines development and clinical practice. Besides their use in clinical trials, such as for enrichment of patients, monitoring safety or response to treatment, biomarkers are a cornerstone of precision medicine. The European Medicines Agency (EMA) emphasised the importance of the discovery, qualification, and use of biomarkers in their Regulatory Science Strategy to 2025, which included the recommendation to enhance early engagement with biomarker developers to facilitate regulatory qualification. This study explores the journey of biomarkers through the EU regulatory system and beyond, based on a review of interactions between developers and the EMA from 2008 to 2020, as well as the use of qualified biomarkers in clinical trials. Of applicants that used early interaction platforms such as the Innovation Task Force, less than half engaged in fee-related follow-up procedures. Results showed that, as compared to companies, consortia were more likely to opt for the Qualification of Novel Methodologies procedure and engage in follow-up procedures. Our results highlight the importance of early engagement with regulators for achieving biomarker qualification, including pre-submission discussions in the context of the qualification procedure. A review of clinical trials showed that all qualified biomarkers are used in practice, although not always according to the endorsed context of use. Overall, this study highlights important aspects of biomarker qualification, including opportunities to improve the seamless support for developers by EMA. The use of qualified biomarkers in clinical trials underlines the importance of regulatory qualification, which will further enable precision medicine for the benefit of patients.
Collapse
Affiliation(s)
- Natalie M Hendrikse
- Regulatory Science and Innovation Task Force, European Medicines Agency, Amsterdam, Netherlands
| | - Jordi Llinares Garcia
- Regulatory Science and Innovation Task Force, European Medicines Agency, Amsterdam, Netherlands
| | - Thorsten Vetter
- Scientific Advice Office, European Medicines Agency, Amsterdam, Netherlands
| | - Anthony J Humphreys
- Regulatory Science and Innovation Task Force, European Medicines Agency, Amsterdam, Netherlands
| | - Falk Ehmann
- Regulatory Science and Innovation Task Force, European Medicines Agency, Amsterdam, Netherlands
| |
Collapse
|
16
|
Ross DE, Seabaugh J, Seabaugh JM, Barcelona J, Seabaugh D, Wright K, Norwind L, King Z, Graham TJ, Baker J, Lewis T. Updated Review of the Evidence Supporting the Medical and Legal Use of NeuroQuant ® and NeuroGage ® in Patients With Traumatic Brain Injury. Front Hum Neurosci 2022; 16:715807. [PMID: 35463926 PMCID: PMC9027332 DOI: 10.3389/fnhum.2022.715807] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Accepted: 03/03/2022] [Indexed: 02/05/2023] Open
Abstract
Over 40 years of research have shown that traumatic brain injury affects brain volume. However, technical and practical limitations made it difficult to detect brain volume abnormalities in patients suffering from chronic effects of mild or moderate traumatic brain injury. This situation improved in 2006 with the FDA clearance of NeuroQuant®, a commercially available, computer-automated software program for measuring MRI brain volume in human subjects. More recent strides were made with the introduction of NeuroGage®, commercially available software that is based on NeuroQuant® and extends its utility in several ways. Studies using these and similar methods have found that most patients with chronic mild or moderate traumatic brain injury have brain volume abnormalities, and several of these studies found-surprisingly-more abnormal enlargement than atrophy. More generally, 102 peer-reviewed studies have supported the reliability and validity of NeuroQuant® and NeuroGage®. Furthermore, this updated version of a previous review addresses whether NeuroQuant® and NeuroGage® meet the Daubert standard for admissibility in court. It concludes that NeuroQuant® and NeuroGage® meet the Daubert standard based on their reliability, validity, and objectivity. Due to the improvements in technology over the years, these brain volumetric techniques are practical and readily available for clinical or forensic use, and thus they are important tools for detecting signs of brain injury.
Collapse
Affiliation(s)
- David E. Ross
- Virginia Institute of Neuropsychiatry, Midlothian, VA, United States
- NeuroGage LLC, Midlothian, VA, United States
- Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, United States
| | - John Seabaugh
- Virginia Institute of Neuropsychiatry, Midlothian, VA, United States
- NeuroGage LLC, Midlothian, VA, United States
- Department of Radiology, St. Mary’s Hospital School of Medical Imaging, Richmond, VA, United States
| | - Jan M. Seabaugh
- Virginia Institute of Neuropsychiatry, Midlothian, VA, United States
- NeuroGage LLC, Midlothian, VA, United States
| | - Justis Barcelona
- Virginia Institute of Neuropsychiatry, Midlothian, VA, United States
- NeuroGage LLC, Midlothian, VA, United States
| | - Daniel Seabaugh
- Virginia Institute of Neuropsychiatry, Midlothian, VA, United States
- NeuroGage LLC, Midlothian, VA, United States
| | - Katherine Wright
- Virginia Institute of Neuropsychiatry, Midlothian, VA, United States
- NeuroGage LLC, Midlothian, VA, United States
- Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, United States
| | - Lee Norwind
- Karp, Wigodsky, Norwind, Kudel & Gold, P.A., Rockville, MD, United States
| | - Zachary King
- Karp, Wigodsky, Norwind, Kudel & Gold, P.A., Rockville, MD, United States
| | | | - Joseph Baker
- Virginia Institute of Neuropsychiatry, Midlothian, VA, United States
- NeuroGage LLC, Midlothian, VA, United States
- Department of Neuroscience, Christopher Newport University, Newport News, VA, United States
| | - Tanner Lewis
- Virginia Institute of Neuropsychiatry, Midlothian, VA, United States
- NeuroGage LLC, Midlothian, VA, United States
- Department of Undergraduate Studies, University of Virginia, Charlottesville, VA, United States
| |
Collapse
|
17
|
Relationship between cerebrospinal fluid neurodegeneration biomarkers and temporal brain atrophy in cognitively healthy older adults. Neurobiol Aging 2022; 116:80-91. [DOI: 10.1016/j.neurobiolaging.2022.04.010] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Revised: 04/05/2022] [Accepted: 04/14/2022] [Indexed: 12/30/2022]
|
18
|
Raikes AC, Hernandez GD, Matthews DC, Lukic AS, Law M, Shi Y, Schneider LS, Brinton RD. Exploratory imaging outcomes of a phase 1b/2a clinical trial of allopregnanolone as a regenerative therapeutic for Alzheimer's disease: Structural effects and functional connectivity outcomes. ALZHEIMER'S & DEMENTIA (NEW YORK, N. Y.) 2022; 8:e12258. [PMID: 35310526 PMCID: PMC8919249 DOI: 10.1002/trc2.12258] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Revised: 12/18/2021] [Accepted: 12/21/2021] [Indexed: 01/14/2023]
Abstract
Introduction Allopregnanolone (ALLO), an endogenous neurosteroid, promoted neurogenesis and oligogenesis and restored cognitive function in animal models of Alzheimer's disease (AD). Based on these discovery research findings, we conducted a randomized-controlled phase 1b/2a multiple ascending dose trial of ALLO in persons with early AD (NCT02221622) to assess safety, tolerability, and pharmacokinetics. Exploratory imaging outcomes to determine whether ALLO impacted hippocampal structure, white matter integrity, and functional connectivity are reported. Methods Twenty-four individuals participated in the trial (n = 6 placebo; n = 18 ALLO) and underwent brain magnetic resonance imaging (MRI) before and after 12 weeks of treatment. Hippocampal atrophy rate was determined from volumetric MRI, computed as rate of change, and qualitatively assessed between ALLO and placebo sex, apolipoprotein E (APOE) ε4 allele, and ALLO dose subgroups. White matter microstructural integrity was compared between placebo and ALLO using fractional and quantitative anisotropy (QA). Changes in local, inter-regional, and network-level functional connectivity were also compared between groups using resting-state functional MRI. Results Rate of decline in hippocampal volume was slowed, and in some cases reversed, in the ALLO group compared to placebo. Gain of hippocampal volume was evident in APOE ε4 carriers (range: 0.6% to 7.8% increased hippocampal volume). Multiple measures of white matter integrity indicated evidence of preserved or improved integrity. ALLO significantly increased fractional anisotropy (FA) in 690 of 690 and QA in 1416 of 1888 fiber tracts, located primarily in the corpus callosum, bilateral thalamic radiations, and bilateral corticospinal tracts. Consistent with structural changes, ALLO strengthened local, inter-regional, and network level functional connectivity in AD-vulnerable regions, including the precuneus and posterior cingulate, and network connections between the default mode network and limbic system. Discussion Indicators of regeneration from previous preclinical studies and these exploratory MRI-based outcomes from this phase 1b/2a clinical cohort support advancement to a phase 2 proof-of-concept efficacy clinical trial of ALLO as a regenerative therapeutic for mild AD (REGEN-BRAIN study; NCT04838301).
Collapse
Affiliation(s)
- Adam C. Raikes
- Center for Innovation in Brain ScienceUniversity of ArizonaTucsonArizonaUSA
| | | | - Dawn C. Matthews
- Departments of Pharmacology and Neurology, College of MedicineADM DiagnosticsNorthbrookIllinoisUSA
| | - Ana S. Lukic
- Departments of Pharmacology and Neurology, College of MedicineADM DiagnosticsNorthbrookIllinoisUSA
| | - Meng Law
- Department of RadiologyAlfred HealthDepartment of Neuroscience and Computer Systems EngineeringMonash UniversityMelbourneAustralia
| | - Yonggang Shi
- Stevens Neuroimaging and Informatics InstituteKeck School of MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Lon S. Schneider
- Keck School of MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Roberta D. Brinton
- Center for Innovation in Brain ScienceUniversity of ArizonaTucsonArizonaUSA
| |
Collapse
|
19
|
Ranson JM, Rittman T, Hayat S, Brayne C, Jessen F, Blennow K, van Duijn C, Barkhof F, Tang E, Mummery CJ, Stephan BCM, Altomare D, Frisoni GB, Ribaldi F, Molinuevo JL, Scheltens P, Llewellyn DJ. Modifiable risk factors for dementia and dementia risk profiling. A user manual for Brain Health Services-part 2 of 6. Alzheimers Res Ther 2021; 13:169. [PMID: 34635138 PMCID: PMC8507172 DOI: 10.1186/s13195-021-00895-4] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Accepted: 08/26/2021] [Indexed: 12/14/2022]
Abstract
We envisage the development of new Brain Health Services to achieve primary and secondary dementia prevention. These services will complement existing memory clinics by targeting cognitively unimpaired individuals, where the focus is on risk profiling and personalized risk reduction interventions rather than diagnosing and treating late-stage disease. In this article, we review key potentially modifiable risk factors and genetic risk factors and discuss assessment of risk factors as well as additional fluid and imaging biomarkers that may enhance risk profiling. We then outline multidomain measures and risk profiling and provide practical guidelines for Brain Health Services, with consideration of outstanding uncertainties and challenges. Users of Brain Health Services should undergo risk profiling tailored to their age, level of risk, and availability of local resources. Initial risk assessment should incorporate a multidomain risk profiling measure. For users aged 39-64, we recommend the Cardiovascular Risk Factors, Aging, and Incidence of Dementia (CAIDE) Dementia Risk Score, whereas for users aged 65 and older, we recommend the Brief Dementia Screening Indicator (BDSI) and the Australian National University Alzheimer's Disease Risk Index (ANU-ADRI). The initial assessment should also include potentially modifiable risk factors including sociodemographic, lifestyle, and health factors. If resources allow, apolipoprotein E ɛ4 status testing and structural magnetic resonance imaging should be conducted. If this initial assessment indicates a low dementia risk, then low intensity interventions can be implemented. If the user has a high dementia risk, additional investigations should be considered if local resources allow. Common variant polygenic risk of late-onset AD can be tested in middle-aged or older adults. Rare variants should only be investigated in users with a family history of early-onset dementia in a first degree relative. Advanced imaging with 18-fluorodeoxyglucose positron emission tomography (FDG-PET) or amyloid PET may be informative in high risk users to clarify the nature and burden of their underlying pathologies. Cerebrospinal fluid biomarkers are not recommended for this setting, and blood-based biomarkers need further validation before clinical use. As new technologies become available, advances in artificial intelligence are likely to improve our ability to combine diverse data to further enhance risk profiling. Ultimately, Brain Health Services have the potential to reduce the future burden of dementia through risk profiling, risk communication, personalized risk reduction, and cognitive enhancement interventions.
