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García-Gutiérrez F, Hernández-Lorenzo L, Cabrera-Martín MN, Matias-Guiu JA, Ayala JL. Predicting changes in brain metabolism and progression from mild cognitive impairment to dementia using multitask Deep Learning models and explainable AI. Neuroimage 2024; 297:120695. [PMID: 38942101 DOI: 10.1016/j.neuroimage.2024.120695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2024] [Accepted: 06/18/2024] [Indexed: 06/30/2024] Open
Abstract
BACKGROUND The prediction of Alzheimer's disease (AD) progression from its early stages is a research priority. In this context, the use of Artificial Intelligence (AI) in AD has experienced a notable surge in recent years. However, existing investigations predominantly concentrate on distinguishing clinical phenotypes through cross-sectional approaches. This study aims to investigate the potential of modeling additional dimensions of the disease, such as variations in brain metabolism assessed via [18F]-fluorodeoxyglucose positron emission tomography (FDG-PET), and utilize this information to identify patients with mild cognitive impairment (MCI) who will progress to dementia (pMCI). METHODS We analyzed data from 1,617 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) who had undergone at least one FDG-PET scan. We identified the brain regions with the most significant hypometabolism in AD and used Deep Learning (DL) models to predict future changes in brain metabolism. The best-performing model was then adapted under a multi-task learning framework to identify pMCI individuals. Finally, this model underwent further analysis using eXplainable AI (XAI) techniques. RESULTS Our results confirm a strong association between hypometabolism, disease progression, and cognitive decline. Furthermore, we demonstrated that integrating data on changes in brain metabolism during training enhanced the models' ability to detect pMCI individuals (sensitivity=88.4%, specificity=86.9%). Lastly, the application of XAI techniques enabled us to delve into the brain regions with the most significant impact on model predictions, highlighting the importance of the hippocampus, cingulate cortex, and some subcortical structures. CONCLUSION This study introduces a novel dimension to predictive modeling in AD, emphasizing the importance of projecting variations in brain metabolism under a multi-task learning paradigm.
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Affiliation(s)
| | | | - María Nieves Cabrera-Martín
- Department of Nuclear Medicine, Hospital Clínico San Carlos, Instituto de Investigación Sanitaria San Carlos (IdISSC), Madrid, Spain.
| | - Jordi A Matias-Guiu
- Department of Neurology, Hospital Clínico San Carlos, Instituto de Investigación Sanitaria San Carlos (IdISSC), Madrid, Spain.
| | - José L Ayala
- Department of Computer Architecture and Automation, Universidad Complutense, Madrid, Spain.
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Martinec Nováková L, Georgi H, Vlčková K, Kopeček M, Babuská A, Havlíček J. Small effects of olfactory identification and discrimination on global cognitive and executive performance over 1 year in aging people without a history of age-related cognitive impairment. Physiol Behav 2024; 282:114579. [PMID: 38710351 DOI: 10.1016/j.physbeh.2024.114579] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Revised: 05/01/2024] [Accepted: 05/02/2024] [Indexed: 05/08/2024]
Abstract
Olfactory and cognitive performance share neural correlates profoundly affected by physiological aging. However, whether odor identification and discrimination scores predict global cognitive status and executive function in healthy older people with intact cognition is unclear. Therefore, in the present study, we set out to elucidate these links in a convenience sample of 204 independently living, cognitively intact healthy Czech adults aged 77.4 ± 8.7 (61-97 years) over two waves of data collection (one-year interval). We used the Czech versions of the Montreal Cognitive Assessment (MoCA) to evaluate global cognition, and the Prague Stroop Test (PST), Trail Making Test (TMT), and several verbal fluency (VF) tests to assess executive function. As a subsidiary aim, we aimed to examine the contribution of olfactory performance towards achieving a MoCA score above vs. below the published cut-off value. We found that the MoCA scores exhibited moderate associations with both odor identification and discrimination. Furthermore, odor identification significantly predicted PST C and C/D scores. Odor discrimination significantly predicted PST C/D, TMT B/A, and standardized composite VF scores. Our findings demonstrate that olfaction, on the one hand, and global cognition and executive function, on the other, are related even in healthy older people.
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Affiliation(s)
- Lenka Martinec Nováková
- Department of Psychology and Life Sciences, Faculty of Humanities, Charles University, Pátkova 2137/5, 182 00 Prague 8 - Libeň, Czech Republic; Department of Chemical Education and Humanities, University of Chemistry and Technology, Prague, Technická 5, 166 28 Prague 6 - Dejvice, Czech Republic.
| | - Hana Georgi
- Prague College of Psychosocial Studies, Hekrova 805, 149 00 Prague 4, Czech Republic
| | - Karolína Vlčková
- Department of Psychiatry and Medical Psychology, Third Faculty of Medicine, Charles University, Ruská 87, 100 00 Prague 10 - Vršovice, Czech Republic; Thomayer Teaching Hospital, Vídeňská 800, 140 59 Prague 4 - Krč, Czech Republic
| | - Miloslav Kopeček
- Department of Psychiatry and Medical Psychology, Third Faculty of Medicine, Charles University, Ruská 87, 100 00 Prague 10 - Vršovice, Czech Republic; National Institute of Mental Health, Topolová 748, 250 67 Klecany, Czech Republic
| | - Anna Babuská
- Department of Zoology, Faculty of Science, Charles University, Viničná 7, 128 00 Prague 2, Czech Republic
| | - Jan Havlíček
- Department of Zoology, Faculty of Science, Charles University, Viničná 7, 128 00 Prague 2, Czech Republic
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Asp I, Cawley-Bennett ATJ, Frascino JC, Golshan S, Bondi MW, Smith CN. News event memory in amnestic and non-amnestic MCI, heritable risk for dementia, and subjective memory complaints. Neuropsychologia 2024; 199:108887. [PMID: 38621578 DOI: 10.1016/j.neuropsychologia.2024.108887] [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: 09/25/2023] [Revised: 04/09/2024] [Accepted: 04/09/2024] [Indexed: 04/17/2024]
Abstract
Robust and sensitive clinical measures are needed for more accurate and earlier detection of Alzheimer's disease (AD), for staging preclinical AD, and for gauging the efficacy of treatments. Mild impairment on episodic memory tests is thought to indicate a cognitive risk of developing AD and mild cognitive impairment (MCI), considered to be a transitional stage between normal aging and AD. Novel tests of semantic memory, such as memory for news events, are also impaired early on but have received little clinical attention even though they may provide a novel way to assess cognitive risk for AD. We examined memory for news events in older adults with normal cognition (NC, N = 34), amnestic MCI (aMCI, N = 27), or non-aMCI (N = 10) using the Retrograde Memory News Events Test (RM-NET). We asked if news event memory was sensitive to 1) aMCI and also non-aMCI, which has rarely been examined, 2) genetic risk for dementia (positive family history of any type of dementia, presence of an APOE-4 allele, or polygenic risk for AD), and 3) subjective memory functioning judgments about the past. We found that both MCI subgroups exhibited impaired RM-NET Lifespan accuracy scores together with temporally-limited retrograde amnesia. For the aMCI group amnesia extended back 45 years prior to testing, but not beyond that time frame. The extent of retrograde amnesia could not be reliably estimated in the small non-aMCI group. The effect sizes of having MCI on the RM-NET were medium for the non-aMCI group and large for the aMCI group, whereas the effect sizes of participant characteristics on RM-NET accuracy scores were small. For the combined MCI group (N = 37), news event memory was significantly related to positive family history of dementia but was not related to the more specific genetic markers of AD risk. For the NC group, news event memory was not related to any measure of genetic risk. Objective measures of past memory from the RM-NET were not related to subjective memory judgements about the present or the recent past in either group. By contrast, when individuals subjectively compared their present versus past memory abilities, there was a significant association between this judgment and objective measures of the past from the RM-NET (direct association for the NC group and inverse for the MCI group). The RM-NET holds significant promise for early identification of those with cognitive and genetic risk factors for AD and non-AD dementias.
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Affiliation(s)
- Isabel Asp
- San Diego Veterans Affairs Medical Center, San Diego, CA, USA
| | | | - Jennifer C Frascino
- San Diego Veterans Affairs Medical Center, San Diego, CA, USA; Department of Psychiatry, University of California San Diego, CA, USA
| | - Shahrokh Golshan
- San Diego Veterans Affairs Medical Center, San Diego, CA, USA; Department of Psychiatry, University of California San Diego, CA, USA
| | - Mark W Bondi
- San Diego Veterans Affairs Medical Center, San Diego, CA, USA; Department of Psychiatry, University of California San Diego, CA, USA
| | - Christine N Smith
- San Diego Veterans Affairs Medical Center, San Diego, CA, USA; Department of Psychiatry, University of California San Diego, CA, USA; Center for the Neurobiology of Learning and Memory, University of California Irvine, CA, USA.
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Tang R, Buchholz E, Dale AM, Rissman RA, Fennema-Notestine C, Gillespie NA, Hagler DJ, Lyons MJ, Neale MC, Panizzon MS, Puckett OK, Reynolds CA, Franz CE, Kremen WS, Elman JA. Associations of plasma neurofilament light chain with cognition and neuroimaging measures in community-dwelling early old age men. Alzheimers Res Ther 2024; 16:90. [PMID: 38664843 PMCID: PMC11044425 DOI: 10.1186/s13195-024-01464-1] [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: 10/31/2023] [Accepted: 04/21/2024] [Indexed: 04/28/2024]
Abstract
BACKGROUND Plasma neurofilament light chain (NfL) is a promising biomarker of neurodegeneration with potential clinical utility in monitoring the progression of neurodegenerative diseases. However, the cross-sectional associations of plasma NfL with measures of cognition and brain have been inconsistent in community-dwelling populations. METHODS We examined these associations in a large community-dwelling sample of early old age men (N = 969, mean age = 67.57 years, range = 61-73 years), who are either cognitively unimpaired (CU) or with mild cognitive impairment (MCI). Specifically, we investigated five cognitive domains (executive function, episodic memory, verbal fluency, processing speed, visual-spatial ability), as well as neuroimaging measures of gray and white matter. RESULTS After adjusting for age, health status, and young adult general cognitive ability, plasma NfL level was only significantly associated with processing speed and white matter hyperintensity (WMH) volume, but not with other cognitive or neuroimaging measures. The association with processing speed was driven by individuals with MCI, as it was not detected in CU individuals. CONCLUSIONS These results suggest that in early old age men without dementia, plasma NfL does not appear to be sensitive to cross-sectional individual differences in most domains of cognition or neuroimaging measures of gray and white matter. The revealed plasma NfL associations were limited to WMH for all participants and processing speed only within the MCI cohort. Importantly, considering cognitive status in community-based samples will better inform the interpretation of the relationships of plasma NfL with cognition and brain and may help resolve mixed findings in the literature.
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Affiliation(s)
- Rongxiang Tang
- Department of Psychiatry, University of California San Diego, La Jolla, 92093, USA.
- Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, 92093, USA.
| | - Erik Buchholz
- Department of Psychiatry, University of California San Diego, La Jolla, 92093, USA
- Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, 92093, USA
| | - Anders M Dale
- Department of Radiology, University of California San Diego, La Jolla, 92093, USA
- Department of Neurosciences, University of California San Diego, La Jolla, 92093, USA
| | - Robert A Rissman
- Department of Neurosciences, University of California San Diego, La Jolla, 92093, USA
| | - Christine Fennema-Notestine
- Department of Psychiatry, University of California San Diego, La Jolla, 92093, USA
- Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, 92093, USA
- Department of Radiology, University of California San Diego, La Jolla, 92093, USA
| | - Nathan A Gillespie
- Department of Psychiatry, Virginia Institute for Psychiatric and Behavior Genetics, Virginia Commonwealth University, Richmond, VA, 23284, USA
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Donald J Hagler
- Department of Neurosciences, University of California San Diego, La Jolla, 92093, USA
| | - Michael J Lyons
- Department of Psychological and Brain Sciences, Boston University, Boston, 02215, USA
| | - Michael C Neale
- Department of Psychiatry, Virginia Institute for Psychiatric and Behavior Genetics, Virginia Commonwealth University, Richmond, VA, 23284, USA
| | - Matthew S Panizzon
- Department of Psychiatry, University of California San Diego, La Jolla, 92093, USA
- Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, 92093, USA
| | - Olivia K Puckett
- Department of Psychiatry, University of California San Diego, La Jolla, 92093, USA
- Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, 92093, USA
| | - Chandra A Reynolds
- Department of Psychology and Neurosciences, University of Colorado Boulder, Boulder, 80309, USA
| | - Carol E Franz
- Department of Psychiatry, University of California San Diego, La Jolla, 92093, USA
- Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, 92093, USA
| | - William S Kremen
- Department of Psychiatry, University of California San Diego, La Jolla, 92093, USA
- Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, 92093, USA
| | - Jeremy A Elman
- Department of Psychiatry, University of California San Diego, La Jolla, 92093, USA
- Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, 92093, USA
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Andargoli AE, Ulapane N, Nguyen TA, Shuakat N, Zelcer J, Wickramasinghe N. Intelligent decision support systems for dementia care: A scoping review. Artif Intell Med 2024; 150:102815. [PMID: 38553156 DOI: 10.1016/j.artmed.2024.102815] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Revised: 12/11/2023] [Accepted: 02/12/2024] [Indexed: 04/02/2024]
Abstract
In the context of dementia care, Artificial Intelligence (AI) powered clinical decision support systems have the potential to enhance diagnosis and management. However, the scope and challenges of applying these technologies remain unclear. This scoping review aims to investigate the current state of AI applications in the development of intelligent decision support systems for dementia care. We conducted a comprehensive scoping review of empirical studies that utilised AI-powered clinical decision support systems in dementia care. The results indicate that AI applications in dementia care primarily focus on diagnosis, with limited attention to other aspects outlined in the World Health Organization (WHO) Global Action Plan on the Public Health Response to Dementia 2017-2025 (GAPD). A trifecta of challenges, encompassing data availability, cost considerations, and AI algorithm performance, emerges as noteworthy barriers in adoption of AI applications in dementia care. To address these challenges and enhance AI reliability, we propose a novel approach: a digital twin-based patient journey model. Future research should address identified gaps in GAPD action areas, navigate data-related obstacles, and explore the implementation of digital twins. Additionally, it is imperative to emphasize that addressing trust and combating the stigma associated with AI in healthcare should be a central focus of future research directions.
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Affiliation(s)
| | | | - Tuan Anh Nguyen
- Swinburne University of Technology, Melbourne, Australia; National Ageing Research Institute, Australia
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Tang Y, Xiong X, Tong G, Yang Y, Zhang H. Multimodal diagnosis model of Alzheimer's disease based on improved Transformer. Biomed Eng Online 2024; 23:8. [PMID: 38243275 PMCID: PMC10799436 DOI: 10.1186/s12938-024-01204-4] [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: 04/05/2023] [Accepted: 01/08/2024] [Indexed: 01/21/2024] Open
Abstract
PURPOSE Recent technological advancements in data acquisition tools allowed neuroscientists to acquire different modality data to diagnosis Alzheimer's disease (AD). However, how to fuse these enormous amount different modality data to improve recognizing rate and find significance brain regions is still challenging. METHODS The algorithm used multimodal medical images [structural magnetic resonance imaging (sMRI) and positron emission tomography (PET)] as experimental data. Deep feature representations of sMRI and PET images are extracted by 3D convolution neural network (3DCNN). An improved Transformer is then used to progressively learn global correlation information among features. Finally, the information from different modalities is fused for identification. A model-based visualization method is used to explain the decisions of the model and identify brain regions related to AD. RESULTS The model attained a noteworthy classification accuracy of 98.1% for Alzheimer's disease (AD) using the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Upon examining the visualization results, distinct brain regions associated with AD diagnosis were observed across different image modalities. Notably, the left parahippocampal region emerged consistently as a prominent and significant brain area. CONCLUSIONS A large number of comparative experiments have been carried out for the model, and the experimental results verify the reliability of the model. In addition, the model adopts a visualization analysis method based on the characteristics of the model, which improves the interpretability of the model. Some disease-related brain regions were found in the visualization results, which provides reliable information for AD clinical research.
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Affiliation(s)
- Yan Tang
- School of Electronic Information, Central South University, Changsha, 410008, Hunan, People's Republic of China
- Guangxi Key Lab of Multi-source Information Mining & Security, Guangxi Normal University, Guilin, 541004, Guangxi, People's Republic of China
| | - Xing Xiong
- School of Computer Science and Engineering, Central South University, Changsha, 410008, Hunan, People's Republic of China
| | - Gan Tong
- School of Computer Science and Engineering, Central South University, Changsha, 410008, Hunan, People's Republic of China
| | - Yuan Yang
- Department of Bioengineering, University of Illinois Urbana-Champaign, Grainger College of Engineering, Urbana, IL, USA
| | - Hao Zhang
- School of Electronic Information, Central South University, Changsha, 410008, Hunan, People's Republic of China.