Collapse
Affiliation(s)
- Janice M Ranson
- College of Medicine and Health, University of Exeter, Exeter, UK
- Deep Dementia Phenotyping (DEMON) Network, Exeter, UK
| | - Timothy Rittman
- Deep Dementia Phenotyping (DEMON) Network, Exeter, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Shabina Hayat
- Department of Public Health and Primary Care, Cambridge Public Health, University of Cambridge, Cambridge, UK
| | - Carol Brayne
- Department of Public Health and Primary Care, Cambridge Public Health, University of Cambridge, Cambridge, UK
| | - Frank Jessen
- Department of Psychiatry and Psychotherapy, Medical Faculty, University of Cologne, Cologne, Germany
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience & Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden; Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Cornelia van Duijn
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Frederik Barkhof
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, UK
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, Amsterdam, The Netherlands
| | - Eugene Tang
- Deep Dementia Phenotyping (DEMON) Network, Exeter, UK
- Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Catherine J Mummery
- Deep Dementia Phenotyping (DEMON) Network, Exeter, UK
- Dementia Research Centre, Institute of Neurology, University College London, and National Hospital for Neurology and Neurosurgery, University College London Hospital, London, UK
| | - Blossom C M Stephan
- Institute of Mental Health, Division of Psychiatry and Applied Psychology, School of Medicine, Nottingham University, Nottingham, UK
| | - Daniele Altomare
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Memory Clinic, Geneva University Hospitals, Geneva, Switzerland
| | - Giovanni B Frisoni
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Memory Clinic, Geneva University Hospitals, Geneva, Switzerland
| | - Federica Ribaldi
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Memory Clinic, Geneva University Hospitals, Geneva, Switzerland
- Laboratory of Alzheimer's Neuroimaging and Epidemiology (LANE), Saint John of God Clinical Research Centre, Brescia, Italy
- Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | - José Luis Molinuevo
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
| | - Philip Scheltens
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Life Science Partners, Amsterdam, The Netherlands
| | - David J Llewellyn
- College of Medicine and Health, University of Exeter, Exeter, UK.
- Deep Dementia Phenotyping (DEMON) Network, Exeter, UK.
- Alan Turing Institute, London, UK.
- 2.04 College House, St Luke's Campus, University of Exeter Medical School, Exeter, EX1 2 LU, UK.
| |
Collapse
|
20
|
Düzel E, Costagli M, Donatelli G, Speck O, Cosottini M. Studying Alzheimer disease, Parkinson disease, and amyotrophic lateral sclerosis with 7-T magnetic resonance. Eur Radiol Exp 2021; 5:36. [PMID: 34435242 PMCID: PMC8387546 DOI: 10.1186/s41747-021-00221-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Accepted: 04/07/2021] [Indexed: 12/18/2022] Open
Abstract
Ultra-high-field (UHF) magnetic resonance (MR) scanners, that is, equipment operating at static magnetic field of 7 tesla (7 T) and above, enable the acquisition of data with greatly improved signal-to-noise ratio with respect to conventional MR systems (e.g., scanners operating at 1.5 T and 3 T). The change in tissue relaxation times at UHF offers the opportunity to improve tissue contrast and depict features that were previously inaccessible. These potential advantages come, however, at a cost: in the majority of UHF-MR clinical protocols, potential drawbacks may include signal inhomogeneity, geometrical distortions, artifacts introduced by patient respiration, cardiac cycle, and motion. This article reviews the 7 T MR literature reporting the recent studies on the most widespread neurodegenerative diseases: Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis.
Collapse
Affiliation(s)
- Emrah Düzel
- Otto-von-Guericke University Magdeburg, Magdeburg, Germany. .,German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany. .,University College London, London, UK.
| | - Mauro Costagli
- IRCCS Stella Maris, Pisa, Italy.,University of Genoa, Genova, Italy
| | - Graziella Donatelli
- Fondazione Imago 7, Pisa, Italy.,Azienda Ospedaliero Universitaria Pisana, Pisa, Italy
| | - Oliver Speck
- Otto-von-Guericke University Magdeburg, Magdeburg, Germany.,German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Mirco Cosottini
- Azienda Ospedaliero Universitaria Pisana, Pisa, Italy.,University of Pisa, Pisa, Italy
| |
Collapse
|
21
|
Wong R, Luo Y, Mok VCT, Shi L. Advances in computerized MRI‐based biomarkers in Alzheimer’s disease. BRAIN SCIENCE ADVANCES 2021. [DOI: 10.26599/bsa.2021.9050005] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
The use of neuroimaging examinations is crucial in Alzheimer’s disease (AD), in both research and clinical settings. Over the years, magnetic resonance imaging (MRI)–based computer‐aided diagnosis has been shown to be helpful for early screening and predicting cognitive decline. Meanwhile, an increasing number of studies have adopted machine learning for the classification of AD, with promising results. In this review article, we focus on computerized MRI‐based biomarkers of AD by reviewing representative studies that used computerized techniques to identify AD patients and predict cognitive progression. We categorized these studies based on the following applications: (1) identifying AD from normal control; (2) identifying AD from other dementia types, including vascular dementia, dementia with Lewy bodies, and frontotemporal dementia; and (3) predicting conversion from NC to mild cognitive impairment (MCI) and from MCI to AD. This systematic review could act as a state‐of‐the‐art overview of this emerging field as well as a basis for designing future studies.
Collapse
Affiliation(s)
- Raymond Wong
- BrainNow Research Institute, Shenzhen 518081, Guangdong, China
| | - Yishan Luo
- BrainNow Research Institute, Shenzhen 518081, Guangdong, China
| | - Vincent Chung-tong Mok
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong 999077, China
| | - Lin Shi
- BrainNow Research Institute, Shenzhen 518081, Guangdong, China
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong 999077, China
| |
Collapse
|
22
|
Schwarz AJ. The Use, Standardization, and Interpretation of Brain Imaging Data in Clinical Trials of Neurodegenerative Disorders. Neurotherapeutics 2021; 18:686-708. [PMID: 33846962 PMCID: PMC8423963 DOI: 10.1007/s13311-021-01027-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/15/2021] [Indexed: 12/11/2022] Open
Abstract
Imaging biomarkers play a wide-ranging role in clinical trials for neurological disorders. This includes selecting the appropriate trial participants, establishing target engagement and mechanism-related pharmacodynamic effect, monitoring safety, and providing evidence of disease modification. In the early stages of clinical drug development, evidence of target engagement and/or downstream pharmacodynamic effect-especially with a clear relationship to dose-can provide confidence that the therapeutic candidate should be advanced to larger and more expensive trials, and can inform the selection of the dose(s) to be further tested, i.e., to "de-risk" the drug development program. In these later-phase trials, evidence that the therapeutic candidate is altering disease-related biomarkers can provide important evidence that the clinical benefit of the compound (if observed) is grounded in meaningful biological changes. The interpretation of disease-related imaging markers, and comparability across different trials and imaging tools, is greatly improved when standardized outcome measures are defined. This standardization should not impinge on scientific advances in the imaging tools per se but provides a common language in which the results generated by these tools are expressed. PET markers of pathological protein aggregates and structural imaging of brain atrophy are common disease-related elements across many neurological disorders. However, PET tracers for pathologies beyond amyloid β and tau are needed, and the interpretability of structural imaging can be enhanced by some simple considerations to guard against the possible confound of pseudo-atrophy. Learnings from much-studied conditions such as Alzheimer's disease and multiple sclerosis will be beneficial as the field embraces rarer diseases.
Collapse
Affiliation(s)
- Adam J Schwarz
- Takeda Pharmaceuticals Ltd., 40 Landsdowne Street, Cambridge, MA, 02139, USA.
| |
Collapse
|
23
|
Balestrieri JVL, Nonato MB, Gheler L, Prandini MN. Structural Volume of Hippocampus and Alzheimer's Disease. ACTA ACUST UNITED AC 2020; 66:512-515. [PMID: 32578788 DOI: 10.1590/1806-9282.66.4.512] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2019] [Accepted: 11/12/2019] [Indexed: 11/22/2022]
Affiliation(s)
| | - Mahara Barbosa Nonato
- . Estudante de Medicina, Faculdade de Medicina do ABC - FMABC, Santo André, SP, Brasil
| | - Larissa Gheler
- . Estudante de Medicina, Centro Universitário de Adamantina (UniFAI), Adamantina, SP, Brasil
| | - Mirto Nelso Prandini
- . Médico e Doutor - Departamento de Neurocirurgia da Universidade Federal de São Paulo, São Paulo, SP, Brasil
| |
Collapse
|
24
|
Sodums DJ, Bohbot VD. Negative correlation between grey matter in the hippocampus and caudate nucleus in healthy aging. Hippocampus 2020; 30:892-908. [PMID: 32384195 DOI: 10.1002/hipo.23210] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2019] [Revised: 04/07/2020] [Accepted: 04/09/2020] [Indexed: 01/18/2023]
Abstract
Neurobiological changes that occur with aging include a reduction in function and volume of the hippocampus. These changes were associated with corresponding memory deficits in navigation tasks. However, navigation can involve different strategies that are dependent on the hippocampus and caudate nucleus. The proportion of people using hippocampus-dependent spatial strategies decreases across the lifespan. As such, the decrease in spatial strategies, and corresponding increase in caudate nucleus-dependent response strategies with age, may play a role in the observed neurobiological changes in the hippocampus. Furthermore, we previously showed a negative correlation between grey matter in the hippocampus and caudate nucleus/striatum in mice, young adults, and in individuals diagnosed with Alzheimer's disease. As such, we hypothesized that this negative relationship between the two structures would be present during normal aging. The aim of the current study was to investigate this gap in the literature by studying the relationship between grey matter in the hippocampus and caudate nucleus of the striatum, in relation to each other and to navigation strategies, during healthy aging. Healthy older adults (N = 39) were tested on the Concurrent Spatial Discrimination Learning Task (CSDLT), a virtual radial task that dissociates between spatial and response strategies. A regression of strategies against structural MRIs showed for the first time in older adults that the response strategy was associated with higher amounts of grey matter in the caudate nucleus. As expected, the spatial strategy correlated with grey matter in the hippocampus, which was negatively correlated with grey matter in the caudate nucleus. Interestingly, a sex difference emerged showing that among older adult response learners, women have the least amount of grey matter in the hippocampus, which is a known risk for Alzheimer's disease. This difference was absent among spatial learners. These results are discussed in the context of the putative protective role of spatial memory against grey matter loss in the hippocampus, especially in women.
Collapse
Affiliation(s)
- Devin J Sodums
- Department of Psychiatry, McGill University, Douglas Mental Health University Institute, Montreal, Quebec, Canada
| | - Véronique D Bohbot
- Department of Psychiatry, McGill University, Douglas Mental Health University Institute, Montreal, Quebec, Canada
| |
Collapse
|
25
|
Young PNE, Estarellas M, Coomans E, Srikrishna M, Beaumont H, Maass A, Venkataraman AV, Lissaman R, Jiménez D, Betts MJ, McGlinchey E, Berron D, O'Connor A, Fox NC, Pereira JB, Jagust W, Carter SF, Paterson RW, Schöll M. Imaging biomarkers in neurodegeneration: current and future practices. Alzheimers Res Ther 2020; 12:49. [PMID: 32340618 PMCID: PMC7187531 DOI: 10.1186/s13195-020-00612-7] [Citation(s) in RCA: 79] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2019] [Accepted: 04/01/2020] [Indexed: 12/12/2022]
Abstract
There is an increasing role for biological markers (biomarkers) in the understanding and diagnosis of neurodegenerative disorders. The application of imaging biomarkers specifically for the in vivo investigation of neurodegenerative disorders has increased substantially over the past decades and continues to provide further benefits both to the diagnosis and understanding of these diseases. This review forms part of a series of articles which stem from the University College London/University of Gothenburg course "Biomarkers in neurodegenerative diseases". In this review, we focus on neuroimaging, specifically positron emission tomography (PET) and magnetic resonance imaging (MRI), giving an overview of the current established practices clinically and in research as well as new techniques being developed. We will also discuss the use of machine learning (ML) techniques within these fields to provide additional insights to early diagnosis and multimodal analysis.
Collapse
Affiliation(s)
- Peter N E Young
- Wallenberg Centre for Molecular and Translational Medicine and the Department of Psychiatry and Neurochemistry, University of Gothenburg, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Mar Estarellas
- Centre for Medical Image Computing (CMIC), Department of Computer Science & Department of Medical Physics and Biomedical Engineering, University College London, London, UK
| | - Emma Coomans
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, Netherlands
| | - Meera Srikrishna
- Wallenberg Centre for Molecular and Translational Medicine and the Department of Psychiatry and Neurochemistry, University of Gothenburg, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Helen Beaumont
- Neuroscience and Aphasia Research Unit, Division of Neuroscience and Experimental Psychology, The University of Manchester, Manchester, UK
| | - Anne Maass
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Ashwin V Venkataraman
- Division of Brain Sciences, Imperial College London, London, UK
- United Kingdom Dementia Research Institute, Imperial College London, London, UK
| | - Rikki Lissaman
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff, UK
| | - Daniel Jiménez
- Dementia Research Centre, UCL Institute of Neurology, University College London, London, UK
- Department of Neurological Sciences, Faculty of Medicine, University of Chile, Santiago, Chile
| | - Matthew J Betts
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
- Institute of Cognitive Neurology and Dementia Research, Otto-von-Guericke-University Magdeburg, Magdeburg, Germany
| | | | - David Berron
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Antoinette O'Connor
- Dementia Research Centre, UCL Institute of Neurology, University College London, London, UK
| | - Nick C Fox
- Dementia Research Centre, UCL Institute of Neurology, University College London, London, UK
| | - Joana B Pereira
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - William Jagust
- Helen Wills Neuroscience Institute, University of California, Berkeley, USA
- Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Stephen F Carter
- Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK
- Wolfson Molecular Imaging Centre, Division of Neuroscience and Experimental Psychology, MAHSC, University of Manchester, Manchester, UK
| | - Ross W Paterson
- Dementia Research Centre, UCL Institute of Neurology, University College London, London, UK
| | - Michael Schöll
- Wallenberg Centre for Molecular and Translational Medicine and the Department of Psychiatry and Neurochemistry, University of Gothenburg, Sahlgrenska University Hospital, Gothenburg, Sweden.
- Dementia Research Centre, UCL Institute of Neurology, University College London, London, UK.
- Department of Clinical Physiology, Sahlgrenska University Hospital, Gothenburg, Sweden.
| |
Collapse
|
26
|
Dawe RJ, Yu L, Arfanakis K, Schneider JA, Bennett DA, Boyle PA. Late-life cognitive decline is associated with hippocampal volume, above and beyond its associations with traditional neuropathologic indices. Alzheimers Dement 2020; 16:209-218. [PMID: 31914231 PMCID: PMC6953608 DOI: 10.1002/alz.12009] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2019] [Revised: 07/12/2019] [Accepted: 11/01/2019] [Indexed: 01/18/2023]
Abstract
INTRODUCTION Reduced hippocampal volume is associated with late-life cognitive decline, but prior studies have not determined whether this association persists after accounting for Alzheimer's disease (AD) and other neuropathologies. METHODS Participants were 531 deceased older adults from community-based cohort studies of aging who had undergone annual cognitive evaluations. At death, brain tissue underwent neuropathologic examination and magnetic resonance imaging (MRI). Linear mixed models examined whether hippocampal volume measured via MRI accounted for variation in decline rate of global cognition and five cognitive domains, above and beyond neuropathologic indices. RESULTS Demographics and indices of AD, cerebrovascular disease, Lewy body disease, hippocampal sclerosis, TDP-43, and atherosclerosis accounted for 42.6% of the variation in global cognitive decline. Hippocampal volume accounted for an additional 5.4% of this variation and made similar contributions in four of the five cognitive domains. DISCUSSION Hippocampal volume is associated with late-life cognitive decline, above and beyond contributions from common neuropathologic indices.
Collapse
Affiliation(s)
- Robert J. Dawe
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL, USA
- Department of Diagnostic Radiology and Nuclear Medicine, Rush University Medical Center, Chicago, IL, USA
| | - Lei Yu
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL, USA
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, USA
| | - Konstantinos Arfanakis
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL, USA
- Department of Diagnostic Radiology and Nuclear Medicine, Rush University Medical Center, Chicago, IL, USA
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Julie A. Schneider
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL, USA
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, USA
- Department of Pathology, Rush University Medical Center, Chicago, IL, USA
| | - David A. Bennett
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL, USA
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, USA
| | - Patricia A. Boyle
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL, USA
- Department of Behavioral Sciences, Rush University Medical Center, Chicago, IL, USA
| |
Collapse
|
27
|
Krajcovicova L, Klobusiakova P, Rektorova I. Gray Matter Changes in Parkinson's and Alzheimer's Disease and Relation to Cognition. Curr Neurol Neurosci Rep 2019; 19:85. [PMID: 31720859 PMCID: PMC6854046 DOI: 10.1007/s11910-019-1006-z] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
PURPOSE OF REVIEW We summarize structural (s)MRI findings of gray matter (GM) atrophy related to cognitive impairment in Alzheimer's disease (AD) and Parkinson's disease (PD) in light of new analytical approaches and recent longitudinal studies results. RECENT FINDINGS The hippocampus-to-cortex ratio seems to be the best sMRI biomarker to discriminate between various AD subtypes, following the spatial distribution of tau pathology, and predict rate of cognitive decline. PD is clinically far more variable than AD, with heterogeneous underlying brain pathology. Novel multivariate approaches have been used to describe patterns of early subcortical and cortical changes that relate to more malignant courses of PD. New emerging analytical approaches that combine structural MRI data with clinical and other biomarker outcomes hold promise for detecting specific GM changes in the early stages of PD and preclinical AD that may predict mild cognitive impairment and dementia conversion.
Collapse
Affiliation(s)
- Lenka Krajcovicova
- Applied Neuroscience Research Group, CEITEC, Masaryk University, Kamenice 5, Brno, Czech Republic
- First Department of Neurology, Faculty of Medicine, St. Anne's University Hospital, Masaryk University, Pekarska 53, Brno, Czech Republic
| | - Patricia Klobusiakova
- Applied Neuroscience Research Group, CEITEC, Masaryk University, Kamenice 5, Brno, Czech Republic
- Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Irena Rektorova
- Applied Neuroscience Research Group, CEITEC, Masaryk University, Kamenice 5, Brno, Czech Republic.
- First Department of Neurology, Faculty of Medicine, St. Anne's University Hospital, Masaryk University, Pekarska 53, Brno, Czech Republic.
| |
Collapse
|
28
|
New Frontiers in Parkinson's Disease: From Genetics to the Clinic. J Neurosci 2019; 38:9375-9382. [PMID: 30381429 DOI: 10.1523/jneurosci.1666-18.2018] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2018] [Revised: 09/15/2018] [Accepted: 09/18/2018] [Indexed: 12/30/2022] Open
Abstract
The greatest unmet therapeutic need in Parkinson's disease (PD) is a treatment that slows the relentless progression of the symptoms and the neurodegenerative process. This review highlights the utility of genetics to understand the pathogenic mechanisms and develop novel therapeutic approaches for PD. The focus is on strategies provided by genetic studies: notably via the reduction and clearance of α-synuclein, inhibition of LRRK2 kinase activity, and modulation of glucocerebrosidase-related substrates. In addition, the critical role of precompetitive public-private partnerships in supporting trial design optimization, overall drug development, and regulatory approvals is illustrated. With these great advances, the promise of developing transformative therapies that halt or slow disease progression is a tangible goal.
Collapse
|
29
|
Multimodal Hippocampal Subfield Grading For Alzheimer's Disease Classification. Sci Rep 2019; 9:13845. [PMID: 31554909 PMCID: PMC6761169 DOI: 10.1038/s41598-019-49970-9] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2018] [Accepted: 08/09/2019] [Indexed: 01/23/2023] Open
Abstract
Numerous studies have proposed biomarkers based on magnetic resonance imaging (MRI) to detect and predict the risk of evolution toward Alzheimer's disease (AD). Most of these methods have focused on the hippocampus, which is known to be one of the earliest structures impacted by the disease. To date, patch-based grading approaches provide among the best biomarkers based on the hippocampus. However, this structure is complex and is divided into different subfields, not equally impacted by AD. Former in-vivo imaging studies mainly investigated structural alterations of these subfields using volumetric measurements and microstructural modifications with mean diffusivity measurements. The aim of our work is to improve the current classification performances based on the hippocampus with a new multimodal patch-based framework combining structural and diffusivity MRI. The combination of these two MRI modalities enables the capture of subtle structural and microstructural alterations. Moreover, we propose to study the efficiency of this new framework applied to the hippocampal subfields. To this end, we compare the classification accuracy provided by the different hippocampal subfields using volume, mean diffusivity, and our novel multimodal patch-based grading framework combining structural and diffusion MRI. The experiments conducted in this work show that our new multimodal patch-based method applied to the whole hippocampus provides the most discriminating biomarker for advanced AD detection while our new framework applied into subiculum obtains the best results for AD prediction, improving by two percentage points the accuracy compared to the whole hippocampus.