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Bucholc M, James C, Khleifat AA, Badhwar A, Clarke N, Dehsarvi A, Madan CR, Marzi SJ, Shand C, Schilder BM, Tamburin S, Tantiangco HM, Lourida I, Llewellyn DJ, Ranson JM. Artificial intelligence for dementia research methods optimization. Alzheimers Dement 2023; 19:5934-5951. [PMID: 37639369 DOI: 10.1002/alz.13441] [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: 04/03/2023] [Revised: 07/19/2023] [Accepted: 07/23/2023] [Indexed: 08/31/2023]
Abstract
Artificial intelligence (AI) and machine learning (ML) approaches are increasingly being used in dementia research. However, several methodological challenges exist that may limit the insights we can obtain from high-dimensional data and our ability to translate these findings into improved patient outcomes. To improve reproducibility and replicability, researchers should make their well-documented code and modeling pipelines openly available. Data should also be shared where appropriate. To enhance the acceptability of models and AI-enabled systems to users, researchers should prioritize interpretable methods that provide insights into how decisions are generated. Models should be developed using multiple, diverse datasets to improve robustness, generalizability, and reduce potentially harmful bias. To improve clarity and reproducibility, researchers should adhere to reporting guidelines that are co-produced with multiple stakeholders. If these methodological challenges are overcome, AI and ML hold enormous promise for changing the landscape of dementia research and care. HIGHLIGHTS: Machine learning (ML) can improve diagnosis, prevention, and management of dementia. Inadequate reporting of ML procedures affects reproduction/replication of results. ML models built on unrepresentative datasets do not generalize to new datasets. Obligatory metrics for certain model structures and use cases have not been defined. Interpretability and trust in ML predictions are barriers to clinical translation.
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Affiliation(s)
- Magda Bucholc
- Cognitive Analytics Research Lab, School of Computing, Engineering & Intelligent Systems, Ulster University, Derry, UK
| | - Charlotte James
- NIHR Bristol Biomedical Research Centre, University Hospitals Bristol and Weston NHS Foundation Trust and University of Bristol, Bristol, UK
| | - Ahmad Al Khleifat
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - AmanPreet Badhwar
- Multiomics Investigation of Neurodegenerative Diseases (MIND) Lab, Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montréal, Quebec, Canada
- Institut de génie biomédical, Université de Montréal, Montréal, Quebec, Canada
- Département de Pharmacologie et Physiologie, Université de Montréal, Montréal, Quebec, Canada
| | - Natasha Clarke
- Multiomics Investigation of Neurodegenerative Diseases (MIND) Lab, Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montréal, Quebec, Canada
| | - Amir Dehsarvi
- Aberdeen Biomedical Imaging Centre, School of Medicine, Medical Sciences, and Nutrition, University of Aberdeen, Aberdeen, UK
| | | | - Sarah J Marzi
- UK Dementia Research Institute, Imperial College London, London, UK
- Department of Brain Sciences, Imperial College London, London, UK
| | - Cameron Shand
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Brian M Schilder
- UK Dementia Research Institute, Imperial College London, London, UK
- Department of Brain Sciences, Imperial College London, London, UK
| | - Stefano Tamburin
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
| | | | | | - David J Llewellyn
- University of Exeter Medical School, Exeter, UK
- The Alan Turing Institute, London, UK
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Reas ET, Shadrin A, Frei O, Motazedi E, McEvoy L, Bahrami S, van der Meer D, Makowski C, Loughnan R, Wang X, Broce I, Banks SJ, Fominykh V, Cheng W, Holland D, Smeland OB, Seibert T, Selbæk G, Brewer JB, Fan CC, Andreassen OA, Dale AM. Improved multimodal prediction of progression from MCI to Alzheimer's disease combining genetics with quantitative brain MRI and cognitive measures. Alzheimers Dement 2023; 19:5151-5158. [PMID: 37132098 PMCID: PMC10620101 DOI: 10.1002/alz.13112] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 03/21/2023] [Accepted: 04/04/2023] [Indexed: 05/04/2023]
Abstract
INTRODUCTION There is a pressing need for non-invasive, cost-effective tools for early detection of Alzheimer's disease (AD). METHODS Using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), Cox proportional models were conducted to develop a multimodal hazard score (MHS) combining age, a polygenic hazard score (PHS), brain atrophy, and memory to predict conversion from mild cognitive impairment (MCI) to dementia. Power calculations estimated required clinical trial sample sizes after hypothetical enrichment using the MHS. Cox regression determined predicted age of onset for AD pathology from the PHS. RESULTS The MHS predicted conversion from MCI to dementia (hazard ratio for 80th versus 20th percentile: 27.03). Models suggest that application of the MHS could reduce clinical trial sample sizes by 67%. The PHS alone predicted age of onset of amyloid and tau. DISCUSSION The MHS may improve early detection of AD for use in memory clinics or for clinical trial enrichment. HIGHLIGHTS A multimodal hazard score (MHS) combined age, genetics, brain atrophy, and memory. The MHS predicted time to conversion from mild cognitive impairment to dementia. MHS reduced hypothetical Alzheimer's disease (AD) clinical trial sample sizes by 67%. A polygenic hazard score predicted age of onset of AD neuropathology.
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Affiliation(s)
- Emilie T. Reas
- Department of Neurosciences, University of California San Diego, La Jolla, CA 92093, USA
| | - Alexey Shadrin
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, 0407 Oslo, Norway
| | - Oleksandr Frei
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, 0407 Oslo, Norway
- Center for Bioinformatics, Department of Informatics, University of Oslo, PO box 1080, Blindern, 0316 Oslo, Norway
| | - Ehsan Motazedi
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, 0407 Oslo, Norway
| | - Linda McEvoy
- Department of Radiology, University of California, San Diego, La Jolla, CA 92093, USA
| | - Shahram Bahrami
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, 0407 Oslo, Norway
| | - Dennis van der Meer
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, 0407 Oslo, Norway
| | - Carolina Makowski
- Department of Radiology, University of California, San Diego, La Jolla, CA 92093, USA
| | - Robert Loughnan
- University of California, San Diego, La Jolla, California, USA
| | - Xin Wang
- Department of Neurosciences, University of California San Diego, La Jolla, CA 92093, USA
| | - Iris Broce
- Department of Neurosciences, University of California San Diego, La Jolla, CA 92093, USA
| | - Sarah J. Banks
- Department of Neurosciences, University of California San Diego, La Jolla, CA 92093, USA
| | - Vera Fominykh
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, 0407 Oslo, Norway
| | - Weiqiu Cheng
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, 0407 Oslo, Norway
| | - Dominic Holland
- Department of Neurosciences, University of California San Diego, La Jolla, CA 92093, USA
| | - Olav B. Smeland
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, 0407 Oslo, Norway
| | - Tyler Seibert
- Department of Radiology, University of California, San Diego, La Jolla, CA 92093, USA
| | | | - James B. Brewer
- Department of Neurosciences, University of California San Diego, La Jolla, CA 92093, USA
| | - Chun C. Fan
- Population Neuroscience and Genetics Lab, University of California, La Jolla, CA 92093, USA
- Center for Human Development, University of California, San Diego, La Jolla, CA 92093, USA
- Department of Radiology, University of California, San Diego, La Jolla, CA 92093, USA
| | - Ole A. Andreassen
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, 0407 Oslo, Norway
| | - Anders M. Dale
- Department of Neurosciences, University of California San Diego, La Jolla, CA 92093, USA
- Population Neuroscience and Genetics Lab, University of California, La Jolla, CA 92093, USA
- Department of Radiology, University of California, San Diego, La Jolla, CA 92093, USA
- Department of Psychiatry, University of California, San Diego, La Jolla, CA 92093, USA
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Cheng Y, Zhang XD, Chen C, He LF, Li FF, Lu ZN, Man WQ, Zhao YJ, Chang ZX, Wu Y, Shen W, Fan LZ, Xu JH. Dynamic evolution of brain structural patterns in liver transplantation recipients: a longitudinal study based on 3D convolutional neuronal network model. Eur Radiol 2023; 33:6134-6144. [PMID: 37014408 DOI: 10.1007/s00330-023-09604-1] [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: 09/17/2022] [Revised: 02/19/2023] [Accepted: 02/24/2023] [Indexed: 04/05/2023]
Abstract
OBJECTIVES To evaluate the dynamic evolution process of overall brain health in liver transplantation (LT) recipients, we employed a deep learning-based neuroanatomic biomarker to measure longitudinal changes of brain structural patterns before and 1, 3, and 6 months after surgery. METHODS Because of the ability to capture patterns across all voxels from a brain scan, the brain age prediction method was adopted. We constructed a 3D-CNN model through T1-weighted MRI of 3609 healthy individuals from 8 public datasets and further applied it to a local dataset of 60 LT recipients and 134 controls. The predicted age difference (PAD) was calculated to estimate brain changes before and after LT, and the network occlusion sensitivity analysis was used to determine the importance of each network in age prediction. RESULTS The PAD of patients with cirrhosis increased markedly at baseline (+ 5.74 years) and continued to increase within one month after LT (+ 9.18 years). After that, the brain age began to decrease gradually, but it was still higher than the chronological age. The PAD values of the OHE subgroup were higher than those of the no-OHE, and the discrepancy was more obvious at 1-month post-LT. High-level cognition-related networks were more important in predicting the brain age of patients with cirrhosis at baseline, while the importance of primary sensory networks increased temporarily within 6-month post-LT. CONCLUSIONS The brain structural patterns of LT recipients showed inverted U-shaped dynamic change in the early stage after transplantation, and the change in primary sensory networks may be the main contributor. KEY POINTS • The recipients' brain structural pattern showed an inverted U-shaped dynamic change after LT. • The patients' brain aging aggravated within 1 month after surgery, and the subset of patients with a history of OHE was particularly affected. • The change of primary sensory networks is the main contributor to the change in brain structural patterns.
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Affiliation(s)
- Yue Cheng
- Department of Radiology, Tianjin First Central Hospital, Tianjin, China
- College of Intelligence and Computing, Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin University, Tianjin, China
| | - Xiao-Dong Zhang
- Department of Radiology, Tianjin First Central Hospital, Tianjin, China
- College of Intelligence and Computing, Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin University, Tianjin, China
| | - Cheng Chen
- College of Intelligence and Computing, Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin University, Tianjin, China
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Ling-Fei He
- College of Intelligence and Computing, Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin University, Tianjin, China
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Fang-Fei Li
- Department of Radiology, Tianjin First Central Hospital, Tianjin, China
| | - Zi-Ning Lu
- Department of Radiology, Tianjin First Central Hospital, Tianjin, China
| | - Wei-Qi Man
- Department of Radiology, Tianjin First Central Hospital, Tianjin, China
| | - Yu-Jiao Zhao
- Department of Radiology, Tianjin First Central Hospital, Tianjin, China
| | | | - Ying Wu
- School of Statistics and Data Science, Key Laboratory for Medical Data Analysis and Statistical Research of Tianjin, Nankai University, Tianjin, China
| | - Wen Shen
- Department of Radiology, Tianjin First Central Hospital, Tianjin, China
| | - Ling-Zhong Fan
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Jun-Hai Xu
- College of Intelligence and Computing, Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin University, Tianjin, China.
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10
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Williams ME, Elman JA, Bell TR, Dale AM, Eyler LT, Fennema-Notestine C, Franz CE, Gillespie NA, Hagler DJ, Lyons MJ, McEvoy LK, Neale MC, Panizzon MS, Reynolds CA, Sanderson-Cimino M, Kremen WS. Higher cortical thickness/volume in Alzheimer's-related regions: protective factor or risk factor? Neurobiol Aging 2023; 129:185-194. [PMID: 37343448 PMCID: PMC10676195 DOI: 10.1016/j.neurobiolaging.2023.05.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 04/18/2023] [Accepted: 05/03/2023] [Indexed: 06/23/2023]
Abstract
Some evidence suggests a biphasic pattern of changes in cortical thickness wherein higher, rather than lower, thickness is associated with very early Alzheimer's disease (AD) pathology. We examined whether integrating information from AD brain signatures based on mean diffusivity (MD) can aid in the interpretation of cortical thickness/volume as a risk factor for future AD-related changes. Participants were 572 men in the Vietnam Era Twin Study of Aging who were cognitively unimpaired at baseline (mean age = 56 years; range = 51-60). Individuals with both high thickness/volume signatures and high MD signatures at baseline had lower cortical thickness/volume in AD signature regions and lower episodic memory performance 12 years later compared to those with high thickness/volume and low MD signatures at baseline. Groups did not differ in level of young adult cognitive reserve. Our findings are in line with a biphasic model in which increased cortical thickness may precede future decline and establish the value of examining cortical MD alongside cortical thickness to identify subgroups with differential risk for poorer brain and cognitive outcomes.
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Affiliation(s)
- McKenna E Williams
- Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA, USA; Joint Doctoral Program in Clinical Psychology, San Diego State University/University of California, San Diego, CA, USA; Department of Psychiatry, University of California San Diego, La Jolla, CA, USA.
| | - Jeremy A Elman
- Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA, USA; Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Tyler R Bell
- Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA, USA; Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Anders M Dale
- Department of Radiology, University of California San Diego, La Jolla, CA, USA; Department of Neuroscience, University of California San Diego, La Jolla, CA, USA
| | - Lisa T Eyler
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA; Desert Pacific Mental Illness Research Education and Clinical Center, VA San Diego Healthcare System, San Diego, CA, USA
| | - Christine Fennema-Notestine
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA; Department of Radiology, University of California San Diego, La Jolla, CA, USA
| | - Carol E Franz
- Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA, USA; Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Nathan A Gillespie
- Virginia Institute for Psychiatric and Behavior Genetics, Virginia Commonwealth University, Richmond, VA, USA
| | - Donald J Hagler
- Department of Radiology, University of California San Diego, La Jolla, CA, USA
| | - Michael J Lyons
- Department of Psychological and Brain Sciences, Boston University, Boston, MA, USA
| | - Linda K McEvoy
- Department of Radiology, University of California San Diego, La Jolla, CA, USA
| | - Michael C Neale
- Virginia Institute for Psychiatric and Behavior Genetics, Virginia Commonwealth University, Richmond, VA, USA
| | - Matthew S Panizzon
- Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA, USA; Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Chandra A Reynolds
- Department of Psychology, University of California Riverside, Riverside, CA, USA
| | - Mark Sanderson-Cimino
- Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA, USA; Joint Doctoral Program in Clinical Psychology, San Diego State University/University of California, San Diego, CA, USA; Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - William S Kremen
- Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA, USA; Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
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11
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Williams ME, Gillespie NA, Bell TR, Dale AM, Elman JA, Eyler LT, Fennema-Notestine C, Franz CE, Hagler DJ, Lyons MJ, McEvoy LK, Neale MC, Panizzon MS, Reynolds CA, Sanderson-Cimino M, Kremen WS. Genetic and Environmental Influences on Structural and Diffusion-Based Alzheimer's Disease Neuroimaging Signatures Across Midlife and Early Old Age. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2023; 8:918-927. [PMID: 35738479 PMCID: PMC9827615 DOI: 10.1016/j.bpsc.2022.06.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 05/04/2022] [Accepted: 06/07/2022] [Indexed: 01/27/2023]
Abstract
BACKGROUND Composite scores of magnetic resonance imaging-derived metrics in brain regions associated with Alzheimer's disease (AD), commonly termed AD signatures, have been developed to distinguish early AD-related atrophy from normal age-associated changes. Diffusion-based gray matter signatures may be more sensitive to early AD-related changes compared with thickness/volume-based signatures, demonstrating their potential clinical utility. The timing of early (i.e., midlife) changes in AD signatures from different modalities and whether diffusion- and thickness/volume-based signatures each capture unique AD-related phenotypic or genetic information remains unknown. METHODS Our validated thickness/volume signature, our novel mean diffusivity (MD) signature, and a magnetic resonance imaging-derived measure of brain age were used in biometrical analyses to examine genetic and environmental influences on the measures as well as phenotypic and genetic relationships between measures over 12 years. Participants were 736 men from 3 waves of the Vietnam Era Twin Study of Aging (VETSA) (baseline/wave 1: mean age [years] = 56.1, SD = 2.6, range = 51.1-60.2). Subsequent waves occurred at approximately 5.7-year intervals. RESULTS MD and thickness/volume signatures were highly heritable (56%-72%). Baseline MD signatures predicted thickness/volume signatures over a decade later, but baseline thickness/volume signatures showed a significantly weaker relationship with future MD signatures. AD signatures and brain age were correlated, but each measure captured unique phenotypic and genetic variance. CONCLUSIONS Cortical MD and thickness/volume AD signatures are heritable, and each signature captures unique variance that is also not explained by brain age. Moreover, results are in line with changes in MD emerging before changes in cortical thickness, underscoring the utility of MD as a very early predictor of AD risk.
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Affiliation(s)
- McKenna E Williams
- Center for Behavior Genetics of Aging, University of California San Diego, San Diego, California; Department of Psychiatry, University of California San Diego, San Diego, California; Joint Doctoral Program in Clinical Psychology, San Diego State University/University of California San Diego, San Diego, California.
| | - Nathan A Gillespie
- Virginia Institute for Psychiatric and Behavior Genetics, Virginia Commonwealth University, Richmond, Virginia
| | - Tyler R Bell
- Center for Behavior Genetics of Aging, University of California San Diego, San Diego, California; Department of Psychiatry, University of California San Diego, San Diego, California
| | - Anders M Dale
- Department of Radiology, University of California San Diego, San Diego, California; Department of Neuroscience, University of California San Diego, San Diego, California
| | - Jeremy A Elman
- Center for Behavior Genetics of Aging, University of California San Diego, San Diego, California; Department of Psychiatry, University of California San Diego, San Diego, California
| | - Lisa T Eyler
- Desert Pacific Mental Illness Research Education and Clinical Center, VA San Diego Healthcare System, San Diego, California
| | - Christine Fennema-Notestine
- Department of Psychiatry, University of California San Diego, San Diego, California; Department of Radiology, University of California San Diego, San Diego, California
| | - Carol E Franz
- Center for Behavior Genetics of Aging, University of California San Diego, San Diego, California; Department of Psychiatry, University of California San Diego, San Diego, California
| | - Donald J Hagler
- Department of Radiology, University of California San Diego, San Diego, California
| | - Michael J Lyons
- Department of Psychological and Brain Sciences, Boston University, Boston, Massachusetts
| | - Linda K McEvoy
- Department of Radiology, University of California San Diego, San Diego, California
| | - Michael C Neale
- Virginia Institute for Psychiatric and Behavior Genetics, Virginia Commonwealth University, Richmond, Virginia
| | - Matthew S Panizzon
- Center for Behavior Genetics of Aging, University of California San Diego, San Diego, California; Department of Psychiatry, University of California San Diego, San Diego, California
| | - Chandra A Reynolds
- Department of Psychology, University of California Riverside, Riverside, California
| | - Mark Sanderson-Cimino
- Center for Behavior Genetics of Aging, University of California San Diego, San Diego, California; Department of Psychiatry, University of California San Diego, San Diego, California; Joint Doctoral Program in Clinical Psychology, San Diego State University/University of California San Diego, San Diego, California
| | - William S Kremen
- Center for Behavior Genetics of Aging, University of California San Diego, San Diego, California; Department of Psychiatry, University of California San Diego, San Diego, California
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12
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Bauer CE, Zachariou V, Sudduth TL, Van Eldik LJ, Jicha GA, Nelson PT, Wilcock DM, Gold BT. Plasma TDP-43 levels are associated with neuroimaging measures of brain structure in limbic regions. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2023; 15:e12437. [PMID: 37266411 PMCID: PMC10230689 DOI: 10.1002/dad2.12437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 04/10/2023] [Accepted: 04/11/2023] [Indexed: 06/03/2023]
Abstract
Introduction We evaluated the relationship between plasma levels of transactive response DNA binding protein of 43 kDa (TDP-43) and neuroimaging (magnetic resonance imaging [MRI]) measures of brain structure in aging. Methods Plasma samples were collected from 72 non-demented older adults (age range 60-94 years) in the University of Kentucky Alzheimer's Disease Research Center cohort. Multivariate linear regression models were run with plasma TDP-43 level as the predictor variable and brain structure (volumetric or cortical thickness) measurements as the dependent variable. Covariates included age, sex, intracranial volume, and plasma markers of Alzheimer's disease neuropathological change (ADNC). Results Negative associations were observed between plasma TDP-43 level and both the volume of the entorhinal cortex, and cortical thickness in the cingulate/parahippocampal gyrus, after controlling for ADNC plasma markers. Discussion Plasma TDP-43 levels may be directly associated with structural MRI measures. Plasma TDP-43 assays may prove useful in clinical trial stratification. HIGHLIGHTS Plasma transactive response DNA binding protein of 43 kDa (TDP-43) levels were associated with entorhinal cortex volume.Biomarkers of TDP-43 and Alzheimer's disease neuropathologic change (ADNC) may help distinguish limbic-predominant age-related TDP-43 encephalopathy neuropathologic change (LATE-NC) from ADNC.A comprehensive biomarker kit could aid enrollment in LATE-NC clinical trials.
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Affiliation(s)
| | | | | | - Linda J. Van Eldik
- Department of NeuroscienceUniversity of KentuckyLexingtonKentuckyUSA
- Sanders‐Brown Center on AgingUniversity of KentuckyLexingtonKentuckyUSA
| | - Gregory A. Jicha
- Sanders‐Brown Center on AgingUniversity of KentuckyLexingtonKentuckyUSA
- Department of NeurologyUniversity of KentuckyLexingtonKentuckyUSA
| | - Peter T. Nelson
- Sanders‐Brown Center on AgingUniversity of KentuckyLexingtonKentuckyUSA
- Department of Pathology and Laboratory MedicineUniversity of KentuckyLexingtonKentuckyUSA
| | - Donna M. Wilcock
- Sanders‐Brown Center on AgingUniversity of KentuckyLexingtonKentuckyUSA
- Department of PhysiologyUniversity of KentuckyLexingtonKentuckyUSA
| | - Brian T. Gold
- Department of NeuroscienceUniversity of KentuckyLexingtonKentuckyUSA
- Sanders‐Brown Center on AgingUniversity of KentuckyLexingtonKentuckyUSA
- Department of RadiologyUniversity of KentuckyLexingtonKentuckyUSA
- Magnetic Resonance Imaging and Spectroscopy CenterUniversity of KentuckyLexingtonKentuckyUSA
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13
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Yoon EJ, Lee JY, Kwak S, Kim YK. Mild behavioral impairment linked to progression to Alzheimer's disease and cortical thinning in amnestic mild cognitive impairment. Front Aging Neurosci 2023; 14:1051621. [PMID: 36688162 PMCID: PMC9846631 DOI: 10.3389/fnagi.2022.1051621] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Accepted: 11/28/2022] [Indexed: 01/06/2023] Open
Abstract
Background Mild behavioral impairment (MBI) is a neurobehavioral syndrome characterized by later life emergence of sustained neuropsychiatric symptoms, as an at-risk state for dementia. However, the associations between MBI and a risk of progression to Alzheimer's disease (AD) and its neuroanatomical correlates in mild cognitive impairment (MCI) are still unclear. Method A total 1,184 older adults with amnestic MCI was followed for a mean of 3.1 ± 2.0 years. MBI was approximated using a transformation algorithm for the Neuropsychiatric Inventory at baseline. A two-step cluster analysis was used to identify subgroups of individuals with amnestic MCI based on profiles of 5 MBI domain symptoms (decreased motivation, affective dysregulation, impulse dyscontrol, social inappropriateness, abnormal perception/thought content). A Cox regression analysis was applied to investigate differences in the risk of progression to AD between subgroups. A subset of participants (n = 202) underwent 3D T1-weighted MRI scans at baseline and cortical thickness was compared between the subgroups of amnestic MCI patients. Result The cluster analysis classified the patients into 3 groups: (1) patients without any MBI domain symptoms (47.4%, asymptomatic group); (2) those with only affective dysregulation (29.4%, affective dysregulation group); (3) those with multiple MBI domain symptoms, particularly affective dysregulation, decreased motivation and impulse dyscontrol (23.2%, complex group). Compared to the asymptomatic group, the complex group was associated with a higher risk of progression to AD (hazard ratio = 2.541 [1.904-3.392], p < 0.001), but the affective dysregulation group was not (1.214 [0.883-1.670], p = 0.232). In cortical thickness analysis, the complex group revealed cortical thinning bilaterally in the inferior parietal, lateral occipital, lateral superior temporal, and frontopolar regions compared with the affective dysregulation group. Conclusion The multiple co-occuring MBI domains in individuals with amnestic MCI are associated with a higher risk of progression to AD and cortical thinning in temporal, parietal and frontal areas. These results suggest that evaluation of MBI could be useful for risk stratification for AD and appropriate intervention in MCI individuals.
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Affiliation(s)
- Eun Jin Yoon
- Memory Network Medical Research Center, Seoul National University, Seoul, South Korea,Department of Nuclear Medicine, SMG-SNU Boramae Medical Center, Seoul, South Korea
| | - Jun-Young Lee
- Department of Psychiatry, SMG-SNU Boramae Medical Center, Seoul, South Korea,Department of Psychiatry, Seoul National University College of Medicine, Seoul, South Korea,Department of Medical Device Development, Seoul National University College of Medicine, Seoul, South Korea
| | - Seyul Kwak
- Department of Psychology, Pusan National University, Busan, South Korea
| | - Yu Kyeong Kim
- Department of Nuclear Medicine, SMG-SNU Boramae Medical Center, Seoul, South Korea,Department of Nuclear Medicine, Seoul National University College of Medicine, Seoul, South Korea,*Correspondence: Yu Kyeong Kim,
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14
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Pölsterl S, Wachinger C. Identification of causal effects of neuroanatomy on cognitive decline requires modeling unobserved confounders. Alzheimers Dement 2022; 19:1994-2005. [PMID: 36419215 DOI: 10.1002/alz.12825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Revised: 09/16/2022] [Accepted: 09/19/2022] [Indexed: 11/27/2022]
Abstract
INTRODUCTION Carrying out a randomized controlled trial to estimate the causal effects of regional brain atrophy due to Alzheimer's disease (AD) is impossible. Instead, we must estimate causal effects from observational data. However, this generally requires knowing and having recorded all confounders, which is often unrealistic. METHODS We provide an approach that leverages the dependencies among multiple neuroanatomical measures to estimate causal effects from observational neuroimaging data without the need to know and record all confounders. RESULTS Our analyses of N = 732 $N=732$ subjects from the Alzheimer's Disease Neuroimaging Initiative demonstrate that using our approach results in biologically meaningful conclusions, whereas ignoring unobserved confounding yields results that conflict with established knowledge on cognitive decline due to AD. DISCUSSION The findings provide evidence that the impact of unobserved confounding can be substantial. To ensure trustworthy scientific insights, future AD research can account for unobserved confounding via the proposed approach.
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Affiliation(s)
- Sebastian Pölsterl
- The Lab for Artificial Intelligence in Medical Imaging (AI-Med), Department of Child and Adolescent Psychiatry, Ludwig-Maximilians-Universität, Munich, Germany
| | - Christian Wachinger
- The Lab for Artificial Intelligence in Medical Imaging (AI-Med), Department of Child and Adolescent Psychiatry, Ludwig-Maximilians-Universität, Munich, Germany.,Technical University of Munich, School of Medicine, Department of Radiology, Munich, Germany
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15
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Wu X, Peng C, Nelson PT, Cheng Q. Deep learning algorithm reveals probabilities of stage-specific time to conversion in individuals with neurodegenerative disease LATE. ALZHEIMER'S & DEMENTIA (NEW YORK, N. Y.) 2022; 8:e12363. [PMID: 36348767 PMCID: PMC9632667 DOI: 10.1002/trc2.12363] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 09/27/2022] [Accepted: 10/04/2022] [Indexed: 11/06/2022]
Abstract
Introduction Limbic-predominant age-related TAR DNA-binding protein 43 (TDP-43) encephalopathy (LATE) is a recently defined neurodegenerative disease. Currently, there is no effective way to make a prognosis of time to stage-specific future conversions at an individual level. Methods After using the Kaplan-Meier estimation and log-rank test to confirm the heterogeneity of LATE progression, we developed a deep learning-based approach to assess the stage-specific probabilities of time to LATE conversions for different subjects. Results Our approach could accurately estimate the disease incidence and transition to next stages: the concordance index was at least 82% and the integrated Brier score was less than 0.14. Moreover, we identified the top 10 important predictors for each disease conversion scenario to help explain the estimation results, which were clinicopathologically meaningful and most were also statistically significant. Discussion Our study has the potential to provide individualized assessment for future time courses of LATE conversions years before their actual occurrence.
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Affiliation(s)
- Xinxing Wu
- Institute for Biomedical InformaticsUniversity of KentuckyLexingtonKentuckyUSA
| | - Chong Peng
- Department of Computer Science and EngineeringQingdao UniversityShandongChina
| | - Peter T. Nelson
- Sanders‐Brown Aging Center and Department of PathologyUniversity of KentuckyLexingtonKentuckyUSA
| | - Qiang Cheng
- Institute for Biomedical InformaticsUniversity of KentuckyLexingtonKentuckyUSA
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16
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Dong A, Zhang G, Liu J, Wei Z. Latent feature representation learning for Alzheimer's disease classification. Comput Biol Med 2022; 150:106116. [PMID: 36215848 DOI: 10.1016/j.compbiomed.2022.106116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 08/18/2022] [Accepted: 09/17/2022] [Indexed: 11/03/2022]
Abstract
Early detection and treatment of Alzheimer's Disease (AD) are significant. Recently, multi-modality imaging data have promoted the development of the automatic diagnosis of AD. This paper proposes a method based on latent feature fusion to make full use of multi-modality image data information. Specifically, we learn a specific projection matrix for each modality by introducing a binary label matrix and local geometry constraints and then project the original features of each modality into a low-dimensional target space. In this space, we fuse latent feature representations of different modalities for AD classification. The experimental results on Alzheimer's Disease Neuroimaging Initiative database demonstrate the proposed methods effectiveness in classifying AD.
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Affiliation(s)
- Aimei Dong
- Faculty of Computer Science and Technology,Qilu University of Technology(Shandong Academy of Sciences),Jinan, 250353, China.
| | - Guodong Zhang
- Faculty of Computer Science and Technology,Qilu University of Technology(Shandong Academy of Sciences),Jinan, 250353, China.
| | - Jian Liu
- Faculty of Computer Science and Technology,Qilu University of Technology(Shandong Academy of Sciences),Jinan, 250353, China.
| | - Zhonghe Wei
- Faculty of Computer Science and Technology,Qilu University of Technology(Shandong Academy of Sciences),Jinan, 250353, China.
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17
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Xu J, Guan X, Wen J, Zhang M, Xu X. Polygenic hazard score modified the relationship between hippocampal subfield atrophy and episodic memory in older adults. Front Aging Neurosci 2022; 14:943702. [PMID: 36389062 PMCID: PMC9659745 DOI: 10.3389/fnagi.2022.943702] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2022] [Accepted: 09/30/2022] [Indexed: 12/30/2023] Open
Abstract
BACKGROUND Understanding genetic influences on Alzheimer's disease (AD) may improve early identification. Polygenic hazard score (PHS) is associated with the age of AD onset and cognitive decline. It interacts with other risk factors, but the nature of such combined effects remains poorly understood. MATERIALS AND METHODS We examined the effect of genetic risk and hippocampal atrophy pattern on episodic memory in a sample of older adults ranging from cognitively normal to those diagnosed with AD using structural MRI. Participants included 51 memory unimpaired normal control (NC), 69 mild cognitive impairment (MCI), and 43 AD adults enrolled in the Alzheimer's Disease Neuroimaging Initiative (ADNI). Hierarchical linear regression analyses examined the main and interaction effects of hippocampal subfield volumes and PHS, indicating genetic risk for AD, on a validated episodic memory composite score. Diagnosis-stratified models further assessed the role of PHS. RESULTS Polygenic hazard score moderated the relationship between right fimbria/hippocampus volume ratio and episodic memory, such that patients with high PHS and lower volume ratio had lower episodic memory composite scores [ΔF = 6.730, p = 0.011, ΔR 2 = 0.059]. This effect was also found among individuals with MCI [ΔF = 4.519, p = 0.038, ΔR 2 = 0.050]. In contrast, no interaction effects were present for those NC or AD individuals. A follow-up mediation analysis also indicated that the right fimbria/hippocampus volume ratio might mediate the link between PHS and episodic memory performance in the MCI group, whereas no mediation effects were present for those NC or AD individuals. CONCLUSION These findings suggest that the interaction between AD genetic risk and hippocampal subfield volume ratio increases memory impairment among older adults. Also, the results highlighted a potential pathway in which genetic risk affects memory by degrading hippocampal subfield volume ratio in cognitive decline subjects.