Collapse
|
30
|
Bartel F, Visser M, de Ruiter M, Belderbos J, Barkhof F, Vrenken H, de Munck JC, van Herk M. Non-linear registration improves statistical power to detect hippocampal atrophy in aging and dementia. Neuroimage Clin 2019; 23:101902. [PMID: 31233953 PMCID: PMC6595082 DOI: 10.1016/j.nicl.2019.101902] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2018] [Revised: 05/01/2019] [Accepted: 06/16/2019] [Indexed: 12/25/2022]
Abstract
OBJECTIVE To compare the performance of different methods for determining hippocampal atrophy rates using longitudinal MRI scans in aging and Alzheimer's disease (AD). BACKGROUND Quantifying hippocampal atrophy caused by neurodegenerative diseases is important to follow the course of the disease. In dementia, the efficacy of new therapies can be partially assessed by measuring their effect on hippocampal atrophy. In radiotherapy, the quantification of radiation-induced hippocampal volume loss is of interest to quantify radiation damage. We evaluated plausibility, reproducibility and sensitivity of eight commonly used methods to determine hippocampal atrophy rates using test-retest scans. MATERIALS AND METHODS Manual, FSL-FIRST, FreeSurfer, multi-atlas segmentation (MALF) and non-linear registration methods (Elastix, NiftyReg, ANTs and MIRTK) were used to determine hippocampal atrophy rates on longitudinal T1-weighted MRI from the ADNI database. Appropriate parameters for the non-linear registration methods were determined using a small training dataset (N = 16) in which two-year hippocampal atrophy was measured using test-retest scans of 8 subjects with low and 8 subjects with high atrophy rates. On a larger dataset of 20 controls, 40 mild cognitive impairment (MCI) and 20 AD patients, one-year hippocampal atrophy rates were measured. A repeated measures ANOVA analysis was performed to determine differences between controls, MCI and AD patients. For each method we calculated effect sizes and the required sample sizes to detect one-year volume change between controls and MCI (NCTRL_MCI) and between controls and AD (NCTRL_AD). Finally, reproducibility of hippocampal atrophy rates was assessed using within-session rescans and expressed as an average distance measure DAve, which expresses the difference in atrophy rate, averaged over all subjects. The same DAve was used to determine the agreement between different methods. RESULTS Except for MALF, all methods detected a significant group difference between CTRL and AD, but none could find a significant difference between the CTRL and MCI. FreeSurfer and MIRTK required the lowest sample sizes (FreeSurfer: NCTRL_MCI = 115, NCTRL_AD = 17 with DAve = 3.26%; MIRTK: NCTRL_MCI = 97, NCTRL_AD = 11 with DAve = 3.76%), while ANTs was most reproducible (NCTRL_MCI = 162, NCTRL_AD = 37 with DAve = 1.06%), followed by Elastix (NCTRL_MCI = 226, NCTRL_AD = 15 with DAve = 1.78%) and NiftyReg (NCTRL_MCI = 193, NCTRL_AD = 14 with DAve = 2.11%). Manually measured hippocampal atrophy rates required largest sample sizes to detect volume change and were poorly reproduced (NCTRL_MCI = 452, NCTRL_AD = 87 with DAve = 12.39%). Atrophy rates of non-linear registration methods also agreed best with each other. DISCUSSION AND CONCLUSION Non-linear registration methods were most consistent in determining hippocampal atrophy and because of their better reproducibility, methods, such as ANTs, Elastix and NiftyReg, are preferred for determining hippocampal atrophy rates on longitudinal MRI. Since performances of non-linear registration methods are well comparable, the preferred method would mostly depend on computational efficiency.
Collapse
Affiliation(s)
- F Bartel
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, the Netherlands.
| | - M Visser
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, the Netherlands
| | - M de Ruiter
- Division of Psychosocial Research and Epidemiology, Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - J Belderbos
- Department of Radiotherapy, Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - F Barkhof
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, the Netherlands; UCL institutes of Neurology and healthcare engineering, London, United Kingdom
| | - H Vrenken
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, the Netherlands
| | - J C de Munck
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, the Netherlands
| | - M van Herk
- Manchester Cancer Research Centre, Division of Cancer Science, School of Medical Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Sciences Centre, Manchester, United Kingdom
| |
Collapse
|
31
|
Abrigo J, Shi L, Luo Y, Chen Q, Chu WCW, Mok VCT. Standardization of hippocampus volumetry using automated brain structure volumetry tool for an initial Alzheimer's disease imaging biomarker. Acta Radiol 2019; 60:769-776. [PMID: 30185071 DOI: 10.1177/0284185118795327] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND One significant barrier to incorporate Alzheimer's disease (AD) imaging biomarkers into diagnostic criteria is the lack of standardized methods for biomarker quantification. The European Alzheimer's Disease Consortium-Alzheimer's Disease Neuroimaging Initiative (EADC-ADNI) Harmonization Protocol project provides the most authoritative guideline for hippocampal definition and has produced a manually segmented reference dataset for validation of automated methods. PURPOSE To validate automated hippocampal volumetry using AccuBrain™, against the EADC-ADNI dataset, and assess its diagnostic performance for differentiating AD and normal aging in an independent cohort. MATERIAL AND METHODS The EADC-ADNI reference dataset comprise of manually segmented hippocampal labels from 135 volumetric T1-weighted scans from various scanners. Dice similarity coefficient (DSC), intraclass correlation coefficient (ICC), and Pearson's r were obtained for AccuBrain™ and FreeSurfer. The magnetic resonance imaging (MRI) of a separate cohort of 299 individuals (150 normal controls, 149 with AD) were obtained from the ADNI database and processed with AccuBrain™ to assess its diagnostic accuracy. Area under the curve (AUC) for total hippocampal volumes (HV) and hippocampal fraction (HF) were determined. RESULTS Compared with EADC-ADNI dataset ground truths, AccuBrain™ had a mean DSC of 0.89/0.89/0.89, ICC of 0.94/0.96/0.95, and r of 0.95/0.96/0.95 for right/left/total HV. AccuBrain™ HV and HF had AUC of 0.76 and 0.80, respectively. Thresholds of ≤ 5.71 mL and ≤ 0.38% afforded 80% sensitivity for AD detection. CONCLUSION AccuBrain™ provides accurate automated hippocampus segmentation in accordance with the EADC-ADNI standard, with great potential value in assisting clinical diagnosis of AD.
Collapse
Affiliation(s)
- Jill Abrigo
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Lin Shi
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong SAR, China
- Chow Yuk Ho Technology Centre for Innovative Medicine, Therese Pei Fong Chow Research Center for Prevention of Dementia, Lui Che Woo Institute of Innovative Medicine, Gerald Choa Neuroscience Center, The Chinese University of Hong Kong, Hong Kong SAR, China
- BrainNow Medical Technology Limited, Hong Kong Science and Technology Park, Hong Kong SAR, China
| | - Yishan Luo
- BrainNow Medical Technology Limited, Hong Kong Science and Technology Park, Hong Kong SAR, China
| | - Qianyun Chen
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Winnie Chiu Wing Chu
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Vincent Chung Tong Mok
- Chow Yuk Ho Technology Centre for Innovative Medicine, Therese Pei Fong Chow Research Center for Prevention of Dementia, Lui Che Woo Institute of Innovative Medicine, Gerald Choa Neuroscience Center, The Chinese University of Hong Kong, Hong Kong SAR, China
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, China
| | | |
Collapse
|
32
|
Huang Y, Xu J, Zhou Y, Tong T, Zhuang X. Diagnosis of Alzheimer's Disease via Multi-Modality 3D Convolutional Neural Network. Front Neurosci 2019; 13:509. [PMID: 31213967 PMCID: PMC6555226 DOI: 10.3389/fnins.2019.00509] [Citation(s) in RCA: 85] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2019] [Accepted: 05/02/2019] [Indexed: 01/28/2023] Open
Abstract
Alzheimer's disease (AD) is one of the most common neurodegenerative diseases. In the last decade, studies on AD diagnosis has attached great significance to artificial intelligence-based diagnostic algorithms. Among the diverse modalities of imaging data, T1-weighted MR and FDG-PET are widely used for this task. In this paper, we propose a convolutional neural network (CNN) to integrate all the multi-modality information included in both T1-MR and FDG-PET images of the hippocampal area, for the diagnosis of AD. Different from the traditional machine learning algorithms, this method does not require manually extracted features, instead, it utilizes 3D image-processing CNNs to learn features for the diagnosis or prognosis of AD. To test the performance of the proposed network, we trained the classifier with paired T1-MR and FDG-PET images in the ADNI datasets, including 731 cognitively unimpaired (labeled as CN) subjects, 647 subjects with AD, 441 subjects with stable mild cognitive impairment (sMCI) and 326 subjects with progressive mild cognitive impairment (pMCI). We obtained higher accuracies of 90.10% for CN vs. AD task, 87.46% for CN vs. pMCI task, and 76.90% for sMCI vs. pMCI task. The proposed framework yields a state-of-the-art performance. Finally, the results have demonstrated that (1) segmentation is not a prerequisite when using a CNN for the classification, (2) the combination of two modality imaging data generates better results.