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Affiliation(s)
| | | | | | | | - Xiaojun Xu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
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18
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Differential Diagnosis of Alzheimer Disease vs. Mild Cognitive Impairment Based on Left Temporal Lateral Lobe Hypomethabolism on 18F-FDG PET/CT and Automated Classifiers. Diagnostics (Basel) 2022; 12:diagnostics12102425. [PMID: 36292114 PMCID: PMC9601187 DOI: 10.3390/diagnostics12102425] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 09/22/2022] [Accepted: 10/04/2022] [Indexed: 11/17/2022] Open
Abstract
Purpose: We evaluate the ability of Artificial Intelligence with automatic classification methods applied to semi-quantitative data from brain 18F-FDG PET/CT to improve the differential diagnosis between Alzheimer Disease (AD) and Mild Cognitive Impairment (MCI). Procedures: We retrospectively analyzed a total of 150 consecutive patients who underwent diagnostic evaluation for suspected AD (n = 67) or MCI (n = 83). All patients received brain 18F-FDG PET/CT according to the international guidelines, and images were analyzed both Qualitatively (QL) and Quantitatively (QN), the latter by a fully automated post-processing software that produced a z score metabolic map of 25 anatomically different cortical regions. A subset of n = 122 cases with a confirmed diagnosis of AD (n = 53) or MDI (n = 69) by 18–24-month clinical follow-up was finally included in the study. Univariate analysis and three automated classification models (classification tree –ClT-, ridge classifier –RC- and linear Support Vector Machine –lSVM-) were considered to estimate the ability of the z scores to discriminate between AD and MCI cases in. Results: The univariate analysis returned 14 areas where the z scores were significantly different between AD and MCI groups, and the classification accuracy ranged between 74.59% and 76.23%, with ClT and RC providing the best results. The best classification strategy consisted of one single split with a cut-off value of ≈ −2.0 on the z score from temporal lateral left area: cases below this threshold were classified as AD and those above the threshold as MCI. Conclusions: Our findings confirm the usefulness of brain 18F-FDG PET/CT QL and QN analyses in differentiating AD from MCI. Moreover, the combined use of automated classifications models can improve the diagnostic process since its use allows identification of a specific hypometabolic area involved in AD cases in respect to MCI. This data improves the traditional 18F-FDG PET/CT image interpretation and the diagnostic assessment of cognitive disorders.
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Nogo D, Nazal H, Song Y, Teopiz KM, Ho R, McIntyre RS, Lui LMW, Rosenblat JD. A review of potential neuropathological changes associated with ketamine. Expert Opin Drug Saf 2022; 21:813-831. [PMID: 35502632 DOI: 10.1080/14740338.2022.2071867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
INTRODUCTION : Ketamine is an established intervention for treatment resistant depression (TRD). However, long-term adverse effects with repeated doses remain insufficiently characterized. Although several animal models have shown N-methyl-D-aspartate glutamate receptor antagonists to produce various neuropathological reactions, attention surrounding the risk of brain lesions has been minimal. AREAS COVERED : The current review focuses on potential neuropathological changes associated with ketamine. Search terms included variations of ketamine, Olney lesions, tau hyperphosphorylation, and parvalbumin interneurons. EXPERT OPINION : Daily high-dose ketamine use in substance use disorder (SUD) populations was associated with clear neurotoxic effects, while no studies specifically evaluated effects of ketamine protocols used for TRD. It is difficult to discern effects directly attributable to ketamine due to methodological factors, such as comorbidities and dramatic differences in dose in SUD populations versus infrequent sub-anesthetic doses typically prescribed for TRD. Taken together, animal models and human ketamine SUD populations suggest potential neuropathology with chronic high-dose ketamine exposure exceeding those recommended for adults with TRD. It is unknown whether repeat sub-anesthetic dosing of ketamine in adults with TRD is associated with Olney lesions or other neuropathologies. In the interim, practitioners should be vigilant for this possibility recognizing that the condition itself is associated with neurodegenerative processes.
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Affiliation(s)
- Danica Nogo
- Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, Canada
| | - Hana Nazal
- Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, Canada.,McMaster University, Hamilton, Canada
| | - Yuetong Song
- Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, Canada.,University of Toronto, Toronto, Canada
| | - Kayla M Teopiz
- Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, Canada.,University of Toronto, Toronto, Canada.,Brain and Cognition Discovery Foundation, Toronto, Canada
| | - Roger Ho
- Department of Psychological Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.,Institute for Health Innovation and Technology (iHealthtech), National University of Singapore, Singapore
| | - Roger S McIntyre
- Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, Canada.,University of Toronto, Toronto, Canada.,Brain and Cognition Discovery Foundation, Toronto, Canada
| | - Leanna M W Lui
- Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, Canada.,University of Toronto, Toronto, Canada
| | - Joshua D Rosenblat
- Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, Canada.,University of Toronto, Toronto, Canada.,Brain and Cognition Discovery Foundation, Toronto, Canada
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20
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Cai S, Yang F, Wang X, Wu S, Huang L. Structural brain characteristics and gene co-expression analysis: A study with outcome label from normal cognition to mild cognitive impairment. Neurobiol Learn Mem 2022; 191:107620. [DOI: 10.1016/j.nlm.2022.107620] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Revised: 02/15/2022] [Accepted: 04/04/2022] [Indexed: 10/18/2022]
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21
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Gillespie NA, Hatton SN, Hagler DJ, Dale AM, Elman JA, McEvoy LK, Eyler LT, Fennema-Notestine C, Logue MW, McKenzie RE, Puckett OK, Tu XM, Whitsel N, Xian H, Reynolds CA, Panizzon MS, Lyons MJ, Neale MC, Kremen WS, Franz C. The Impact of Genes and Environment on Brain Ageing in Males Aged 51 to 72 Years. Front Aging Neurosci 2022; 14:831002. [PMID: 35493948 PMCID: PMC9051484 DOI: 10.3389/fnagi.2022.831002] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Accepted: 03/15/2022] [Indexed: 01/27/2023] Open
Abstract
Magnetic resonance imaging data are being used in statistical models to predicted brain ageing (PBA) and as biomarkers for neurodegenerative diseases such as Alzheimer's Disease. Despite their increasing application, the genetic and environmental etiology of global PBA indices is unknown. Likewise, the degree to which genetic influences in PBA are longitudinally stable and how PBA changes over time are also unknown. We analyzed data from 734 men from the Vietnam Era Twin Study of Aging with repeated MRI assessments between the ages 51-72 years. Biometrical genetic analyses "twin models" revealed significant and highly correlated estimates of additive genetic heritability ranging from 59 to 75%. Multivariate longitudinal modeling revealed that covariation between PBA at different timepoints could be explained by a single latent factor with 73% heritability. Our results suggest that genetic influences on PBA are detectable in midlife or earlier, are longitudinally very stable, and are largely explained by common genetic influences.
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Affiliation(s)
- Nathan A. Gillespie
- Virginia Institute for Psychiatric and Behaviour Genetics, Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, United States,QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia,*Correspondence: Nathan A. Gillespie,
| | - Sean N. Hatton
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, United States,Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA, United States,Department of Neurosciences, University of California, San Diego, La Jolla, CA, United States
| | - Donald J. Hagler
- Department of Radiology, University of California, San Diego, La Jolla, CA, United States
| | - Anders M. Dale
- Department of Neurosciences, University of California, San Diego, La Jolla, CA, United States,Center for Multimodal Imaging and Genetics, University of California, San Diego, La Jolla, CA, United States,Halıcıoğlu Data Science Institute, University of California, San Diego, La Jolla, CA, United States
| | - Jeremy A. Elman
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, United States,Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA, United States
| | - Linda K. McEvoy
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, La Jolla, CA, United States
| | - Lisa T. Eyler
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, United States,Mental Illness Research Education and Clinical Center, VA San Diego Healthcare System, San Diego, CA, United States
| | - Christine Fennema-Notestine
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, United States,Department of Radiology, University of California, San Diego, La Jolla, CA, United States
| | - Mark W. Logue
- National Center for PTSD, VA Boston Healthcare System, Boston, MA, United States,Department of Psychiatry and Biomedical Genetics Section, Boston University School of Medicine, Boston, MA, United States,Department of Biostatistics, Boston University School of Public Health, Boston, MA, United States
| | - Ruth E. McKenzie
- Department of Psychology, Boston University, Boston, MA, United States,School of Education and Social Policy, Merrimack College, North Andover, MA, United States
| | - Olivia K. Puckett
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, United States,Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA, United States
| | - Xin M. Tu
- Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA, United States,Division of Biostatistics and Bioinformatics, Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, La Jolla, CA, United States
| | - Nathan Whitsel
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, United States,Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA, United States
| | - Hong Xian
- Department of Epidemiology and Biostatistics, Saint. Louis University, St. Louis, MO, United States,Research Service, VA St. Louis Healthcare System, St. Louis, MO, United States
| | - Chandra A. Reynolds
- Department of Psychology, University of California, Riverside, Riverside, CA, United States
| | - Matthew S. Panizzon
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, United States,Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA, United States
| | - Michael J. Lyons
- Department of Psychological and Brain Sciences, Boston University, Boston, MA, United States
| | - Michael C. Neale
- Virginia Institute for Psychiatric and Behaviour Genetics, Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, United States,Department of Biological Psychology, Free University of Amsterdam, Amsterdam, Netherlands
| | - William S. Kremen
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, United States,Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA, United States,Center of Excellence for Stress and Mental Health, VA San Diego Healthcare System, La Jolla, CA, United States,William S. Kremen,
| | - Carol Franz
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, United States,Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA, United States,Carol Franz,
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22
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Chen Y, Qian X, Zhang Y, Su W, Huang Y, Wang X, Chen X, Zhao E, Han L, Ma Y. Prediction Models for Conversion From Mild Cognitive Impairment to Alzheimer’s Disease: A Systematic Review and Meta-Analysis. Front Aging Neurosci 2022; 14:840386. [PMID: 35493941 PMCID: PMC9049273 DOI: 10.3389/fnagi.2022.840386] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 02/02/2022] [Indexed: 11/13/2022] Open
Abstract
Background and PurposeAlzheimer’s disease (AD) is a devastating neurodegenerative disorder with no cure, and available treatments are only able to postpone the progression of the disease. Mild cognitive impairment (MCI) is considered to be a transitional stage preceding AD. Therefore, prediction models for conversion from MCI to AD are desperately required. These will allow early treatment of patients with MCI before they develop AD. This study performed a systematic review and meta-analysis to summarize the reported risk prediction models and identify the most prevalent factors for conversion from MCI to AD.MethodsWe systematically reviewed the studies from the databases of PubMed, CINAHL Plus, Web of Science, Embase, and Cochrane Library, which were searched through September 2021. Two reviewers independently identified eligible articles and extracted the data. We used the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies (CHARMS) checklist for the risk of bias assessment.ResultsIn total, 18 articles describing the prediction models for conversion from MCI to AD were identified. The dementia conversion rate of elderly patients with MCI ranged from 14.49 to 87%. Models in 12 studies were developed using the data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). C-index/area under the receiver operating characteristic curve (AUC) of development models were 0.67–0.98, and the validation models were 0.62–0.96. MRI, apolipoprotein E genotype 4 (APOE4), older age, Mini-Mental State Examination (MMSE) score, and Alzheimer’s Disease Assessment Scale cognitive (ADAS-cog) score were the most common and strongest predictors included in the models.ConclusionIn this systematic review, many prediction models have been developed and have good predictive performance, but the lack of external validation of models limited the extensive application in the general population. In clinical practice, it is recommended that medical professionals adopt a comprehensive forecasting method rather than a single predictive factor to screen patients with a high risk of MCI. Future research should pay attention to the improvement, calibration, and validation of existing models while considering new variables, new methods, and differences in risk profiles across populations.
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Affiliation(s)
- Yanru Chen
- Evidence-Based Nursing, School of Nursing, Lanzhou University, Lanzhou, China
| | - Xiaoling Qian
- Department of Neurology, Second Hospital of Lanzhou University, Lanzhou, China
| | - Yuanyuan Zhang
- Evidence-Based Nursing, School of Nursing, Lanzhou University, Lanzhou, China
| | - Wenli Su
- Evidence-Based Nursing, School of Nursing, Lanzhou University, Lanzhou, China
| | - Yanan Huang
- Evidence-Based Nursing, School of Nursing, Lanzhou University, Lanzhou, China
| | - Xinyu Wang
- Evidence-Based Nursing, School of Nursing, Lanzhou University, Lanzhou, China
| | - Xiaoli Chen
- Evidence-Based Nursing, School of Nursing, Lanzhou University, Lanzhou, China
| | - Enhan Zhao
- Evidence-Based Nursing, School of Nursing, Lanzhou University, Lanzhou, China
| | - Lin Han
- Evidence-Based Nursing, School of Nursing, Lanzhou University, Lanzhou, China
- Department of Nursing, Gansu Provincial Hospital, Lanzhou, China
- *Correspondence: Yuxia Ma,
| | - Yuxia Ma
- Evidence-Based Nursing, School of Nursing, Lanzhou University, Lanzhou, China
- First School of Clinical Medicine, Lanzhou University, Lanzhou, China
- *Correspondence: Yuxia Ma,
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23
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Jin L, Zhao K, Zhao Y, Che T, Li S. A Hybrid Deep Learning Method for Early and Late Mild Cognitive Impairment Diagnosis With Incomplete Multimodal Data. Front Neuroinform 2022; 16:843566. [PMID: 35370588 PMCID: PMC8965366 DOI: 10.3389/fninf.2022.843566] [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: 12/26/2021] [Accepted: 02/21/2022] [Indexed: 11/13/2022] Open
Abstract
Multimodality neuroimages have been widely applied to diagnose mild cognitive impairment (MCI). However, the missing data problem is unavoidable. Most previously developed methods first train a generative adversarial network (GAN) to synthesize missing data and then train a classification network with the completed data. These methods independently train two networks with no information communication. Thus, the resulting GAN cannot focus on the crucial regions that are helpful for classification. To overcome this issue, we propose a hybrid deep learning method. First, a classification network is pretrained with paired MRI and PET images. Afterward, we use the pretrained classification network to guide a GAN by focusing on the features that are helpful for classification. Finally, we synthesize the missing PET images and use them with real MR images to fine-tune the classification model to make it better adapt to the synthesized images. We evaluate our proposed method on the ADNI dataset, and the results show that our method improves the accuracies obtained on the validation and testing sets by 3.84 and 5.82%, respectively. Moreover, our method increases the accuracies for the validation and testing sets by 7.7 and 9.09%, respectively, when we synthesize the missing PET images via our method. An ablation experiment shows that the last two stages are essential for our method. We also compare our method with other state-of-the-art methods, and our method achieves better classification performance.
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Affiliation(s)
- Leiming Jin
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Kun Zhao
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Yan Zhao
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Tongtong Che
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Shuyu Li
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China
- State Key Lab of Cognition Neuroscience and Learning, Beijing Normal University, Beijing, China
- *Correspondence: Shuyu Li,
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24
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Wu-Chung EL, Leal SL, Denny BT, Cheng SL, Fagundes CP. Spousal caregiving, widowhood, and cognition: A systematic review and a biopsychosocial framework for understanding the relationship between interpersonal losses and dementia risk in older adulthood. Neurosci Biobehav Rev 2022; 134:104487. [PMID: 34971701 PMCID: PMC8925984 DOI: 10.1016/j.neubiorev.2021.12.010] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 11/23/2021] [Accepted: 12/06/2021] [Indexed: 01/18/2023]
Abstract
Accumulating research suggests that stressful life events, especially those that threaten close intimate bonds, are associated with an increased risk of dementia. Grieving the loss of a spouse, whether in the form of caregiving or after the death, ranks among 'life's most significant stressors', evoking intense psychological and physiological distress. Despite numerous studies reporting elevated dementia risk or poorer cognition among spousal caregivers and widow(er)s compared to controls, no review has summarized findings across cognitive outcomes (i.e., dementia incidence, cognitive impairment rates, cognitive performance) or proposed a theoretical model for understanding the links between partner loss and abnormal cognitive decline. The current systematic review summarizes findings across 64 empirical studies. Overall, both cross-sectional and longitudinal studies revealed an adverse association between partner loss and cognitive outcomes. In turn, we propose a biopsychosocial model of cognitive decline that explains how caregiving and bereavement may position some to develop cognitive impairment or Alzheimer's disease and related dementias. More longitudinal studies that focus on the biopsychosocial context of caregivers and widow(er)s are needed.
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Affiliation(s)
- E Lydia Wu-Chung
- Department of Psychological Sciences, Rice University, Houston, TX, United States.
| | - Stephanie L Leal
- Department of Psychological Sciences, Rice University, Houston, TX, United States
| | - Bryan T Denny
- Department of Psychological Sciences, Rice University, Houston, TX, United States
| | - Samantha L Cheng
- Department of Psychological Sciences, Rice University, Houston, TX, United States
| | - Christopher P Fagundes
- Department of Psychological Sciences, Rice University, Houston, TX, United States; Department of Behavioral Science, The University of Texas MD Anderson Cancer Center, Houston, TX, United States; Department of Psychiatry & Behavioral Sciences, Baylor College of Medicine, Houston, TX, United States
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25
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Jang I, Li B, Riphagen JM, Dickerson BC, Salat DH. Multiscale structural mapping of Alzheimer's disease neurodegeneration. Neuroimage Clin 2022; 33:102948. [PMID: 35121307 PMCID: PMC8814667 DOI: 10.1016/j.nicl.2022.102948] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Revised: 12/09/2021] [Accepted: 01/19/2022] [Indexed: 01/25/2023]
Abstract
The recently described biological framework of Alzheimer's disease (AD) emphasizes three types of pathology to characterize this disorder, referred to as the 'amyloid/tau/neurodegeneration' (A-T-N) status. The 'neurodegenerative' component is typically defined by atrophy measures derived from structural magnetic resonance imaging (MRI) such as hippocampal volume. Neurodegeneration measures from imaging are associated with disease symptoms and prognosis. Thus, sensitive image-based quantification of neurodegeneration in AD has an important role in a range of clinical and research operations. Although hippocampal volume is a sensitive metric of neurodegeneration, this measure is impacted by several clinical conditions other than AD and therefore lacks specificity. In contrast, selective regional cortical atrophy, known as the 'cortical signature of AD' provides greater specificity to AD pathology. Although atrophy is apparent even in the preclinical stages of the disease, it is possible that increased sensitivity to degeneration could be achieved by including tissue microstructural properties in the neurodegeneration measure. However, to facilitate clinical feasibility, such information should be obtainable from a single, short, noninvasive imaging protocol. We propose a multiscale MRI procedure that advances prior work through the quantification of features at both macrostructural (morphometry) and microstructural (tissue properties obtained from multiple layers of cortex and subcortical white matter) scales from a single structural brain image (referred to as 'multi-scale structural mapping'; MSSM). Vertex-wise partial least squares (PLS) regression was used to compress these multi-scale structural features. When contrasting patients with AD to cognitively intact matched older adults, the MSSM procedure showed substantially broader regional group differences including areas that were not statistically significant when using cortical thickness alone. Further, with multiple machine learning algorithms and ensemble procedures, we found that MSSM provides accurate detection of individuals with AD dementia (AUROC = 0.962, AUPRC = 0.976) and individuals with mild cognitive impairment (MCI) that subsequently progressed to AD dementia (AUROC = 0.908, AUPRC = 0.910). The findings demonstrate the critical advancement of neurodegeneration quantification provided through multiscale mapping. Future work will determine the sensitivity of this technique for accurately detecting individuals with earlier impairment and biomarker positivity in the absence of impairment.
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Affiliation(s)
- Ikbeom Jang
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
| | - Binyin Li
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Department of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Joost M Riphagen
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Faculty of Health, Medicine and Life Sciences, School for Mental Health and Neuroscience, Alzheimer Centre Limburg, Maastricht University, Maastricht, the Netherlands
| | - Bradford C Dickerson
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - David H Salat
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Neuroimaging Research for Veterans Center, VA Boston Healthcare System, Boston, MA, USA
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Tang S, Cao P, Huang M, Liu X, Zaiane O. Dual feature correlation guided multi-task learning for Alzheimer's disease prediction. Comput Biol Med 2022; 140:105090. [PMID: 34875406 DOI: 10.1016/j.compbiomed.2021.105090] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Revised: 11/22/2021] [Accepted: 11/26/2021] [Indexed: 12/12/2022]
Abstract
Alzheimer's disease (AD) is a gradually progressive neurodegenerative disease affecting cognition functions. Predicting the cognitive scores from neuroimage measures and identifying relevant imaging biomarkers are important research topics in the study of AD. Despite machine learning algorithms having many successful applications, the prediction model suffers from the so-called curse of dimensionality. Multi-task feature learning (MTFL) has helped tackle this problem incorporating the correlations among multiple clinical cognitive scores. However, MTFL neglects the inherent correlation among brain imaging measures. In order to better predict the cognitive scores and identify stable biomarkers, we first propose a generalized multi-task formulation framework that incorporates the task and feature correlation structures simultaneously. Second, we present a novel feature-aware sparsity-inducing norm (FAS-norm) penalty to incorporate a useful correlation between brain regions by exploiting correlations among features. Three multi-task learning models that incorporate the FAS-norm penalty are proposed following our framework. Finally, the algorithm based on the alternating direction method of multipliers (ADMM) is developed to optimize the non-smooth problems. We comprehensively evaluate the proposed models on the cross-sectional and longitudinal Alzheimer's disease neuroimaging initiative datasets. The inputs are the thickness measures and the volume measures of the cortical regions of interest. Compared with MTFL, our methods achieve an average decrease of 4.28% in overall error in the cross-sectional analysis and an average decrease of 7.97% in the Alzheimer's Disease Assessment Scale cognitive total score longitudinal analysis. Moreover, our methods identify sensitive and stable biomarkers to physicians, such as the hippocampus, lateral ventricle, and corpus callosum.
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Affiliation(s)
- Shanshan Tang
- College of Information Science and Engineering, State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang, Liaoning, 110819, China
| | - Peng Cao
- College of Computer Science and Engineering, Northeastern University, Shenyang, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China.
| | - Min Huang
- College of Information Science and Engineering, State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang, Liaoning, 110819, China.
| | - Xiaoli Liu
- Department of Chemical and Biomolecular Engineering, Faculty of Engineering, National University of Singapore, Singapore
| | - Osmar Zaiane
- Department of Computing Science, University of Alberta, Edmonton, Canada
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27
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Wu X, Peng C, Nelson PT, Cheng Q. Machine Learning Approach Predicts Probability of Time to Stage-Specific Conversion of Alzheimer's Disease. J Alzheimers Dis 2022; 90:891-903. [PMID: 36189595 PMCID: PMC9840816 DOI: 10.3233/jad-220590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
BACKGROUND The progression of Alzheimer's disease (AD) varies in different patients at different stages, which makes predicting the time of disease conversions challenging. OBJECTIVE We established an algorithm by leveraging machine learning techniques to predict the probability of the conversion time to next stage for different subjects during a given period. METHODS Firstly, we used Kaplan-Meier (KM) estimation to get the transition curves of different AD stages, and calculated Log-rank statistics to test whether the progression rate between different stages was identical. This quantitatively confirmed the progression rates known in the literature. Then, we developed an approach based on deep learning model, DeepSurv, to predict the probabilities of time-to-conversion. Finally, to help interpret the deep learning model in our approach, we identified important variables contributing the most to the DeepSurv prediction, whose significance were validated with the analysis of variance (ANOVA). RESULTS Our machine learning approach predicted the time to conversion with a high accuracy. For each of the different stages, the concordance index (CI) of our approach was at least 86%, and the integrated Brier score (IBS) was less than 0.1. To facilitate interpretability of the prediction results, our approach identified the top 10 variables for each disease conversion scenario, which were clinicopathologically meaningful, and most of them were also statistically significant. CONCLUSION Our study has the potential to provide individualized prediction for future time course of AD conversions years before their actual occurrence, thus facilitating personalized prevention and intervention strategies to slow down the progression of AD.
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Affiliation(s)
- Xinxing Wu
- Institute for Biomedical Informatics, University of Kentucky, Lexington, KY, USA
| | - Chong Peng
- Department of Computer Science and Engineering, Qingdao University, Shandong, China
| | - Peter T. Nelson
- Department of Pathology, Sanders Brown Center for Dementia, University of Kentucky, Lexington, KY, USA
| | - Qiang Cheng
- Institute for Biomedical Informatics, University of Kentucky, Lexington, KY, USA,Correspondence to: Qiang Cheng, Institute for Biomedical Informatics, University of Kentucky, Lexington, KY, USA. Tel.: +1 859 323 3162; Fax: +1 859 257 0483;
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Das S, Panigrahi P, Chakrabarti S. Corpus Callosum Atrophy in Detection of Mild and Moderate Alzheimer's Disease Using Brain Magnetic Resonance Image Processing and Machine Learning Techniques. J Alzheimers Dis Rep 2021; 5:771-788. [PMID: 34870103 PMCID: PMC8609489 DOI: 10.3233/adr-210314] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/24/2021] [Indexed: 01/25/2023] Open
Abstract
Background: The total number of people with dementia is projected to reach 82 million in 2030 and 152 in 2050. Early and accurate identification of the underlying causes of dementia, such as Alzheimer’s disease (AD) is of utmost importance. A large body of research has shown that imaging techniques are most promising technologies to improve subclinical and early diagnosis of dementia. Morphological changes, especially atrophy in various structures like cingulate gyri, caudate nucleus, hippocampus, frontotemporal lobe, etc., have been established as markers for AD. Being the largest white matter structure with a high demand of blood supply from several main arterial systems, anatomical alterations of the corpus callosum (CC) may serve as potential indication neurodegenerative disease. Objective: To detect mild and moderate AD using brain magnetic resonance image (MRI) processing and machine learning techniques. Methods: We have performed automatic detection and segmentation of the CC and calculated its morphological features to feed into a multivariate pattern analysis using support vector machine (SVM) learning techniques. Results: Our results using large patients’ cohort show CC atrophy-based features are capable of distinguishing healthy and mild/moderate AD patients. Our classifiers obtain more than 90%sensitivity and specificity in differentiating demented patients from healthy cohorts and importantly, achieved more than 90%sensitivity and > 80%specificity in detecting mild AD patients. Conclusion: Results from this analysis are encouraging and advocate development of an image analysis software package to detect dementia from brain MRI using morphological alterations of the CC.
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Affiliation(s)
- Subhrangshu Das
- Structural Biology and Bioinformatics Division, Council for Scientific and Industrial Research (CSIR) - Indian Institute of Chemical Biology (IICB), Kolkata, West Bengal, India
| | - Priyanka Panigrahi
- Structural Biology and Bioinformatics Division, Council for Scientific and Industrial Research (CSIR) - Indian Institute of Chemical Biology (IICB), Kolkata, West Bengal, India.,Academy of Scientific and Innovative Research, Ghaziabad, Uttar Pradesh, India
| | - Saikat Chakrabarti
- Structural Biology and Bioinformatics Division, Council for Scientific and Industrial Research (CSIR) - Indian Institute of Chemical Biology (IICB), Kolkata, West Bengal, India.,Academy of Scientific and Innovative Research, Ghaziabad, Uttar Pradesh, India
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Kanemaru N, Takao H, Amemiya S, Abe O. The effect of a post-scan processing denoising system on image quality and morphometric analysis. J Neuroradiol 2021; 49:205-212. [PMID: 34863809 DOI: 10.1016/j.neurad.2021.11.007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Revised: 11/26/2021] [Accepted: 11/26/2021] [Indexed: 10/19/2022]
Abstract
PURPOSE MR image quality and subsequent brain morphometric analysis are inevitably affected by noise. The purpose of this study was to evaluate the effectiveness of an artificial intelligence (AI)-based post-scan processing denoising system, intelligent Quick Magnetic Resonance (iQMR), on MR image quality and brain morphometric analysis. METHODS We used 1.5T MP-RAGE MR images acquired from the Alzheimer's Disease Neuroimaging Initiative 1 database. The images of 21 subjects were used for cross-sectional analysis and 15 for longitudinal analysis. In the longitudinal analysis, two timepoints over a 2-year interval were used. Each subject was scanned twice at each timepoint. MR images processed with and without the denoising system were compared both visually and objectively using FreeSurfer cortical thickness analysis. RESULTS The denoising system reduced the noise with good white-gray matter contrast (noise: p < 0.001; contrast: p = 0.49). The mean intraclass correlation coefficients (ICCs) of cortical thickness were slightly better in the images processed with the denoising system (0.739/0.859/0.883; Gaussian smoothing kernel of full width at half maximum = 0/10/20) compared with the unprocessed images (0.718/0.854/0.880). In the longitudinal analysis, the mean ICCs of symmetrized percent change improved in images processed with the denoising system (0.202/0.349/0.431) compared with the unprocessed images (0.167/0.325/0.404). In addition, the detectability of significant cortical thickness atrophy improved with denoising. CONCLUSION We confirm that the AI-based denoising system could effectively reduce the noise while retaining the contrast. We also confirm the improvement of the reliability and detectability of brain morphometric analysis with the denoising system.
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Affiliation(s)
| | - Hidemasa Takao
- Department of Radiology, University of Tokyo, Tokyo, Japan
| | - Shiori Amemiya
- Department of Radiology, University of Tokyo, Tokyo, Japan
| | - Osamu Abe
- Department of Radiology, University of Tokyo, Tokyo, Japan
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Franz CE, Hatton SN, Elman JA, Warren T, Gillespie NA, Whitsel NA, Puckett OK, Dale AM, Eyler LT, Fennema-Notestine C, Hagler DJ, Hauger RL, McKenzie R, Neale MC, Panizzon MS, Pearce RC, Reynolds CA, Sanderson-Cimino M, Toomey R, Tu XM, Williams M, Xian H, Lyons MJ, Kremen WS. Lifestyle and the aging brain: interactive effects of modifiable lifestyle behaviors and cognitive ability in men from midlife to old age. Neurobiol Aging 2021; 108:80-89. [PMID: 34547718 PMCID: PMC8862767 DOI: 10.1016/j.neurobiolaging.2021.08.007] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Revised: 07/23/2021] [Accepted: 08/12/2021] [Indexed: 01/18/2023]
Abstract
We examined the influence of lifestyle on brain aging after nearly 30 years, and tested the hypothesis that young adult general cognitive ability (GCA) would moderate these effects. In the community-dwelling Vietnam Era Twin Study of Aging (VETSA), 431 largely non-Hispanic white men completed a test of GCA at mean age 20. We created a modifiable lifestyle behavior composite from data collected at mean age 40. During VETSA, MRI-based measures at mean age 68 included predicted brain age difference (PBAD), Alzheimer's disease (AD) brain signature, and abnormal white matter scores. There were significant main effects of young adult GCA and lifestyle on PBAD and the AD signature (ps ≤ 0.012), and a GCA-by-lifestyle interaction on both (ps ≤ 0.006). Regardless of GCA level, having more favorable lifestyle behaviors predicted less advanced brain age and less AD-like brain aging. Unfavorable lifestyles predicted advanced brain aging in those with lower age 20 GCA, but did not affect brain aging in those with higher age 20 GCA. Targeting early lifestyle modification may promote dementia risk reduction, especially among lower reserve individuals.
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Affiliation(s)
- Carol E Franz
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA; Center for Behavior Genetics of Aging, University of California San Diego, San Diego, CA, USA.
| | - Sean N Hatton
- Department of Radiology, University of California San Diego, San Diego, CA, USA
| | - Jeremy A Elman
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA; Center for Behavior Genetics of Aging, University of California San Diego, San Diego, CA, USA
| | - Teresa Warren
- Department of Psychology, San Diego State University, San Diego, CA, USA
| | - Nathan A Gillespie
- Virginia Institute for Psychiatric and Behavior Genetics, Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA; QIMR Berghofer Medical Research Institute, Herston, Queensland, Australia
| | - Nathan A Whitsel
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA; Center for Behavior Genetics of Aging, University of California San Diego, San Diego, CA, USA
| | - Olivia K Puckett
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA; Center for Behavior Genetics of Aging, University of California San Diego, San Diego, CA, USA
| | - Anders M Dale
- Department of Radiology, University of California San Diego, San Diego, CA, USA
| | - Lisa T Eyler
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA; Center for Behavior Genetics of Aging, University of California San Diego, San Diego, CA, USA
| | - Christine Fennema-Notestine
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA; Center for Behavior Genetics of Aging, University of California San Diego, San Diego, CA, USA; Department of Radiology, University of California San Diego, San Diego, CA, USA
| | - Donald J Hagler
- Department of Radiology, University of California San Diego, San Diego, CA, USA
| | - Richard L Hauger
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA; Center for Behavior Genetics of Aging, University of California San Diego, San Diego, CA, USA; Center of Excellence for Stress and Mental Health, VA San Diego Healthcare System, San Diego, CA, USA
| | - Ruth McKenzie
- Department of Psychological and Brain Sciences, Boston University, Boston, MA, USA
| | - Michael C Neale
- Virginia Institute for Psychiatric and Behavior Genetics, Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA
| | - Matthew S Panizzon
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA; Center for Behavior Genetics of Aging, University of California San Diego, San Diego, CA, USA
| | - Rahul C Pearce
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA; Center for Behavior Genetics of Aging, University of California San Diego, San Diego, CA, USA
| | - Chandra A Reynolds
- Department of Psychology, University of California Riverside, Riverside, CA, USA
| | - Mark Sanderson-Cimino
- San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology, San Diego, CA, USA; Center of Excellence for Stress and Mental Health, VA San Diego Healthcare System, San Diego, CA, USA
| | - Rosemary Toomey
- Department of Psychological and Brain Sciences, Boston University, Boston, MA, USA
| | - Xin M Tu
- Department of Family Medicine, University of California San Diego, San Diego, CA, USA
| | - McKenna Williams
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA; Center for Behavior Genetics of Aging, University of California San Diego, San Diego, CA, USA; San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology, San Diego, CA, USA
| | - Hong Xian
- Department of Epidemiology & Biostatistics, St. Louis University, St. Louis, MO, USA
| | - Michael J Lyons
- Department of Psychological and Brain Sciences, Boston University, Boston, MA, USA
| | - William S Kremen
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA; Center for Behavior Genetics of Aging, University of California San Diego, San Diego, CA, USA
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Medical Health Records-Based Mild Cognitive Impairment (MCI) Prediction for Effective Dementia Care. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18179223. [PMID: 34501812 PMCID: PMC8431613 DOI: 10.3390/ijerph18179223] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Revised: 08/27/2021] [Accepted: 08/30/2021] [Indexed: 11/17/2022]
Abstract
Dementia is a cognitive impairment that poses a global threat. Current dementia treatments slow the progression of the disease. The timing of starting such treatment markedly affects the effectiveness of the treatment. Some experts mentioned that the optimal timing for starting the currently available treatment in order to delay progression to dementia is the mild cognitive impairment stage, which is the prior stage of dementia. However, medical records are typically only available at a later stage, i.e., from the early or middle stage of dementia. In order to address this limitation, this study developed a model using national health information data from 5 years prior, to predict dementia development 5 years in the future. The Senior Cohort Database, comprising 550,000 samples, were used for model development. The F-measure of the model predicting dementia development after a 5-year incubation period was 77.38%. Models for a 1- and 3-year incubation period were also developed for comparative analysis of dementia risk factors. The three models had some risk factors in common, but also had unique risk factors, depending on the stage. For the common risk factors, a difference in disease severity was confirmed. These findings indicate that the diagnostic criteria and treatment strategy for dementia should differ depending on the timing. Furthermore, since the results of this study present new dementia risk factors that have not been reported previously, this study may also contribute to identification of new dementia risk factors.