Collapse
Affiliation(s)
- Yechong Huang
- School of Data Science, Fudan University, Shanghai, China
| | - Jiahang Xu
- School of Data Science, Fudan University, Shanghai, China
| | - Yuncheng Zhou
- School of Data Science, Fudan University, Shanghai, China
| | - Tong Tong
- Fujian Provincial Key Laboratory of Medical Instrument and Pharmaceutical Technology, Fuzhou, China
| | - Xiahai Zhuang
- School of Data Science, Fudan University, Shanghai, China
| | | |
Collapse
|
33
|
Marizzoni M, Ferrari C, Jovicich J, Albani D, Babiloni C, Cavaliere L, Didic M, Forloni G, Galluzzi S, Hoffmann KT, Molinuevo JL, Nobili F, Parnetti L, Payoux P, Ribaldi F, Rossini PM, Schönknecht P, Salvatore M, Soricelli A, Hensch T, Tsolaki M, Visser PJ, Wiltfang J, Richardson JC, Bordet R, Blin O, Frisoni GB. Predicting and Tracking Short Term Disease Progression in Amnestic Mild Cognitive Impairment Patients with Prodromal Alzheimer’s Disease: Structural Brain Biomarkers. J Alzheimers Dis 2019; 69:3-14. [DOI: 10.3233/jad-180152] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Affiliation(s)
- Moira Marizzoni
- Laboratory of Neuroimaging and Alzheimer’s Epidemiology, IRCCS Istituto Centro San Giovanni diDio Fatebenefratelli, Brescia, Italy
| | - Clarissa Ferrari
- Unit of Statistics, IRCCS Istituto Centro San Giovanni diDio Fatebenefratelli, Brescia, Italy
| | - Jorge Jovicich
- Center for Mind/Brain Sciences, University of Trento, Italy
| | - Diego Albani
- Neuroscience Department, IRCCS - Istituto di Ricerche Farmacologiche Mario Negri, Milano, Italy
| | - Claudio Babiloni
- Department of Physiology and Pharmacology “V. Erspamer”, Sapienza University of Rome, Rome, Italy
- IRCCS San Raffaele Pisana of Rome, Rome, Italy
| | - Libera Cavaliere
- Laboratory of Neuroimaging and Alzheimer’s Epidemiology, IRCCS Istituto Centro San Giovanni diDio Fatebenefratelli, Brescia, Italy
| | - Mira Didic
- Aix-Marseille Université, Inserm, INS UMR_S 1106, Marseille, France
- APHM, Timone, Service de Neurologie et Neuropsychologie, APHM Hôpital Timone Adultes, Marseille, France
| | - Gianluigi Forloni
- Neuroscience Department, IRCCS - Istituto di Ricerche Farmacologiche Mario Negri, Milano, Italy
| | - Samantha Galluzzi
- Laboratory of Neuroimaging and Alzheimer’s Epidemiology, IRCCS Istituto Centro San Giovanni diDio Fatebenefratelli, Brescia, Italy
| | | | - José Luis Molinuevo
- Alzheimer’s Disease Unit and Other Cognitive Disorders Unit, Hospital Clínic de Barcelona, and Institut d’Investigacions Biomédiques August Pi i Sunyer (IDIBAPS), Barcelona, Catalunya, Spain
| | - Flavio Nobili
- Clinical Neurology, Dept. of Neuroscience (DINOGMI), University of Genoa and IRCCS AOU SanMartino-IST, Genoa, Italy
| | - Lucilla Parnetti
- Clinica Neurologica, Università di Perugia, Ospedale Santa Mariadella Misericordia, Perugia, Italy
| | - Pierre Payoux
- INSERM; Imagerie cérébrale et handicapsneurologiques UMR 825, Toulouse, France
| | - Federica Ribaldi
- Laboratory of Neuroimaging and Alzheimer’s Epidemiology, IRCCS Istituto Centro San Giovanni diDio Fatebenefratelli, Brescia, Italy
- Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | - Paolo Maria Rossini
- Area of Neuroscience, Department of Gerontology, Neurosciences & Orthopedics, Catholic University, Policlinic A. Gemelli Foundation Rome, Italy
| | - Peter Schönknecht
- Department of Psychiatry and Psychotherapy, University of Leipzig, Leipzig, Germany
| | - Marco Salvatore
- SDN Istituto di Ricerca Diagnostica e Nucleare, Napoli, Italy
| | | | - Tilman Hensch
- Department of Psychiatry and Psychotherapy, University of Leipzig, Leipzig, Germany
| | - Magda Tsolaki
- 3rd Neurologic Clinic, Medical School, G. Papanikolaou Hospital, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Pieter Jelle Visser
- Department of Neurology, Alzheimer Centre, VU Medical Centre, Amsterdam, The Netherlands
| | - Jens Wiltfang
- LVR-Hospital Essen, Department of Psychiatry and Psychotherapy, Faculty of Medicine, University of Duisburg-Essen, Essen, Germany
- Department of Psychiatry and Psychotherapy, University Medical Center (UMG), Georg-August-University, Goettingen, Germany
- iBiMED, Medical Sciences Department, University of Aveiro, Aveiro, Portugal
| | - Jill C. Richardson
- Neurosciences Therapeutic Area, GlaxoSmithKline R&D, Gunnels Wood Road, Stevenage, United Kingdom
| | - Régis Bordet
- University of Lille, Inserm, CHU Lille, U1171 - Degenerative and vascular cognitive disorders, Lille, France
| | - Olivier Blin
- Aix Marseille University, UMR-CNRS 7289, Service de Pharmacologie Clinique, AP-HM, Marseille, France
| | - Giovanni B. Frisoni
- Laboratory of Neuroimaging and Alzheimer’s Epidemiology, IRCCS Istituto Centro San Giovanni diDio Fatebenefratelli, Brescia, Italy
- Memory Clinic and LANVIE - Laboratory of Neuroimaging of Aging, University Hospitals and University of Geneva, Geneva, Switzerland
| | | |
Collapse
|
34
|
Prediction of Alzheimer's disease dementia with MRI beyond the short-term: Implications for the design of predictive models. NEUROIMAGE-CLINICAL 2019; 23:101837. [PMID: 31078938 PMCID: PMC6515129 DOI: 10.1016/j.nicl.2019.101837] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/03/2018] [Revised: 02/12/2019] [Accepted: 04/24/2019] [Indexed: 11/22/2022]
Abstract
Magnetic resonance imaging (MRI) volumetric measures have become a standard tool for the detection of incipient Alzheimer's Disease (AD) dementia in mild cognitive impairment (MCI). Focused on providing an earlier and more accurate diagnosis, sophisticated MRI machine learning algorithms have been developed over the recent years, most of them learning their non-disease patterns from MCI that remained stable over 2–3 years. In this work, we analyzed whether these stable MCI over short-term periods are actually appropriate training examples of non-disease patterns. To this aim, we compared the diagnosis of MCI patients at 2 and 5 years of follow-up and investigated its impact on the predictive performance of baseline volumetric MRI measures primarily involved in AD, i.e., hippocampal and entorhinal cortex volumes. Predictive power was evaluated in terms of the area under the ROC curve (AUC), sensitivity, and specificity in a trial sample of 248 MCI patients followed-up over 5 years. We further compared the sensitivity in those MCI that converted before 2 years and those that converted after 2 years. Our results indicate that 23% of the stable MCI at 2 years progressed in the next three years and that MRI volumetric measures are good predictors of conversion to AD dementia even at the mid-term, showing a better specificity and AUC as follow-up time increases. The combination of hippocampus and entorhinal cortex yielded an AUC that was significantly higher for the 5-year follow-up (AUC = 73% at 2 years vs. AUC = 84% at 5 years), as well as for specificity (56% vs. 71%). Sensitivity showed a non-significant slight decrease (81% vs. 78%). Remarkably, the performance of this model was comparable to machine learning models at the same follow-up times. MRI correctly identified most of the patients that converted after 2 years (with sensitivity >60%), and these patients showed a similar degree of abnormalities to those that converted before 2 years. This implies that most of the MCI patients that remained stable over short periods and subsequently progressed to AD dementia had evident atrophies at baseline. Therefore, machine learning models that use these patients to learn non-disease patterns are including an important fraction of patients with evident pathological changes related to the disease, something that might result in reduced performance and lack of biological interpretability. MRI predicted AD dementia significantly better when extending follow-up from 2 to 5 years. MRI was similarly able to detect AD in short and mid-term converters. Predictive models should not learn non-disease patterns from stable MCI over short periods.
Collapse
|
35
|
Probabilistic disease progression modeling to characterize diagnostic uncertainty: Application to staging and prediction in Alzheimer's disease. Neuroimage 2019; 190:56-68. [DOI: 10.1016/j.neuroimage.2017.08.059] [Citation(s) in RCA: 57] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2017] [Revised: 08/07/2017] [Accepted: 08/23/2017] [Indexed: 12/30/2022] Open
|
36
|
Stephenson D, Hill D, Cedarbaum JM, Tome M, Vamvakas S, Romero K, Conrado DJ, Dexter DT, Seibyl J, Jennings D, Nicholas T, Matthews D, Xie Z, Imam S, Maguire P, Russell D, Gordon MF, Stebbins GT, Somer E, Gallagher J, Roach A, Basseches P, Grosset D, Marek K. The Qualification of an Enrichment Biomarker for Clinical Trials Targeting Early Stages of Parkinson's Disease. JOURNAL OF PARKINSON'S DISEASE 2019; 9:553-563. [PMID: 31306141 PMCID: PMC6700608 DOI: 10.3233/jpd-191648] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Accepted: 05/31/2019] [Indexed: 12/12/2022]
Abstract
As therapeutic trials target early stages of Parkinson's disease (PD), appropriate patient selection based purely on clinical criteria poses significant challenges. Members of the Critical Path for Parkinson's Consortium formally submitted documentation to the European Medicines Agency (EMA) supporting the use of Dopamine Transporter (DAT) neuroimaging in early PD. Regulatory documents included a comprehensive literature review, a proposed analysis plan of both observational and clinical trial data, and an assessment of biomarker reproducibility and reliability. The research plan included longitudinal analysis of the Parkinson Research Examination of CEP-1347 Trial (PRECEPT) and the Parkinson's Progression Markers Initiative (PPMI) study to estimate the degree of enrichment achieved and impact on future trials in subjects with early motor PD. The presence of reduced striatal DAT binding based on visual reads of single photon emission tomography (SPECT) scans in early motor PD subjects was an independent predictor of faster decline in UPDRS Parts II and III as compared to subjects with scans without evidence of dopaminergic deficit (SWEDD) over 24 months. The EMA issued in 2018 a full Qualification Opinion for the use of DAT as an enrichment biomarker in PD trials targeting subjects with early motor symptoms. Exclusion of SWEDD subjects in future clinical trials targeting early motor PD subjects aims to enrich clinical trial populations with idiopathic PD patients, improve statistical power, and exclude subjects who are unlikely to progress clinically from being exposed to novel test therapeutics.
Collapse
Affiliation(s)
| | | | | | - Maria Tome
- European Medicines Agency, Amsterdam, Netherlands
| | | | | | | | | | - John Seibyl
- Institute for Neurodegenerative Disorders, New Haven, CT, USA
| | | | | | | | | | - Syed Imam
- U.S. Food and Drug Administration, National Center for Toxicological Research, Jefferson, AR, USA
| | | | - David Russell
- Institute for Neurodegenerative Disorders, New Haven, CT, USA
| | | | | | | | | | | | | | | | - Kenneth Marek
- Institute for Neurodegenerative Disorders, New Haven, CT, USA
| | - on behalf of the Critical Path for Parkinson’s Consortium
- Critical Path Institute, Tucson, AZ, USA
- University College London, UK
- Biogen, Cambridge, MA, USA
- European Medicines Agency, Amsterdam, Netherlands
- Parkinson’s UK, London, UK
- Institute for Neurodegenerative Disorders, New Haven, CT, USA
- Denali Therapeutics, San Francisco, CA, USA
- Pfizer, Groton, CT, USA
- ADM Diagnostics, Northbrook, IL, USA
- UCB, Brussels, Belgium
- CPP Scientific Advisor, PA, USA
- GE Healthcare, London, UK
- Merck & Co., Philadelphia, PA, USA
- University of Glasgow, Scotland
- Rush University, Chicago, IL, USA
- U.S. Food and Drug Administration, National Center for Toxicological Research, Jefferson, AR, USA
| |
Collapse
|
37
|
Cummings J. The Role of Biomarkers in Alzheimer's Disease Drug Development. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2019; 1118:29-61. [PMID: 30747416 DOI: 10.1007/978-3-030-05542-4_2] [Citation(s) in RCA: 73] [Impact Index Per Article: 14.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Biomarkers have a key role in Alzheimer's disease (AD) drug development. Biomarkers can assist in diagnosis, demonstrate target engagement, support disease modification, and monitor for safety. The amyloid (A), tau (T), neurodegeneration (N) Research Framework emphasizes brain imaging and CSF measures relevant to disease diagnosis and staging and can be applied to drug development and clinical trials. Demonstration of target engagement in Phase 2 is critical before advancing a treatment candidate to Phase 3. Trials with biomarker outcomes are shorter and smaller than those required to show clinical benefit and are important to understanding the biological impact of an agent and inform go/no-go decisions. Companion diagnostics are required for safe and effective use of treatments and may emerge in AD drug development programs. Complementary biomarkers inform the use of therapies but are not mandatory for use. Biomarkers promise to de-risk AD drug development, attract sponsors to AD research, and accelerate getting new drugs to those with or at risk for AD.