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Magnetic Resonance Imaging Measurement of Entorhinal Cortex in the Diagnosis and Differential Diagnosis of Mild Cognitive Impairment and Alzheimer's Disease. Brain Sci 2021; 11:brainsci11091129. [PMID: 34573151 PMCID: PMC8471837 DOI: 10.3390/brainsci11091129] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2021] [Revised: 08/12/2021] [Accepted: 08/20/2021] [Indexed: 11/17/2022] Open
Abstract
Several magnetic resonance imaging studies have shown that the entorhinal cortex (ERC) is the first brain area related to pathologic changes in Alzheimer’s disease (AD), even before atrophy of the hippocampus (HP). However, change in ERC morphology (thickness, surface area and volume) in the progression from aMCI to AD, especially in the subtypes of aMCI (single-domain and multiple-domain: aMCI-s and aMCI-m), however, is still unclear. ERC thickness, surface area and volume were measured in 29 people with aMCI-s, 22 people with aMCI-m, 18 patients with AD and 26 age-/sex-matched healthy controls. Group comparisons of the ERC geometry measurements (including thickness, volume and surface area) were performed using analyses of covariance (ANCOVA). Furthermore, receiver operator characteristic (ROC) analyses and the area under the curve (AUC) were employed to investigate classification ability (HC, aMCI-s, aMCI-m and AD from each other). There was a significant decreasing tendency in ERC thickness from HC to aMCI-s to aMCI-m to finally AD in both the left and the right hemispheres (left hemisphere: HC > aMCI-s > AD; right hemisphere: aMCI-s > aMCI-m > AD). For ERC volume, both the AD group and the aMCI-m group showed significantly decreased volume on both sides compared with the HC group. In addition, the AD group also had significantly decreased volume on both sides compared with the aMCI-s group. As for the ERC surface area, no significant difference was identified among the four groups. Furthermore, the AUC results demonstrate that combined ERC parameters (thickness and volume) can better discriminate the four groups from each other than ERC thickness alone. Finally, and most importantly, relative to HP volume, the capacity of combined ERC parameters was better at discriminating between HC and aMCI-s, as well as aMCI-m and AD. ERC atrophy, particularly the combination of ERC thickness and volume, might be regarded as a promising candidate biomarker in the diagnosis and differential diagnosis of aMCI and AD.
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Williams ME, Elman JA, McEvoy LK, Andreassen OA, Dale AM, Eglit GML, Eyler LT, Fennema-Notestine C, Franz CE, Gillespie NA, Hagler DJ, Hatton SN, Hauger RL, Jak AJ, Logue MW, Lyons MJ, McKenzie RE, Neale MC, Panizzon MS, Puckett OK, Reynolds CA, Sanderson-Cimino M, Toomey R, Tu XM, Whitsel N, Xian H, Kremen WS. 12-year prediction of mild cognitive impairment aided by Alzheimer's brain signatures at mean age 56. Brain Commun 2021; 3:fcab167. [PMID: 34396116 PMCID: PMC8361427 DOI: 10.1093/braincomms/fcab167] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Revised: 04/26/2021] [Accepted: 05/10/2021] [Indexed: 01/22/2023] Open
Abstract
Neuroimaging signatures based on composite scores of cortical thickness and hippocampal volume predict progression from mild cognitive impairment to Alzheimer's disease. However, little is known about the ability of these signatures among cognitively normal adults to predict progression to mild cognitive impairment. Towards that end, a signature sensitive to microstructural changes that may predate macrostructural atrophy should be useful. We hypothesized that: (i) a validated MRI-derived Alzheimer's disease signature based on cortical thickness and hippocampal volume in cognitively normal middle-aged adults would predict progression to mild cognitive impairment; and (ii) a novel grey matter mean diffusivity signature would be a better predictor than the thickness/volume signature. This cohort study was part of the Vietnam Era Twin Study of Aging. Concurrent analyses compared cognitively normal and mild cognitive impairment groups at each of three study waves (ns = 246-367). Predictive analyses included 169 cognitively normal men at baseline (age = 56.1, range = 51-60). Our previously published thickness/volume signature derived from independent data, a novel mean diffusivity signature using the same regions and weights as the thickness/volume signature, age, and an Alzheimer's disease polygenic risk score were used to predict incident mild cognitive impairment an average of 12 years after baseline (follow-up age = 67.2, range = 61-71). Additional analyses adjusted for predicted brain age difference scores (chronological age minus predicted brain age) to determine if signatures were Alzheimer-related and not simply ageing-related. In concurrent analyses, individuals with mild cognitive impairment had higher (worse) mean diffusivity signature scores than cognitively normal participants, but thickness/volume signature scores did not differ between groups. In predictive analyses, age and polygenic risk score yielded an area under the curve of 0.74 (sensitivity = 80.00%; specificity = 65.10%). Prediction was significantly improved with addition of the mean diffusivity signature (area under the curve = 0.83; sensitivity = 85.00%; specificity = 77.85%; P = 0.007), but not with addition of the thickness/volume signature. A model including both signatures did not improve prediction over a model with only the mean diffusivity signature. Results held up after adjusting for predicted brain age difference scores. The novel mean diffusivity signature was limited by being yoked to the thickness/volume signature weightings. An independently derived mean diffusivity signature may thus provide even stronger prediction. The young age of the sample at baseline is particularly notable. Given that the brain signatures were examined when participants were only in their 50 s, our results suggest a promising step towards improving very early identification of Alzheimer's disease risk and the potential value of mean diffusivity and/or multimodal brain signatures.
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Affiliation(s)
- McKenna E Williams
- Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA 92093, USA
- Joint Doctoral Program in Clinical Psychology, San Diego State University/University of California, San Diego, CA 92093, USA
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, USA
| | - Jeremy A Elman
- Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA 92093, USA
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, USA
| | - Linda K McEvoy
- Department of Radiology, University of California San Diego, La Jolla, CA 92093, USA
| | - Ole A Andreassen
- NORMENT, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo 0316, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo 0372, Norway
| | - Anders M Dale
- Department of Radiology, University of California San Diego, La Jolla, CA 92093, USA
- Department of Neuroscience, University of California San Diego, La Jolla, CA 92093, USA
| | - Graham M L Eglit
- Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA 92093, USA
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, USA
| | - Lisa T Eyler
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, USA
- Desert Pacific Mental Illness Research Education and Clinical Center, VA San Diego Healthcare System, CA 92093, USA
| | - Christine Fennema-Notestine
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, USA
- Department of Radiology, University of California San Diego, La Jolla, CA 92093, USA
| | - Carol E Franz
- Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA 92093, USA
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, USA
| | - Nathan A Gillespie
- Virginia Institute for Psychiatric and Behavior Genetics, Virginia Commonwealth University, Richmond, VA 23284, USA
| | - Donald J Hagler
- Department of Radiology, University of California San Diego, La Jolla, CA 92093, USA
| | - Sean N Hatton
- Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA 92093, USA
- Department of Neuroscience, University of California San Diego, La Jolla, CA 92093, USA
| | - Richard L Hauger
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, USA
- Center of Excellence for Stress and Mental Health (CESAMH), VA San Diego Healthcare System, San Diego, CA 92093, USA
| | - Amy J Jak
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, USA
- VA San Diego Healthcare System, San Diego, CA 92093, USA
| | - Mark W Logue
- National Center for PTSD: Behavioral Science Division, VA Boston Healthcare System, Boston, MA 02130, USA
- Department of Psychiatry and the Biomedical Genetics Section, Boston University School of Medicine, Boston, MA 02118, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA
| | - Michael J Lyons
- Department of Psychological and Brain Sciences, Boston University, Boston, MA 02212, USA
| | - Ruth E McKenzie
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA
- School of Education and Social Policy, Merrimack College, North Andover, MA 01845, USA
| | - Michael C Neale
- Virginia Institute for Psychiatric and Behavior Genetics, Virginia Commonwealth University, Richmond, VA 23284, USA
| | - Matthew S Panizzon
- Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA 92093, USA
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, USA
| | - Olivia K Puckett
- Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA 92093, USA
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, USA
| | - Chandra A Reynolds
- Department of Psychology, University of California Riverside, Riverside, CA 92521, USA
| | - Mark Sanderson-Cimino
- Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA 92093, USA
- Joint Doctoral Program in Clinical Psychology, San Diego State University/University of California, San Diego, CA 92093, USA
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, USA
| | - Rosemary Toomey
- Department of Psychological and Brain Sciences, Boston University, Boston, MA 02212, USA
| | - Xin M Tu
- Family Medicine and Public Health, University of California San Diego, La Jolla, CA 92093, USA
| | - Nathan Whitsel
- Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA 92093, USA
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, USA
| | - Hong Xian
- Department of Biostatistics, St. Louis University, St. Louis, MO 63103, USA
| | - William S Kremen
- Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA 92093, USA
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, USA
- Center of Excellence for Stress and Mental Health (CESAMH), VA San Diego Healthcare System, San Diego, CA 92093, USA
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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.
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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
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Keret O, Staffaroni AM, Ringman JM, Cobigo Y, Goh SM, Wolf A, Allen IE, Salloway S, Chhatwal J, Brickman AM, Reyes‐Dumeyer D, Bateman RJ, Benzinger TL, Morris JC, Ances BM, Joseph‐Mathurin N, Perrin RJ, Gordon BA, Levin J, Vöglein J, Jucker M, la Fougère C, Martins RN, Sohrabi HR, Taddei K, Villemagne VL, Schofield PR, Brooks WS, Fulham M, Masters CL, Ghetti B, Saykin AJ, Jack CR, Graff‐Radford NR, Weiner M, Cash DM, Allegri RF, Chrem P, Yi S, Miller BL, Rabinovici GD, Rosen HJ. Pattern and degree of individual brain atrophy predicts dementia onset in dominantly inherited Alzheimer's disease. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2021; 13:e12197. [PMID: 34258377 PMCID: PMC8256623 DOI: 10.1002/dad2.12197] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Accepted: 04/13/2021] [Indexed: 01/18/2023]
Abstract
INTRODUCTION Asymptomatic and mildly symptomatic dominantly inherited Alzheimer's disease mutation carriers (DIAD-MC) are ideal candidates for preventative treatment trials aimed at delaying or preventing dementia onset. Brain atrophy is an early feature of DIAD-MC and could help predict risk for dementia during trial enrollment. METHODS We created a dementia risk score by entering standardized gray-matter volumes from 231 DIAD-MC into a logistic regression to classify participants with and without dementia. The score's predictive utility was assessed using Cox models and receiver operating curves on a separate group of 65 DIAD-MC followed longitudinally. RESULTS Our risk score separated asymptomatic versus demented DIAD-MC with 96.4% (standard error = 0.02) and predicted conversion to dementia at next visit (hazard ratio = 1.32, 95% confidence interval [CI: 1.15, 1.49]) and within 2 years (area under the curve = 90.3%, 95% CI [82.3%-98.2%]) and improved prediction beyond established methods based on familial age of onset. DISCUSSION Individualized risk scores based on brain atrophy could be useful for establishing enrollment criteria and stratifying DIAD-MC participants for prevention trials.