Collapse
Affiliation(s)
- Jeffrey Cummings
- Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, USA.
| |
Collapse
|
38
|
ten Kate M, Ingala S, Schwarz AJ, Fox NC, Chételat G, van Berckel BNM, Ewers M, Foley C, Gispert JD, Hill D, Irizarry MC, Lammertsma AA, Molinuevo JL, Ritchie C, Scheltens P, Schmidt ME, Visser PJ, Waldman A, Wardlaw J, Haller S, Barkhof F. Secondary prevention of Alzheimer's dementia: neuroimaging contributions. Alzheimers Res Ther 2018; 10:112. [PMID: 30376881 PMCID: PMC6208183 DOI: 10.1186/s13195-018-0438-z] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2018] [Accepted: 10/10/2018] [Indexed: 02/06/2023]
Abstract
BACKGROUND In Alzheimer's disease (AD), pathological changes may arise up to 20 years before the onset of dementia. This pre-dementia window provides a unique opportunity for secondary prevention. However, exposing non-demented subjects to putative therapies requires reliable biomarkers for subject selection, stratification, and monitoring of treatment. Neuroimaging allows the detection of early pathological changes, and longitudinal imaging can assess the effect of interventions on markers of molecular pathology and rates of neurodegeneration. This is of particular importance in pre-dementia AD trials, where clinical outcomes have a limited ability to detect treatment effects within the typical time frame of a clinical trial. We review available evidence for the use of neuroimaging in clinical trials in pre-dementia AD. We appraise currently available imaging markers for subject selection, stratification, outcome measures, and safety in the context of such populations. MAIN BODY Amyloid positron emission tomography (PET) is a validated in-vivo marker of fibrillar amyloid plaques. It is appropriate for inclusion in trials targeting the amyloid pathway, as well as to monitor treatment target engagement. Amyloid PET, however, has limited ability to stage the disease and does not perform well as a prognostic marker within the time frame of a pre-dementia AD trial. Structural magnetic resonance imaging (MRI), providing markers of neurodegeneration, can improve the identification of subjects at risk of imminent decline and hence play a role in subject inclusion. Atrophy rates (either hippocampal or whole brain), which can be reliably derived from structural MRI, are useful in tracking disease progression and have the potential to serve as outcome measures. MRI can also be used to assess comorbid vascular pathology and define homogeneous groups for inclusion or for subject stratification. Finally, MRI also plays an important role in trial safety monitoring, particularly the identification of amyloid-related imaging abnormalities (ARIA). Tau PET to measure neurofibrillary tangle burden is currently under development. Evidence to support the use of advanced MRI markers such as resting-state functional MRI, arterial spin labelling, and diffusion tensor imaging in pre-dementia AD is preliminary and requires further validation. CONCLUSION We propose a strategy for longitudinal imaging to track early signs of AD including quantitative amyloid PET and yearly multiparametric MRI.
Collapse
Affiliation(s)
- Mara ten Kate
- Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, the Netherlands
- Alzheimer Center & Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, PO Box 7056, 1007 MB Amsterdam, the Netherlands
| | - Silvia Ingala
- Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, the Netherlands
| | - Adam J. Schwarz
- Takeda Pharmaceuticals Comparny, Cambridge, MA USA
- Eli Lilly and Company, Indianapolis, Indiana USA
| | - Nick C. Fox
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK
| | - Gaël Chételat
- Institut National de la Santé et de la Recherche Médicale, Inserm UMR-S U1237, Université de Caen-Normandie, GIP Cyceron, Caen, France
| | - Bart N. M. van Berckel
- Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, the Netherlands
| | - Michael Ewers
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-Universität LMU, Munich, Germany
| | | | - Juan Domingo Gispert
- Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain
| | | | | | - Adriaan A. Lammertsma
- Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, the Netherlands
| | - José Luis Molinuevo
- Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain
| | - Craig Ritchie
- Centre for Dementia Prevention, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Philip Scheltens
- Alzheimer Center & Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, PO Box 7056, 1007 MB Amsterdam, the Netherlands
| | | | - Pieter Jelle Visser
- Alzheimer Center & Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, PO Box 7056, 1007 MB Amsterdam, the Netherlands
| | - Adam Waldman
- Centre for Dementia Prevention, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Joanna Wardlaw
- Centre for Dementia Prevention, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- Dementia Research Centre, University of Edinburgh, Edinburgh, UK
| | - Sven Haller
- Affidea Centre de Diagnostic Radiologique de Carouge, Geneva, Switzerland
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, the Netherlands
- Insititutes of Neurology and Healthcare Engineering, University College London, London, UK
| |
Collapse
|
39
|
Impact of video games on plasticity of the hippocampus. Mol Psychiatry 2018; 23:1566-1574. [PMID: 28785110 DOI: 10.1038/mp.2017.155] [Citation(s) in RCA: 71] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/09/2017] [Revised: 06/05/2017] [Accepted: 06/07/2017] [Indexed: 01/18/2023]
Abstract
The hippocampus is critical to healthy cognition, yet results in the current study show that action video game players have reduced grey matter within the hippocampus. A subsequent randomised longitudinal training experiment demonstrated that first-person shooting games reduce grey matter within the hippocampus in participants using non-spatial memory strategies. Conversely, participants who use hippocampus-dependent spatial strategies showed increased grey matter in the hippocampus after training. A control group that trained on 3D-platform games displayed growth in either the hippocampus or the functionally connected entorhinal cortex. A third study replicated the effect of action video game training on grey matter in the hippocampus. These results show that video games can be beneficial or detrimental to the hippocampal system depending on the navigation strategy that a person employs and the genre of the game.
Collapse
|
40
|
Buchert R, Lange C, Suppa P, Apostolova I, Spies L, Teipel S, Dubois B, Hampel H, Grothe MJ. Magnetic resonance imaging-based hippocampus volume for prediction of dementia in mild cognitive impairment: Why does the measurement method matter so little? Alzheimers Dement 2018; 14:976-978. [PMID: 29679575 DOI: 10.1016/j.jalz.2018.03.006] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2017] [Revised: 02/23/2018] [Accepted: 03/01/2018] [Indexed: 11/27/2022]
Affiliation(s)
- Ralph Buchert
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Hospital Hamburg-Eppendorf, Hamburg, Germany.
| | - Catharina Lange
- Department of Nuclear Medicine, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Per Suppa
- jung diagnostics GmbH, Hamburg, Germany
| | - Ivayla Apostolova
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Hospital Hamburg-Eppendorf, Hamburg, Germany
| | | | - Stefan Teipel
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany
| | - Bruno Dubois
- AXA Research Fund and Sorbonne University Chair, Paris, France, Sorbonne University, GRC No. 21, Alzheimer Precision Medicine (APM), AP-HP, Pitié-Salpêtrière Hospital, Paris, France, Brain & Spine Institute (ICM), INSERM U 1127, CNRS UMR 7225, Paris, France, Institute of Memory and Alzheimer's Disease (IM2A), Department of Neurology, Pitié-Salpêtrière Hospital, AP-HP, Paris, France
| | - Harald Hampel
- AXA Research Fund and Sorbonne University Chair, Paris, France, Sorbonne University, GRC No. 21, Alzheimer Precision Medicine (APM), AP-HP, Pitié-Salpêtrière Hospital, Paris, France, Brain & Spine Institute (ICM), INSERM U 1127, CNRS UMR 7225, Paris, France, Institute of Memory and Alzheimer's Disease (IM2A), Department of Neurology, Pitié-Salpêtrière Hospital, AP-HP, Paris, France
| | - Michel J Grothe
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany
| |
Collapse
|
41
|
Arnerić SP, Batrla-Utermann R, Beckett L, Bittner T, Blennow K, Carter L, Dean R, Engelborghs S, Genius J, Gordon MF, Hitchcock J, Kaplow J, Luthman J, Meibach R, Raunig D, Romero K, Samtani MN, Savage M, Shaw L, Stephenson D, Umek RM, Vanderstichele H, Willis B, Yule S. Cerebrospinal Fluid Biomarkers for Alzheimer's Disease: A View of the Regulatory Science Qualification Landscape from the Coalition Against Major Diseases CSF Biomarker Team. J Alzheimers Dis 2018; 55:19-35. [PMID: 27662307 PMCID: PMC5115607 DOI: 10.3233/jad-160573] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Alzheimer's disease (AD) drug development is burdened with the current requirement to conduct large, lengthy, and costly trials to overcome uncertainty in patient progression and effect size on treatment outcome measures. There is an urgent need for the discovery, development, and implementation of novel, objectively measured biomarkers for AD that would aid selection of the appropriate subpopulation of patients in clinical trials, and presumably, improve the likelihood of successfully evaluating innovative treatment options. Amyloid deposition and tau in the brain, which are most commonly assessed either in cerebrospinal fluid (CSF) or by molecular imaging, are consistently and widely accepted. Nonetheless, a clear gap still exists in the accurate identification of subjects that truly have the hallmarks of AD. The Coalition Against Major Diseases (CAMD), one of 12 consortia of the Critical Path Institute (C-Path), aims to streamline drug development for AD and related dementias by advancing regulatory approved drug development tools for clinical trials through precompetitive data sharing and adoption of consensus clinical data standards. This report focuses on the regulatory process for biomarker qualification, briefly comments on how it contrasts with approval or clearance of companion diagnostics, details the qualifications currently available to the field of AD, and highlights the current challenges facing the landscape of CSF biomarkers qualified as hallmarks of AD. Finally, it recommends actions to accelerate regulatory qualification of CSF biomarkers that would, in turn, improve the efficiency of AD therapeutic development.
Collapse
Affiliation(s)
- Stephen P Arnerić
- Critical Path Institute, Coalition Against Major Diseases, Tucson, AZ, USA
| | | | | | | | - Kaj Blennow
- Clinical Neurochemistry Lab, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at University of Gothenburg, Sahlgrenska University Hospital, Mölndal, Sweden
| | | | - Robert Dean
- Eli Lilly and Company, Indianapolis, IN, USA
| | - Sebastiaan Engelborghs
- Reference Center for Biological Markers of Dementia (BIODEM), University of Antwerp, Antwerp, Belgium
| | | | | | | | | | | | | | | | - Klaus Romero
- Critical Path Institute, Coalition Against Major Diseases, Tucson, AZ, USA
| | | | | | - Leslie Shaw
- University of Pennsylvania, Philadelphia, PA, USA
| | - Diane Stephenson
- Critical Path Institute, Coalition Against Major Diseases, Tucson, AZ, USA
| | | | | | | | | |
Collapse
|
42
|
Arnerić SP, Kern VD, Stephenson DT. Regulatory-accepted drug development tools are needed to accelerate innovative CNS disease treatments. Biochem Pharmacol 2018; 151:291-306. [PMID: 29410157 DOI: 10.1016/j.bcp.2018.01.043] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2017] [Accepted: 01/26/2018] [Indexed: 02/07/2023]
Abstract
Central Nervous System (CNS) diseases represent one of the most challenging therapeutic areas for successful drug approvals. Developing quantitative biomarkers as Drug Development Tools (DDTs) can catalyze the path to innovative treatments, and improve the chances of drug approvals. Drug development and healthcare management requires sensitive, reliable, validated, and regulatory accepted biomarkers and endpoints. This review highlights the regulatory paths and considerations for developing DDTs required to advance biomarker and endpoint use in clinical development (e.g., consensus CDISC [Clinical Data Interchange Standards Consortium] data standards, precompetitive sharing of anonymized patient-level data, and continual alignment with regulators). Summarized is the current landscape of biomarkers in a range of CNS diseases including Alzheimer disease, Parkinson Disease, Amyotrophic Lateral Sclerosis, Autism Spectrum Disorders, Depression, Huntington's disease, Multiple Sclerosis and Traumatic Brain Injury. Advancing DDTs for these devastating diseases that are both validated and qualified will require an integrated, cross-consortium approach to accelerate the delivery of innovative CNS therapeutics.
Collapse
Affiliation(s)
- Stephen P Arnerić
- Critical Path for Alzheimer's Disease, Crititcal Path Institute, United States.
| | - Volker D Kern
- Critical Path for Alzheimer's Disease, Crititcal Path Institute, United States
| | | |
Collapse
|
43
|
Ten Kate M, Barkhof F, Boccardi M, Visser PJ, Jack CR, Lovblad KO, Frisoni GB, Scheltens P. Clinical validity of medial temporal atrophy as a biomarker for Alzheimer's disease in the context of a structured 5-phase development framework. Neurobiol Aging 2017; 52:167-182.e1. [PMID: 28317647 DOI: 10.1016/j.neurobiolaging.2016.05.024] [Citation(s) in RCA: 54] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2015] [Revised: 05/01/2016] [Accepted: 05/10/2016] [Indexed: 01/18/2023]
Abstract
Research criteria for Alzheimer's disease recommend the use of biomarkers for diagnosis, but whether biomarkers improve the diagnosis in clinical routine has not been systematically assessed. The aim is to evaluate the evidence for use of medial temporal lobe atrophy (MTA) as a biomarker for Alzheimer's disease at the mild cognitive impairment stage in routine clinical practice, with an adapted version of the 5-phase oncology framework for biomarker development. A literature review on visual assessment of MTA and hippocampal volumetry was conducted with other biomarkers addressed in parallel reviews. Ample evidence is available for phase 1 (rationale for use) and phase 2 (discriminative ability between diseased and control subjects). Phase 3 (early detection ability) is partly achieved: most evidence is derived from research cohorts or clinical populations with short follow-up, but validation in clinical mild cognitive impairment cohorts is required. In phase 4, only the practical feasibility has been addressed for visual rating of MTA. The rest of phase 4 and phase 5 have not yet been addressed.
Collapse
Affiliation(s)
- Mara Ten Kate
- Department of Neurology, Alzheimer Center, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, the Netherlands.
| | - Frederik Barkhof
- Department of Radiology & Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, the Netherlands; European Society of Neuroradiology (ESNR); Institutes of Neurology and Healthcare Engineering, University College London, London, UK
| | - Marina Boccardi
- Laboratory of Alzheimer's Neuroimaging and Epidemiology (LANE), IRCCS S.Giovanni di Dio - Fatebenefratelli, Brescia, Italy; LANVIE (Laboratory of Neuroimaging of Aging) - Department of Psychiatry, University of Geneva, Geneva, Switzerland
| | - Pieter Jelle Visser
- Department of Neurology, Alzheimer Center, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, the Netherlands; Department of Psychiatry & Neuropsychology, School for Mental Health and Neuroscience, Maastricht University, Maastricht, the Netherlands
| | | | - Karl-Olof Lovblad
- Department of Neuroradiology, University Hospital of Geneva, Geneva, Switzerland
| | - Giovanni B Frisoni
- Institutes of Neurology and Healthcare Engineering, University College London, London, UK; Memory Clinic - Department of Internal Medicine, University Hospital of Geneva, Geneva, Switzerland
| | - Philip Scheltens
- Department of Neurology, Alzheimer Center, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, the Netherlands
| | | |
Collapse
|
44
|
Rice L, Bisdas S. The diagnostic value of FDG and amyloid PET in Alzheimer’s disease—A systematic review. Eur J Radiol 2017; 94:16-24. [DOI: 10.1016/j.ejrad.2017.07.014] [Citation(s) in RCA: 74] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2017] [Revised: 06/13/2017] [Accepted: 07/17/2017] [Indexed: 12/12/2022]
|
45
|
Cavedo E, Suppa P, Lange C, Opfer R, Lista S, Galluzzi S, Schwarz AJ, Spies L, Buchert R, Hampel H. Fully Automatic MRI-Based Hippocampus Volumetry Using FSL-FIRST: Intra-Scanner Test-Retest Stability, Inter-Field Strength Variability, and Performance as Enrichment Biomarker for Clinical Trials Using Prodromal Target Populations at Risk for Alzheimer’s Disease. J Alzheimers Dis 2017; 60:151-164. [DOI: 10.3233/jad-161108] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Affiliation(s)
- Enrica Cavedo
- AXA Research Fund and UPMC Chair, Sorbonne Universités, Université Pierre et Marie Curie (UPMC) Paris 06, Inserm, CNRS, Institut du Cerveau et de la Moelle Épinière (ICM), Département de Neurologie, Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Hôpital Pitié-Salpêtrière, Boulevard de l’hôpital, F-75013, Paris, France
- IRCCS Centro San Giovanni di Dio, Brescia, Italy
| | - Per Suppa
- Department of Nuclear Medicine, Charité– Universitätsmedizin Berlin, Berlin, Germany
- Jung diagnostics GmbH, Hamburg, Germany
| | - Catharina Lange
- Department of Nuclear Medicine, Charité– Universitätsmedizin Berlin, Berlin, Germany
| | | | - Simone Lista
- AXA Research Fund and UPMC Chair, Sorbonne Universités, Université Pierre et Marie Curie (UPMC) Paris 06, Inserm, CNRS, Institut du Cerveau et de la Moelle Épinière (ICM), Département de Neurologie, Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Hôpital Pitié-Salpêtrière, Boulevard de l’hôpital, F-75013, Paris, France
| | | | | | | | - Ralph Buchert
- Department of Nuclear Medicine, Charité– Universitätsmedizin Berlin, Berlin, Germany
- Department of Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Harald Hampel
- AXA Research Fund and UPMC Chair, Sorbonne Universités, Université Pierre et Marie Curie (UPMC) Paris 06, Inserm, CNRS, Institut du Cerveau et de la Moelle Épinière (ICM), Département de Neurologie, Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Hôpital Pitié-Salpêtrière, Boulevard de l’hôpital, F-75013, Paris, France
| | | | | |
Collapse
|
46
|
Conrado DJ, Nicholas T, Tsai K, Macha S, Sinha V, Stone J, Corrigan B, Bani M, Muglia P, Watson IA, Kern VD, Sheveleva E, Marek K, Stephenson DT, Romero K. Dopamine Transporter Neuroimaging as an Enrichment Biomarker in Early Parkinson's Disease Clinical Trials: A Disease Progression Modeling Analysis. Clin Transl Sci 2017; 11:63-70. [PMID: 28749580 PMCID: PMC5759747 DOI: 10.1111/cts.12492] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2017] [Accepted: 06/27/2017] [Indexed: 01/01/2023] Open
Abstract
Given the recognition that disease‐modifying therapies should focus on earlier Parkinson's disease stages, trial enrollment based purely on clinical criteria poses significant challenges. The goal herein was to determine the utility of dopamine transporter neuroimaging as an enrichment biomarker in early motor Parkinson's disease clinical trials. Patient‐level longitudinal data of 672 subjects with early‐stage Parkinson's disease in the Parkinson's Progression Markers Initiative (PPMI) observational study and the Parkinson Research Examination of CEP‐1347 Trial (PRECEPT) clinical trial were utilized in a linear mixed‐effects model analysis. The rate of worsening in the motor scores between subjects with or without a scan without evidence of dopamine transporter deficit was different both statistically and clinically. The average difference in the change from baseline of motor scores at 24 months between biomarker statuses was –3.16 (90% confidence interval [CI] = –0.96 to –5.42) points. Dopamine transporter imaging could identify subjects with a steeper worsening of the motor scores, allowing trial enrichment and 24% reduction of sample size.
Collapse
Affiliation(s)
| | | | - Kuenhi Tsai
- Merck Sharp & Dohme, North Wales, Pennsylvania, USA
| | | | - Vikram Sinha
- Merck Sharp & Dohme, North Wales, Pennsylvania, USA
| | - Julie Stone
- Merck Sharp & Dohme, North Wales, Pennsylvania, USA
| | | | | | | | | | | | - Elena Sheveleva
- Critical Path Institute, Tucson, Arizona, USA.,University of Arizona, Tucson, Arizona, USA
| | - Kenneth Marek
- Institute for Neurodegenerative Disorders, New Haven, Connecticut, USA
| | | | | | | |
Collapse
|
47
|
Manley GT, Mac Donald CL, Markowitz AJ, Stephenson D, Robbins A, Gardner RC, Winkler E, Bodien YG, Taylor SR, Yue JK, Kannan L, Kumar A, McCrea MA, Wang KK. The Traumatic Brain Injury Endpoints Development (TED) Initiative: Progress on a Public-Private Regulatory Collaboration To Accelerate Diagnosis and Treatment of Traumatic Brain Injury. J Neurotrauma 2017; 34:2721-2730. [PMID: 28363253 DOI: 10.1089/neu.2016.4729] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
The Traumatic Brain Injury Endpoints Development (TED) Initiative is a 5-year, Department of Defense-funded project that is working toward the ultimate goal of developing better designed clinical trials, leading to more precise diagnosis, and effective treatments for traumatic brain injury (TBI). TED is comprised of leading academic clinician-scientists, along with innovative industry leaders in biotechnology and imaging technology, patient advocacy organizations, and philanthropists, working collaboratively with regulatory authorities, specifically the U.S. Food and Drug Administration (FDA). The goals of the TED Initiative are to gain consensus and validation of TBI clinical outcome assessment measures and biomarkers for endorsement by global regulatory agencies for use in drug and device development processes. This article summarizes the Initiative's Stage I progress over the first 18 months, including intensive engagement with a number of FDA divisions responsible for review and validation of biomarkers and clinical outcome assessments, progression into the prequalification phase of the FDA's Medical Device Development Tool program for a candidate set of neuroimaging biomarkers, and receipt of the FDA's Recognition of Research Importance Letter and a Letter of Support regarding TBI. Other signal achievements relate to the creation of the TED Metadataset, harmonizing study measures across eight major TBI studies, and the leadership role played by TED investigators in the conversion of the NINDS TBI Common Data Elements to Clinical Data Interchange Standards Consortium standards. This article frames both the near-term expectations and the Initiative's long-term vision to accelerate approval of treatments for patients affected by TBI in urgent need of effective therapies.