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Affiliation(s)
- Ophir Keret
- Global Brain Health InstituteUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - Adam M. Staffaroni
- Department of Neurology, Memory and Aging CenterUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - John M. Ringman
- Alzheimer's Disease Research Center, Keck School of MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Yann Cobigo
- Department of Neurology, Memory and Aging CenterUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - Sheng‐Yang M. Goh
- Department of Neurology, Memory and Aging CenterUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - Amy Wolf
- Department of Neurology, Memory and Aging CenterUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - Isabel Elaine Allen
- Global Brain Health InstituteUniversity of CaliforniaSan FranciscoCaliforniaUSA
- Department of Epidemiology and BiostatisticsUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - Stephen Salloway
- Warren Alpert Medical SchoolBrown UniversityProvidenceRhode IslandUSA
| | - Jasmeer Chhatwal
- Massachusetts General Hospital, Harvard Medical School BostonBostonMassachusettsUSA
| | - Adam M. Brickman
- Taub Institute for Research on Alzheimer's Disease and the Aging BrainColumbia UniversityNew YorkNew YorkUSA
| | - Dolly Reyes‐Dumeyer
- Taub Institute for Research on Alzheimer's Disease and the Aging BrainColumbia UniversityNew YorkNew YorkUSA
| | - Randal J. Bateman
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Department of NeurologyWashington University School of MedicineSt. LouisMissouriUSA
- Department of NeurologyWashington University School of MedicineSt. LouisMissouriUSA
- Department of RadiologyWashington University School of MedicineSt. LouisMissouriUSA
- Division of Neuropathology, Department of Pathology & ImmunologyWashington University School of MedicineSt. LouisMissouriUSA
- Division of Biostatistics, Department of PsychiatryWashington University in St. Louis School of MedicineSt. LouisMissouriUSA
| | - Tammie L.S. Benzinger
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Department of NeurologyWashington University School of MedicineSt. LouisMissouriUSA
- Department of RadiologyWashington University School of MedicineSt. LouisMissouriUSA
| | - John C. Morris
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Department of NeurologyWashington University School of MedicineSt. LouisMissouriUSA
- Department of NeurologyWashington University School of MedicineSt. LouisMissouriUSA
- Department of RadiologyWashington University School of MedicineSt. LouisMissouriUSA
- Division of Neuropathology, Department of Pathology & ImmunologyWashington University School of MedicineSt. LouisMissouriUSA
- Division of Biostatistics, Department of PsychiatryWashington University in St. Louis School of MedicineSt. LouisMissouriUSA
| | - Beau M. Ances
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Department of NeurologyWashington University School of MedicineSt. LouisMissouriUSA
- Department of NeurologyWashington University School of MedicineSt. LouisMissouriUSA
- Department of RadiologyWashington University School of MedicineSt. LouisMissouriUSA
- Division of Neuropathology, Department of Pathology & ImmunologyWashington University School of MedicineSt. LouisMissouriUSA
- Division of Biostatistics, Department of PsychiatryWashington University in St. Louis School of MedicineSt. LouisMissouriUSA
| | - Nelly Joseph‐Mathurin
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Department of NeurologyWashington University School of MedicineSt. LouisMissouriUSA
- Department of NeurologyWashington University School of MedicineSt. LouisMissouriUSA
- Department of RadiologyWashington University School of MedicineSt. LouisMissouriUSA
- Division of Neuropathology, Department of Pathology & ImmunologyWashington University School of MedicineSt. LouisMissouriUSA
- Division of Biostatistics, Department of PsychiatryWashington University in St. Louis School of MedicineSt. LouisMissouriUSA
| | - Richard J. Perrin
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Department of NeurologyWashington University School of MedicineSt. LouisMissouriUSA
- Department of NeurologyWashington University School of MedicineSt. LouisMissouriUSA
- Department of RadiologyWashington University School of MedicineSt. LouisMissouriUSA
- Division of Neuropathology, Department of Pathology & ImmunologyWashington University School of MedicineSt. LouisMissouriUSA
- Division of Biostatistics, Department of PsychiatryWashington University in St. Louis School of MedicineSt. LouisMissouriUSA
| | - Brian A. Gordon
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Department of NeurologyWashington University School of MedicineSt. LouisMissouriUSA
- Department of NeurologyWashington University School of MedicineSt. LouisMissouriUSA
- Department of RadiologyWashington University School of MedicineSt. LouisMissouriUSA
- Division of Neuropathology, Department of Pathology & ImmunologyWashington University School of MedicineSt. LouisMissouriUSA
- Division of Biostatistics, Department of PsychiatryWashington University in St. Louis School of MedicineSt. LouisMissouriUSA
| | - Johannes Levin
- German Center for Neurodegenerative Diseases (DZNE)MunichGermany
- Department of NeurologyLudwig‐Maximilians‐Universität MünchenMunichGermany
| | - Jonathan Vöglein
- German Center for Neurodegenerative Diseases (DZNE)MunichGermany
- Department of NeurologyLudwig‐Maximilians‐Universität MünchenMunichGermany
| | - Mathias Jucker
- German Center for Neurodegenerative Diseases (DZNE)TübingenGermany
- Department of Neurodegenerative Diseases, Hertie Institute for Clinical Brain ResearchUniversity of TübingenTübingenGermany
| | - Christian la Fougère
- German Center for Neurodegenerative Diseases (DZNE)TübingenGermany
- Institute for Nuclear Medicine and Clinical Molecular ImagingEberhard Karls UniversityTübingenGermany
| | - Ralph N. Martins
- Department of Biomedical SciencesMacquarie UniversityNorth RydeNew South WalesAustralia
- Centre of Excellence for Alzheimer's Disease Research and Care, School of Medical and Health SciencesEdith Cowan UniversityJoondalupWestern AustraliaAustralia
- School of Psychiatry and Clinical NeurosciencesUniversity of Western AustraliaCrawleyWestern AustraliaAustralia
- Australian Alzheimer's Research FoundationNedlandsWestern AustraliaAustralia
- The Cooperative Research Centre for Mental HealthCarlton SouthVictoriaAustralia
| | - Hamid R. Sohrabi
- Department of Biomedical SciencesMacquarie UniversityNorth RydeNew South WalesAustralia
- Centre of Excellence for Alzheimer's Disease Research and Care, School of Medical and Health SciencesEdith Cowan UniversityJoondalupWestern AustraliaAustralia
- School of Psychiatry and Clinical NeurosciencesUniversity of Western AustraliaCrawleyWestern AustraliaAustralia
- Australian Alzheimer's Research FoundationNedlandsWestern AustraliaAustralia
- The Cooperative Research Centre for Mental HealthCarlton SouthVictoriaAustralia
| | - Kevin Taddei
- Centre of Excellence for Alzheimer's Disease Research and Care, School of Medical and Health SciencesEdith Cowan UniversityJoondalupWestern AustraliaAustralia
- Australian Alzheimer's Research FoundationNedlandsWestern AustraliaAustralia
| | - Victor L. Villemagne
- Department of Molecular Imaging and TherapyAustin HealthMelbourneVictoriaAustralia
| | - Peter R. Schofield
- Neuroscience Research Australia, RandwickSydneyNew South WalesAustralia
- School of Medical SciencesUNSW SydneySydneyNew South WalesAustralia
| | - William S. Brooks
- Neuroscience Research Australia, RandwickSydneyNew South WalesAustralia
- Prince of Wales Hospital Clinical SchoolUNSW SydneySydneyNew South WalesAustralia
| | - Michael Fulham
- Department of Molecular Imaging, Royal Prince Alfred Hospital, Sydney Medical SchoolUniversity of SydneyCamperdownNew South WalesAustralia
| | - Colin L. Masters
- The Florey InstituteUniversity of MelbourneParkvilleVictoriaAustralia
| | - Bernardino Ghetti
- Department of Pathology and Laboratory MedicineIndiana University School of MedicineIndianapolisIndianaUSA
| | - Andrew J. Saykin
- Department of NeurologyIndiana University School of MedicineIndianapolisIndianaUSA
- Department of RadiologyIndiana University School of MedicineIndianapolisIndianaUSA
| | | | | | - Michael Weiner
- Department of Veterans Affairs Medical CenterCenter for Imaging of Neurodegenerative DiseasesSan FranciscoCaliforniaUSA
- Department of RadiologyUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
- Department of MedicineUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
- Department of PsychiatryUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
- Department of NeurologyUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
| | - David M. Cash
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of NeurologyUniversity College LondonLondonUK
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical EngineeringUniversity College LondonLondonUK
| | - Ricardo F. Allegri
- Department of Cognitive Neurology, Neuropsychiatry and NeuropsychologyInstituto de InvestigacionesNeurológicas FLENIBuenos AiresArgentina
| | - Patricio Chrem
- Department of Cognitive Neurology, Neuropsychiatry and NeuropsychologyInstituto de InvestigacionesNeurológicas FLENIBuenos AiresArgentina
| | - Su Yi
- Banner Alzheimer's InstitutePhoenixArizonaUSA
| | - Bruce L. Miller
- Global Brain Health InstituteUniversity of CaliforniaSan FranciscoCaliforniaUSA
- Department of Neurology, Memory and Aging CenterUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - Gil D. Rabinovici
- Global Brain Health InstituteUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - Howard J. Rosen
- Global Brain Health InstituteUniversity of CaliforniaSan FranciscoCaliforniaUSA
- Department of Neurology, Memory and Aging CenterUniversity of CaliforniaSan FranciscoCaliforniaUSA
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Combination of automated brain volumetry on MRI and quantitative tau deposition on THK-5351 PET to support diagnosis of Alzheimer's disease. Sci Rep 2021; 11:10343. [PMID: 33990649 PMCID: PMC8121780 DOI: 10.1038/s41598-021-89797-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2020] [Accepted: 04/27/2021] [Indexed: 01/18/2023] Open
Abstract
Imaging biomarkers support the diagnosis of Alzheimer’s disease (AD). We aimed to determine whether combining automated brain volumetry on MRI and quantitative measurement of tau deposition on [18F] THK-5351 PET can aid discrimination of AD spectrum. From a prospective database in an IRB-approved multicenter study (NCT02656498), 113 subjects (32 healthy control, 55 mild cognitive impairment, and 26 Alzheimer disease) with baseline structural MRI and [18F] THK-5351 PET were included. Cortical volumes were quantified from FDA-approved software for automated volumetric MRI analysis (NeuroQuant). Standardized uptake value ratio (SUVR) was calculated from tau PET images for 6 composite FreeSurfer-derived regions-of-interests approximating in vivo Braak stage (Braak ROIs). On volumetric MRI analysis, stepwise logistic regression analyses identified the cingulate isthmus and inferior parietal lobule as significant regions in discriminating AD from HC and MCI. The combined model incorporating automated volumes of selected brain regions on MRI (cingulate isthmus, inferior parietal lobule, hippocampus) and SUVRs of Braak ROIs on [18F] THK-5351 PET showed higher performance than SUVRs of Braak ROIs on [18F] THK-5351 PET in discriminating AD from HC (0.98 vs 0.88, P = 0.033) but not in discriminating AD from MCI (0.85 vs 0.79, P = 0.178). The combined model showed comparable performance to automated volumes of selected brain regions on MRI in discriminating AD from HC (0.98 vs 0.94, P = 0.094) and MCI (0.85 vs 0.78; P = 0.065).
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Glynn K, O'Callaghan M, Hannigan O, Bruce I, Gibb M, Coen R, Green E, A Lawlor B, Robinson D. Clinical utility of mild cognitive impairment subtypes and number of impaired cognitive domains at predicting progression to dementia: A 20-year retrospective study. Int J Geriatr Psychiatry 2021; 36:31-37. [PMID: 32748438 DOI: 10.1002/gps.5385] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/17/2020] [Revised: 07/07/2020] [Accepted: 07/21/2020] [Indexed: 11/12/2022]
Abstract
OBJECTIVE To determine the utility of mild cognitive impairment (MCI) subtypes and number of impaired cognitive domains on initial assessment at predicting progression to dementia in a sample of memory clinic patients over a 20-year period. METHODS A retrospective analysis was conducted of those presenting to a memory clinic with MCI from 1 January 1999 to 31 December 2018 inclusive. Those with MCI were broken down into one of the four subtypes using recommended cut-off scores on the Cambridge Cognitive Assessment (CAMCOG). Binomial logistic regression analysis was used to determine the utility of MCI subtypes and number of impaired cognitive domains as predictors for dementia. RESULTS Overall 1188 individuals with MCI diagnosis were identified, with 378 (32%) progressing to dementia, with median [range] time to diagnosis of 2 years [1-8.4]. Six hundred and forty-nine (55%) were identified as amnestic MCI and 539 (45%) as non-amnestic MCI. Amnestic MCI was a significant predictor of progression compared to non-amnestic MCI (OR = 1.85, df = 1, P < .001). Number of cognitive domains impaired was also a significant predictor of progression to dementia (OR = 1.07, df = 1, P = .01) but the single-/multi-domain distinction was not (OR = 1.29, df = 1, P = .36). CONCLUSION This study shows that approximately 32% of those diagnosed with MCI in a memory clinic progressed to dementia, with a median time to progression of 2 years. Those with amnestic MCI are almost twice as likely to progress to dementia than non-amnestic MCI and that therefore this is a useful distinction. However, the utility of the single- and multi-domain MCI distinction is called into question by our findings.
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Affiliation(s)
- Kevin Glynn
- Mercer Institute for Research on Aging, St James's Hospital, Dublin 8, Ireland
| | | | - Oisin Hannigan
- Mercer Institute for Research on Aging, St James's Hospital, Dublin 8, Ireland
| | - Irene Bruce
- Mercer Institute for Research on Aging, St James's Hospital, Dublin 8, Ireland
| | - Mathew Gibb
- Mercer Institute for Research on Aging, St James's Hospital, Dublin 8, Ireland
| | - Robert Coen
- Mercer Institute for Research on Aging, St James's Hospital, Dublin 8, Ireland
| | - Elaine Green
- Mercer Institute for Research on Aging, St James's Hospital, Dublin 8, Ireland.,Trinity College Dublin, Dublin, Ireland
| | - Brian A Lawlor
- Mercer Institute for Research on Aging, St James's Hospital, Dublin 8, Ireland.,Trinity College Dublin, Dublin, Ireland
| | - David Robinson
- Mercer Institute for Research on Aging, St James's Hospital, Dublin 8, Ireland
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Suh CH, Shim WH, Kim SJ, Roh JH, Lee JH, Kim MJ, Park S, Jung W, Sung J, Jahng GH. Development and Validation of a Deep Learning-Based Automatic Brain Segmentation and Classification Algorithm for Alzheimer Disease Using 3D T1-Weighted Volumetric Images. AJNR Am J Neuroradiol 2020; 41:2227-2234. [PMID: 33154073 DOI: 10.3174/ajnr.a6848] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2020] [Accepted: 08/07/2020] [Indexed: 01/17/2023]
Abstract
BACKGROUND AND PURPOSE Limited evidence has suggested that a deep learning automatic brain segmentation and classification method, based on T1-weighted brain MR images, can predict Alzheimer disease. Our aim was to develop and validate a deep learning-based automatic brain segmentation and classification algorithm for the diagnosis of Alzheimer disease using 3D T1-weighted brain MR images. MATERIALS AND METHODS A deep learning-based algorithm was developed using a dataset of T1-weighted brain MR images in consecutive patients with Alzheimer disease and mild cognitive impairment. We developed a 2-step algorithm using a convolutional neural network to perform brain parcellation followed by 3 classifier techniques including XGBoost for disease prediction. All classification experiments were performed using 5-fold cross-validation. The diagnostic performance of the XGBoost method was compared with logistic regression and a linear Support Vector Machine by calculating their areas under the curve for differentiating Alzheimer disease from mild cognitive impairment and mild cognitive impairment from healthy controls. RESULTS In a total of 4 datasets, 1099, 212, 711, and 705 eligible patients were included. Compared with the linear Support Vector Machine and logistic regression, XGBoost significantly improved the prediction of Alzheimer disease (P < .001). In terms of differentiating Alzheimer disease from mild cognitive impairment, the 3 algorithms resulted in areas under the curve of 0.758-0.825. XGBoost had a sensitivity of 68% and a specificity of 70%. In terms of differentiating mild cognitive impairment from the healthy control group, the 3 algorithms resulted in areas under the curve of 0.668-0.870. XGBoost had a sensitivity of 79% and a specificity of 80%. CONCLUSIONS The deep learning-based automatic brain segmentation and classification algorithm allowed an accurate diagnosis of Alzheimer disease using T1-weighted brain MR images. The widespread availability of T1-weighted brain MR imaging suggests that this algorithm is a promising and widely applicable method for predicting Alzheimer disease.
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Affiliation(s)
- C H Suh
- From the Department of Radiology and Research Institute of Radiology (C.H.S., W.H.S., S.J.K.)
| | - W H Shim
- From the Department of Radiology and Research Institute of Radiology (C.H.S., W.H.S., S.J.K.)
| | - S J Kim
- From the Department of Radiology and Research Institute of Radiology (C.H.S., W.H.S., S.J.K.)
| | - J H Roh
- Department of Neurology (J.H.R., J.-H.L.).,Department of Physiology (J.H.R.), Korea University College of Medicine, Seoul, Republic of Korea
| | - J-H Lee
- Department of Neurology (J.H.R., J.-H.L.)
| | - M-J Kim
- Health Screening and Promotion Center (M.-J.K.), Asan Medical Center, Seoul, Republic of Korea
| | - S Park
- VUNO Inc (S.P., W.J., J.S.), Seoul, Republic of Korea
| | - W Jung
- VUNO Inc (S.P., W.J., J.S.), Seoul, Republic of Korea
| | - J Sung
- VUNO Inc (S.P., W.J., J.S.), Seoul, Republic of Korea
| | - G-H Jahng
- Department of Radiology (G.-H.J.), Kyung Hee University Hospital at Gangdong, College of Medicine, Kyung Hee University, Seoul, Republic of Korea
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Spencer BE, Jennings RG, Brewer JB. Combined Biomarker Prognosis of Mild Cognitive Impairment: An 11-Year Follow-Up Study in the Alzheimer's Disease Neuroimaging Initiative. J Alzheimers Dis 2020; 68:1549-1559. [PMID: 30958366 DOI: 10.3233/jad-181243] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
BACKGROUND Biomarkers may soon be used to predict decline in older individuals. Extended follow-up studies are needed to determine the stability of such biomarker-based predictions. OBJECTIVE To examine the long-term performance of baseline cognitive, neuroimaging, and cerebrospinal fluid (CSF) biomarker-assisted prognosis in patients with mild cognitive impairment. METHODS Established, biomarker-defined, cohorts of subjects with mild cognitive impairment were examined for progression to dementia. Subjects with a baseline volumetric MRI, lumbar puncture, and Rey Auditory Verbal Learning Test were included. Dementia-free survival time in each biomarker-defined risk group was determined with Kaplan-Meier survival curves. The influence of each risk factor or combination of factors on dementia-free survival was examined with Cox proportional hazard analyses. RESULTS 185 subjects were followed longitudinally for a mean (SD) 4.3 (2.8) years. 59% of participants converted within the follow-up period and the median dementia-free survival time was 2.8 years. Each individual risk factor predicted conversion to dementia (HR 1.9-3.7). The joint presence of any two risk factors increased risk for conversion (HR 7.1-11.0), with the presence of medial temporal atrophy and memory impairment showing the greatest risk for decline. Concordant atrophy, memory impairment, and abnormal CSF amyloid and tau was associated with the highest risk for conversion (HR 15.1). The presence of medial temporal atrophy was associated with the shortest dementia-free survival time, both alone and in combination with memory impairment, abnormal CSF amyloid and tau, or both. CONCLUSION These results suggest that baseline biomarker-assisted predictions of decline to dementia are stable over the long term, and that combinations of complementary biomarkers can improve the accuracy of these predictions.
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Affiliation(s)
- Barbara E Spencer
- Department of Neurosciences, University of California, San Diego, La Jolla, CA, USA
| | - Robin G Jennings
- Department of Neurosciences, University of California, San Diego, La Jolla, CA, USA
| | - James B Brewer
- Department of Neurosciences, University of California, San Diego, La Jolla, CA, USA.,Department of Radiology, University of California, San Diego, La Jolla, CA, USA
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Association of cortical thickness with age of onset in first-episode, drug-naïve major depression. Neuroreport 2020; 30:1074-1080. [PMID: 31503209 DOI: 10.1097/wnr.0000000000001314] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
OBJECTIVE We previously showed differences in brain grey matter volume changes between patients with early-onset adult depression (EOD) and late-onset adult depression (LOD). Here, we aim to identify whether cortical thickness (CT) is affected by the age of onset in patients with depression. METHODS High-resolution MRI images were obtained for 54 major depressive disorder (MDD) patients with EOD, 58 patients with LOD, 57 young healthy controls (HCs), and 58 aged HCs. Depression severity was assessed using the Hamilton Depression Rating Scale 17-item (HDRS17). Associations between CT of patients and clinical scores were analyzed. RESULTS There was a significant main effect of diagnosis for the left rostal anterior cingulate (rACC), right inferior temporal, right lateral orbitofrontal cortex (lOFC), and bilateral pericalcarine. A remarkable onset age-group effect on CT was observed in the rACC and bilateral caudal anterior cingulate (cACC). The diagnosis-by-onset age interaction effect was found in bilateral rACC and right lOFC. Thinning CT in bilateral rACC was observed in EOD patients compared to young HCs. Compared to older HCs, thicker CT in lOFC was seen in the LOD patient group. Compared with the LOD group, the EOD group showed cortical thinning of the right cACC and posterior cingulate cortex (PCC). There were no significant associations between CT in right cACC or PCC with symptom severity or illness duration. CONCLUSIONS MDD patients with different age at onset show distinct CT alterations, suggesting potentially divergent pathological mechanisms of EOD and LOD.