Collapse
Affiliation(s)
- Geoffrey T Manley
- Department of Neurological Surgery, University of California San Francisco, Zuckerberg San Francisco General Hospital and Trauma Center, and the Brain and Spinal Injury Center, University of California San Francisco, San Francisco, California
| | | | - Amy J Markowitz
- Department of Neurological Surgery, University of California San Francisco, Zuckerberg San Francisco General Hospital and Trauma Center, and the Brain and Spinal Injury Center, University of California San Francisco, San Francisco, California
| | | | | | - Raquel C Gardner
- Department of Neurology, University of California San Francisco, San Francisco, California
| | - Ethan Winkler
- Department of Neurological Surgery, University of California San Francisco, San Francisco, California
| | - Yelena G Bodien
- Department of Physical Medicine and Rehabilitation, Harvard Medical School, Charlestown, Massachusetts
| | - Sabrina R Taylor
- Department of Neurological Surgery, University of California San Francisco, Zuckerberg San Francisco General Hospital and Trauma Center, and the Brain and Spinal Injury Center, University of California San Francisco, San Francisco, California
| | - John K Yue
- Department of Neurological Surgery, University of California San Francisco, Zuckerberg San Francisco General Hospital and Trauma Center, and the Brain and Spinal Injury Center, University of California San Francisco, San Francisco, California
| | - Lakshmi Kannan
- Emergency Preparedness/Operations and Medical Countermeasures Program, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland
| | | | - Michael A McCrea
- Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Kevin K Wang
- Program for Neurotrauma, Neuroproteomics & Biomarker Research, Department of Psychiatry, McKnight Brain Institute, University of Florida, Gainesville, Florida
| | | |
Collapse
|
48
|
Weiner MW, Veitch DP, Aisen PS, Beckett LA, Cairns NJ, Green RC, Harvey D, Jack CR, Jagust W, Morris JC, Petersen RC, Saykin AJ, Shaw LM, Toga AW, Trojanowski JQ. Recent publications from the Alzheimer's Disease Neuroimaging Initiative: Reviewing progress toward improved AD clinical trials. Alzheimers Dement 2017; 13:e1-e85. [PMID: 28342697 DOI: 10.1016/j.jalz.2016.11.007] [Citation(s) in RCA: 170] [Impact Index Per Article: 24.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2016] [Revised: 11/21/2016] [Accepted: 11/28/2016] [Indexed: 01/31/2023]
Abstract
INTRODUCTION The Alzheimer's Disease Neuroimaging Initiative (ADNI) has continued development and standardization of methodologies for biomarkers and has provided an increased depth and breadth of data available to qualified researchers. This review summarizes the over 400 publications using ADNI data during 2014 and 2015. METHODS We used standard searches to find publications using ADNI data. RESULTS (1) Structural and functional changes, including subtle changes to hippocampal shape and texture, atrophy in areas outside of hippocampus, and disruption to functional networks, are detectable in presymptomatic subjects before hippocampal atrophy; (2) In subjects with abnormal β-amyloid deposition (Aβ+), biomarkers become abnormal in the order predicted by the amyloid cascade hypothesis; (3) Cognitive decline is more closely linked to tau than Aβ deposition; (4) Cerebrovascular risk factors may interact with Aβ to increase white-matter (WM) abnormalities which may accelerate Alzheimer's disease (AD) progression in conjunction with tau abnormalities; (5) Different patterns of atrophy are associated with impairment of memory and executive function and may underlie psychiatric symptoms; (6) Structural, functional, and metabolic network connectivities are disrupted as AD progresses. Models of prion-like spreading of Aβ pathology along WM tracts predict known patterns of cortical Aβ deposition and declines in glucose metabolism; (7) New AD risk and protective gene loci have been identified using biologically informed approaches; (8) Cognitively normal and mild cognitive impairment (MCI) subjects are heterogeneous and include groups typified not only by "classic" AD pathology but also by normal biomarkers, accelerated decline, and suspected non-Alzheimer's pathology; (9) Selection of subjects at risk of imminent decline on the basis of one or more pathologies improves the power of clinical trials; (10) Sensitivity of cognitive outcome measures to early changes in cognition has been improved and surrogate outcome measures using longitudinal structural magnetic resonance imaging may further reduce clinical trial cost and duration; (11) Advances in machine learning techniques such as neural networks have improved diagnostic and prognostic accuracy especially in challenges involving MCI subjects; and (12) Network connectivity measures and genetic variants show promise in multimodal classification and some classifiers using single modalities are rivaling multimodal classifiers. DISCUSSION Taken together, these studies fundamentally deepen our understanding of AD progression and its underlying genetic basis, which in turn informs and improves clinical trial design.
Collapse
Affiliation(s)
- Michael W Weiner
- Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA; Department of Radiology, University of California, San Francisco, CA, USA; Department of Medicine, University of California, San Francisco, CA, USA; Department of Psychiatry, University of California, San Francisco, CA, USA; Department of Neurology, University of California, San Francisco, CA, USA.
| | - Dallas P Veitch
- Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA
| | - Paul S Aisen
- Alzheimer's Therapeutic Research Institute, University of Southern California, San Diego, CA, USA
| | - Laurel A Beckett
- Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, CA, USA
| | - Nigel J Cairns
- Knight Alzheimer's Disease Research Center, Washington University School of Medicine, Saint Louis, MO, USA; Department of Neurology, Washington University School of Medicine, Saint Louis, MO, USA
| | - Robert C Green
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Danielle Harvey
- Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, CA, USA
| | | | - William Jagust
- Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA, USA
| | - John C Morris
- Alzheimer's Therapeutic Research Institute, University of Southern California, San Diego, CA, USA
| | | | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Leslie M Shaw
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Arthur W Toga
- Laboratory of Neuroimaging, Institute of Neuroimaging and Informatics, Keck School of Medicine of University of Southern California, Los Angeles, CA, USA
| | - John Q Trojanowski
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Institute on Aging, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Alzheimer's Disease Core Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Udall Parkinson's Research Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | | |
Collapse
|
49
|
Modulation of APOE and SORL1 genes on hippocampal functional connectivity in healthy young adults. Brain Struct Funct 2017; 222:2877-2889. [PMID: 28229235 PMCID: PMC5541082 DOI: 10.1007/s00429-017-1377-3] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2015] [Accepted: 01/26/2017] [Indexed: 10/27/2022]
Abstract
Apolipoprotein E (APOE) and sortilin-related receptor (SORL1) genes act on the same metabolic pathway and have been associated with Alzheimer's disease (AD) characterized by hippocampal impairment. Although the effects of APOE on hippocampal resting-state functional connectivity (rsFC) have been reported, the main effects of SORL1 and SORL1 × APOE interactions on hippocampal rsFC in healthy subjects remain largely unknown. Here, we systematically investigated the main effects of SORL1 rs2070045, and APOE, and their interaction effects on hippocampal rsFC in healthy young adults. The main effect of APOE showed that risk ε4 carriers had decreased positive hippocampal rsFC with the precuneus/posterior cingulate cortex and subgenual anterior cingulate cortex, and increased positive hippocampal rsFC with the sensorimotor cortex compared with non-ε4 carriers. The main effect of SORL1 showed that risk G-allele carriers had decreased positive rsFC between the hippocampus and middle temporal gyrus compared with TT carriers. No significant additive interaction was observed. Instead, significant SORL1 × APOE non-additive interaction was found in negative rsFC between the hippocampus and inferior frontal gyrus. Compared with subjects with TT genotype, SORL1 G-allele carriers had a stronger negative rsFC in APOE ε4 carriers, but a weaker negative rsFC in APOE non-ε4 carriers. These findings suggest that SORL1 and APOE genes modulate different hippocampal rsFCs and have a complex interaction. The SORL1- and APOE-dependent hippocampal connectivity changes may at least partly account for their association with AD.
Collapse
|
50
|
Woodward MR, Amrutkar CV, Shah HC, Benedict RHB, Rajakrishnan S, Doody RS, Yan L, Szigeti K. Validation of olfactory deficit as a biomarker of Alzheimer disease. Neurol Clin Pract 2017; 7:5-14. [PMID: 28243501 PMCID: PMC5310210 DOI: 10.1212/cpj.0000000000000293] [Citation(s) in RCA: 62] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2016] [Accepted: 06/20/2016] [Indexed: 01/17/2023]
Abstract
BACKGROUND We evaluated smell identification as a biomarker for Alzheimer disease (AD) by assessing its utility in differentiating normal aging from an amnestic disorder and determining its predictive value for conversion from amnestic mild cognitive impairment (aMCI) to AD. METHODS Cross-sectional study (AD = 262, aMCI = 110, controls = 194) measuring smell identification (University of Pennsylvania Smell Identification Test [UPSIT]) and cognitive status was performed, as well as longitudinal analysis of aMCI participants (n = 96) with at least 1 year follow-up (mean 477.6 ± 223.3 days), to determine conversion by National Institute of Neurological and Communicative Disorders and Stroke-Alzheimer's Disease and Related Disorders Association criteria. RESULTS Odor identification and disease status were highly correlated after correcting for age, sex, and APOE (p < 0.001). Receiver operating characteristic (ROC)/area under the curve (AUC) was similar for the 40-item UPSIT, the top 10 smells in our study, and the 10-item subset previously proposed. Smeller/nonsmeller based on the 10-item subset with a cutoff of 7 (≤7, nonsmeller; >7, smeller) had a sensitivity and specificity of 88% and 71% for identifying AD and 74% sensitivity and 71% specificity for identifying an amnestic disorder. A total of 36.4% of participants with impaired olfaction and 17.3% with intact olfaction converted to AD (p = 0.03). The ROC/AUC for prediction of conversion to AD was 0.62. CONCLUSIONS Olfactory identification deficit is a useful screening tool for AD-related amnestic disorder, with sensitivity and specificity comparable to other established biomarkers, with benefits such as ease of administration and low cost. Olfactory identification deficit can be utilized to stratify risk of conversion from aMCI to AD and enrich clinical trials of disease-modifying therapy. CLASSIFICATION OF EVIDENCE This study provides Class III evidence that smell identification (10-item UPSIT subset) accurately identifies patients with amnestic disorders.
Collapse
Affiliation(s)
- Matthew R Woodward
- Alzheimer's Disease and Memory Disorders Center, Department of Neurology (MRW, CVA, HCS, RHBB, SR, KS), and Department of Bioinformatics (LY), University at Buffalo, SUNY, NY; and Alzheimer's Disease and Memory Disorders Center (RSD), Department of Neurology, Baylor College of Medicine, Houston, TX
| | - Chaitanya V Amrutkar
- Alzheimer's Disease and Memory Disorders Center, Department of Neurology (MRW, CVA, HCS, RHBB, SR, KS), and Department of Bioinformatics (LY), University at Buffalo, SUNY, NY; and Alzheimer's Disease and Memory Disorders Center (RSD), Department of Neurology, Baylor College of Medicine, Houston, TX
| | - Harshit C Shah
- Alzheimer's Disease and Memory Disorders Center, Department of Neurology (MRW, CVA, HCS, RHBB, SR, KS), and Department of Bioinformatics (LY), University at Buffalo, SUNY, NY; and Alzheimer's Disease and Memory Disorders Center (RSD), Department of Neurology, Baylor College of Medicine, Houston, TX
| | - Ralph H B Benedict
- Alzheimer's Disease and Memory Disorders Center, Department of Neurology (MRW, CVA, HCS, RHBB, SR, KS), and Department of Bioinformatics (LY), University at Buffalo, SUNY, NY; and Alzheimer's Disease and Memory Disorders Center (RSD), Department of Neurology, Baylor College of Medicine, Houston, TX
| | - Sanjanaa Rajakrishnan
- Alzheimer's Disease and Memory Disorders Center, Department of Neurology (MRW, CVA, HCS, RHBB, SR, KS), and Department of Bioinformatics (LY), University at Buffalo, SUNY, NY; and Alzheimer's Disease and Memory Disorders Center (RSD), Department of Neurology, Baylor College of Medicine, Houston, TX
| | - Rachelle S Doody
- Alzheimer's Disease and Memory Disorders Center, Department of Neurology (MRW, CVA, HCS, RHBB, SR, KS), and Department of Bioinformatics (LY), University at Buffalo, SUNY, NY; and Alzheimer's Disease and Memory Disorders Center (RSD), Department of Neurology, Baylor College of Medicine, Houston, TX
| | - Li Yan
- Alzheimer's Disease and Memory Disorders Center, Department of Neurology (MRW, CVA, HCS, RHBB, SR, KS), and Department of Bioinformatics (LY), University at Buffalo, SUNY, NY; and Alzheimer's Disease and Memory Disorders Center (RSD), Department of Neurology, Baylor College of Medicine, Houston, TX
| | - Kinga Szigeti
- Alzheimer's Disease and Memory Disorders Center, Department of Neurology (MRW, CVA, HCS, RHBB, SR, KS), and Department of Bioinformatics (LY), University at Buffalo, SUNY, NY; and Alzheimer's Disease and Memory Disorders Center (RSD), Department of Neurology, Baylor College of Medicine, Houston, TX
| |
Collapse
|