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Moore EE, Gifford KA, Khan OA, Liu D, Pechman KR, Acosta LMY, Bell SP, Turchan M, Landman BA, Blennow K, Zetterberg H, Hohman TJ, Jefferson AL. Cerebrospinal fluid biomarkers of neurodegeneration, synaptic dysfunction, and axonal injury relate to atrophy in structural brain regions specific to Alzheimer's disease. Alzheimers Dement 2020; 16:883-895. [PMID: 32378327 DOI: 10.1002/alz.12087] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2019] [Revised: 01/09/2020] [Accepted: 01/15/2020] [Indexed: 12/22/2022]
Abstract
INTRODUCTION Patterns of atrophy can distinguish normal cognition from Alzheimer's disease (AD), but neuropathological drivers of this pattern are unknown. This study examined associations between cerebrospinal fluid biomarkers of AD pathology, synaptic dysfunction, and neuroaxonal injury with two AD imaging signatures. METHODS Signatures were calculated using published guidelines. Linear regressions related each biomarker to both signatures, adjusting for demographic factors. Bootstrapped analyses tested if associations were stronger with one signature versus the other. RESULTS Increased phosphorylated tau (p-tau), total tau, and neurofilament light (P-values <.045) related to smaller signatures (indicating greater atrophy). Diagnosis and sex modified associations between p-tau and neurogranin (P-values<.05) and signatures, such that associations were stronger among participants with mild cognitive impairment and female participants. The strength of associations did not differ between signatures. DISCUSSION Increased evidence of neurodegeneration, axonopathy, and tau phosphorylation relate to greater AD-related atrophy. Tau phosphorylation and synaptic dysfunction may be more prominent in AD-affected regions in females.
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Affiliation(s)
- Elizabeth E Moore
- Department of Neurology, Vanderbilt Memory & Alzheimer's Center, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Katherine A Gifford
- Department of Neurology, Vanderbilt Memory & Alzheimer's Center, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Omair A Khan
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Dandan Liu
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Kimberly R Pechman
- Department of Neurology, Vanderbilt Memory & Alzheimer's Center, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Lealani Mae Y Acosta
- Department of Neurology, Vanderbilt Memory & Alzheimer's Center, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Susan P Bell
- Department of Neurology, Vanderbilt Memory & Alzheimer's Center, Vanderbilt University Medical Center, Nashville, Tennessee.,Division of Cardiovascular Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Maxim Turchan
- Department of Neurology, Vanderbilt Memory & Alzheimer's Center, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Bennett A Landman
- Department of Radiology & Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee.,Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee.,Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at University of Gothenburg, Mölndal, Sweden.,Clinical Neurochemistry Lab, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at University of Gothenburg, Mölndal, Sweden.,Clinical Neurochemistry Lab, Sahlgrenska University Hospital, Mölndal, Sweden.,Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, UK.,UK Dementia Research Institute at UCL, London, UK
| | - Timothy J Hohman
- Department of Neurology, Vanderbilt Memory & Alzheimer's Center, Vanderbilt University Medical Center, Nashville, Tennessee.,Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Angela L Jefferson
- Department of Neurology, Vanderbilt Memory & Alzheimer's Center, Vanderbilt University Medical Center, Nashville, Tennessee.,Division of Cardiovascular Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
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Abstract
The concept of sleep health provides a positive holistic framing of multiple sleep characteristics, including sleep duration, continuity, timing, alertness, and satisfaction. Sleep health promotion is an underrecognized public health opportunity with implications for a wide range of critical health outcomes, including cardiovascular disease, obesity, mental health, and neurodegenerative disease. Using a socioecological framework, we describe interacting domains of individual, social, and contextual influences on sleep health. To the extent that these determinants of sleep health are modifiable, sleep and public health researchers may benefit from taking a multilevel approach for addressing disparities in sleep health. For example, in addition to providing individual-level sleep behavioral recommendations, health promotion interventions need to occur at multiple contextual levels (e.g., family, schools, workplaces, media, and policy). Because sleep health, a key indicator of overall health, is unevenly distributed across the population, we consider improving sleep health a necessary step toward achieving health equity.
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Affiliation(s)
- Lauren Hale
- Program in Public Health; and Department of Family, Population, and Preventive Medicine; Renaissance School of Medicine, Stony Brook University, Stony Brook, New York 11794-8338, USA;
| | - Wendy Troxel
- Division of Behavior and Policy Sciences, RAND Corporation, Pittsburgh, Pennsylvania 15213, USA;
| | - Daniel J Buysse
- Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania 15213, USA;
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Lombardi G, Crescioli G, Cavedo E, Lucenteforte E, Casazza G, Bellatorre A, Lista C, Costantino G, Frisoni G, Virgili G, Filippini G. Structural magnetic resonance imaging for the early diagnosis of dementia due to Alzheimer's disease in people with mild cognitive impairment. Cochrane Database Syst Rev 2020; 3:CD009628. [PMID: 32119112 PMCID: PMC7059964 DOI: 10.1002/14651858.cd009628.pub2] [Citation(s) in RCA: 56] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
BACKGROUND Mild cognitive impairment (MCI) due to Alzheimer's disease is the symptomatic predementia phase of Alzheimer's disease dementia, characterised by cognitive and functional impairment not severe enough to fulfil the criteria for dementia. In clinical samples, people with amnestic MCI are at high risk of developing Alzheimer's disease dementia, with annual rates of progression from MCI to Alzheimer's disease estimated at approximately 10% to 15% compared with the base incidence rates of Alzheimer's disease dementia of 1% to 2% per year. OBJECTIVES To assess the diagnostic accuracy of structural magnetic resonance imaging (MRI) for the early diagnosis of dementia due to Alzheimer's disease in people with MCI versus the clinical follow-up diagnosis of Alzheimer's disease dementia as a reference standard (delayed verification). To investigate sources of heterogeneity in accuracy, such as the use of qualitative visual assessment or quantitative volumetric measurements, including manual or automatic (MRI) techniques, or the length of follow-up, and age of participants. MRI was evaluated as an add-on test in addition to clinical diagnosis of MCI to improve early diagnosis of dementia due to Alzheimer's disease in people with MCI. SEARCH METHODS On 29 January 2019 we searched Cochrane Dementia and Cognitive Improvement's Specialised Register and the databases, MEDLINE, Embase, BIOSIS Previews, Science Citation Index, PsycINFO, and LILACS. We also searched the reference lists of all eligible studies identified by the electronic searches. SELECTION CRITERIA We considered cohort studies of any size that included prospectively recruited people of any age with a diagnosis of MCI. We included studies that compared the diagnostic test accuracy of baseline structural MRI versus the clinical follow-up diagnosis of Alzheimer's disease dementia (delayed verification). We did not exclude studies on the basis of length of follow-up. We included studies that used either qualitative visual assessment or quantitative volumetric measurements of MRI to detect atrophy in the whole brain or in specific brain regions, such as the hippocampus, medial temporal lobe, lateral ventricles, entorhinal cortex, medial temporal gyrus, lateral temporal lobe, amygdala, and cortical grey matter. DATA COLLECTION AND ANALYSIS Four teams of two review authors each independently reviewed titles and abstracts of articles identified by the search strategy. Two teams of two review authors each independently assessed the selected full-text articles for eligibility, extracted data and solved disagreements by consensus. Two review authors independently assessed the quality of studies using the QUADAS-2 tool. We used the hierarchical summary receiver operating characteristic (HSROC) model to fit summary ROC curves and to obtain overall measures of relative accuracy in subgroup analyses. We also used these models to obtain pooled estimates of sensitivity and specificity when sufficient data sets were available. MAIN RESULTS We included 33 studies, published from 1999 to 2019, with 3935 participants of whom 1341 (34%) progressed to Alzheimer's disease dementia and 2594 (66%) did not. Of the participants who did not progress to Alzheimer's disease dementia, 2561 (99%) remained stable MCI and 33 (1%) progressed to other types of dementia. The median proportion of women was 53% and the mean age of participants ranged from 63 to 87 years (median 73 years). The mean length of clinical follow-up ranged from 1 to 7.6 years (median 2 years). Most studies were of poor methodological quality due to risk of bias for participant selection or the index test, or both. Most of the included studies reported data on the volume of the total hippocampus (pooled mean sensitivity 0.73 (95% confidence interval (CI) 0.64 to 0.80); pooled mean specificity 0.71 (95% CI 0.65 to 0.77); 22 studies, 2209 participants). This evidence was of low certainty due to risk of bias and inconsistency. Seven studies reported data on the atrophy of the medial temporal lobe (mean sensitivity 0.64 (95% CI 0.53 to 0.73); mean specificity 0.65 (95% CI 0.51 to 0.76); 1077 participants) and five studies on the volume of the lateral ventricles (mean sensitivity 0.57 (95% CI 0.49 to 0.65); mean specificity 0.64 (95% CI 0.59 to 0.70); 1077 participants). This evidence was of moderate certainty due to risk of bias. Four studies with 529 participants analysed the volume of the total entorhinal cortex and four studies with 424 participants analysed the volume of the whole brain. We did not estimate pooled sensitivity and specificity for the volume of these two regions because available data were sparse and heterogeneous. We could not statistically evaluate the volumes of the lateral temporal lobe, amygdala, medial temporal gyrus, or cortical grey matter assessed in small individual studies. We found no evidence of a difference between studies in the accuracy of the total hippocampal volume with regards to duration of follow-up or age of participants, but the manual MRI technique was superior to automatic techniques in mixed (mostly indirect) comparisons. We did not assess the relative accuracy of the volumes of different brain regions measured by MRI because only indirect comparisons were available, studies were heterogeneous, and the overall accuracy of all regions was moderate. AUTHORS' CONCLUSIONS The volume of hippocampus or medial temporal lobe, the most studied brain regions, showed low sensitivity and specificity and did not qualify structural MRI as a stand-alone add-on test for an early diagnosis of dementia due to Alzheimer's disease in people with MCI. This is consistent with international guidelines, which recommend imaging to exclude non-degenerative or surgical causes of cognitive impairment and not to diagnose dementia due to Alzheimer's disease. In view of the low quality of most of the included studies, the findings of this review should be interpreted with caution. Future research should not focus on a single biomarker, but rather on combinations of biomarkers to improve an early diagnosis of Alzheimer's disease dementia.
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Affiliation(s)
- Gemma Lombardi
- University of FlorenceDepartment of Neurosciences, Psychology, Drug Research and Child Health (NEUROFARBA)Largo Brambilla, 3FlorenceItaly50134
| | - Giada Crescioli
- University of FlorenceDepartment of Neurosciences, Psychology, Drug Research and Child Health (NEUROFARBA)Largo Brambilla, 3FlorenceItaly50134
| | - Enrica Cavedo
- Pitie‐Salpetriere Hospital, Sorbonne UniversityAlzheimer Precision Medicine (APM), AP‐HP47 boulevard de l'HopitalParisFrance75013
| | - Ersilia Lucenteforte
- University of PisaDepartment of Clinical and Experimental MedicineVia Savi 10PisaItaly56126
| | - Giovanni Casazza
- Università degli Studi di MilanoDipartimento di Scienze Biomediche e Cliniche "L. Sacco"via GB Grassi 74MilanItaly20157
| | | | - Chiara Lista
- Fondazione I.R.C.C.S. Istituto Neurologico Carlo BestaNeuroepidemiology UnitVia Celoria, 11MilanoItaly20133
| | - Giorgio Costantino
- Ospedale Maggiore Policlinico, Università degli Studi di MilanoUOC Pronto Soccorso e Medicina D'Urgenza, Fondazione IRCCS Ca' GrandaMilanItaly
| | | | - Gianni Virgili
- University of FlorenceDepartment of Neurosciences, Psychology, Drug Research and Child Health (NEUROFARBA)Largo Brambilla, 3FlorenceItaly50134
| | - Graziella Filippini
- Carlo Besta Foundation and Neurological InstituteScientific Director’s Officevia Celoria, 11MilanItaly20133
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Tanveer M, Richhariya B, Khan RU, Rashid AH, Khanna P, Prasad M, Lin CT. Machine Learning Techniques for the Diagnosis of Alzheimer’s Disease. ACM TRANSACTIONS ON MULTIMEDIA COMPUTING, COMMUNICATIONS, AND APPLICATIONS 2020; 16:1-35. [DOI: 10.1145/3344998] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/01/2019] [Accepted: 07/01/2019] [Indexed: 08/30/2023]
Abstract
Alzheimer’s disease is an incurable neurodegenerative disease primarily affecting the elderly population. Efficient automated techniques are needed for early diagnosis of Alzheimer’s. Many novel approaches are proposed by researchers for classification of Alzheimer’s disease. However, to develop more efficient learning techniques, better understanding of the work done on Alzheimer’s is needed. Here, we provide a review on 165 papers from 2005 to 2019, using various feature extraction and machine learning techniques. The machine learning techniques are surveyed under three main categories: support vector machine (SVM), artificial neural network (ANN), and deep learning (DL) and ensemble methods. We present a detailed review on these three approaches for Alzheimer’s with possible future directions.
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Affiliation(s)
- M. Tanveer
- Discipline of Mathematics, Indian Institute of Technology Indore, Simrol, Indore, India
| | - B. Richhariya
- Discipline of Mathematics, Indian Institute of Technology Indore, Simrol, Indore, India
| | - R. U. Khan
- Discipline of Mathematics, Indian Institute of Technology Indore, Simrol, Indore, India
| | - A. H. Rashid
- Discipline of Mathematics, Indian Institute of Technology Indore, Simrol, Indore 8 School of Computer Science and Engineering, National Institute of Science and Technology, Berhampur, Odisha, India
| | - P. Khanna
- PDPM Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, India
| | - M. Prasad
- Centre for Artificial Intelligence, School of Computer Science, FEIT, University of Technology Sydney, Sydney, Australia
| | - C. T. Lin
- Centre for Artificial Intelligence, School of Computer Science, FEIT, University of Technology Sydney, Sydney, Australia
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45
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Estimation of connectional brain templates using selective multi-view network normalization. Med Image Anal 2020; 59:101567. [DOI: 10.1016/j.media.2019.101567] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2019] [Revised: 09/23/2019] [Accepted: 09/27/2019] [Indexed: 11/19/2022]
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Shen L, Thompson PM. Brain Imaging Genomics: Integrated Analysis and Machine Learning. PROCEEDINGS OF THE IEEE. INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS 2020; 108:125-162. [PMID: 31902950 PMCID: PMC6941751 DOI: 10.1109/jproc.2019.2947272] [Citation(s) in RCA: 76] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
Brain imaging genomics is an emerging data science field, where integrated analysis of brain imaging and genomics data, often combined with other biomarker, clinical and environmental data, is performed to gain new insights into the phenotypic, genetic and molecular characteristics of the brain as well as their impact on normal and disordered brain function and behavior. It has enormous potential to contribute significantly to biomedical discoveries in brain science. Given the increasingly important role of statistical and machine learning in biomedicine and rapidly growing literature in brain imaging genomics, we provide an up-to-date and comprehensive review of statistical and machine learning methods for brain imaging genomics, as well as a practical discussion on method selection for various biomedical applications.
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Affiliation(s)
- Li Shen
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, PA 19104, USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA 90232, USA
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47
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Staffaroni AM, Cobigo Y, Goh SYM, Kornak J, Bajorek L, Chiang K, Appleby B, Bove J, Bordelon Y, Brannelly P, Brushaber D, Caso C, Coppola G, Dever R, Dheel C, Dickerson BC, Dickinson S, Dominguez S, Domoto-Reilly K, Faber K, Ferrall J, Fields JA, Fishman A, Fong J, Foroud T, Forsberg LK, Gavrilova R, Gearhart D, Ghazanfari B, Ghoshal N, Goldman J, Graff-Radford J, Graff-Radford N, Grant I, Grossman M, Haley D, Heuer HW, Hsiung GY, Huey ED, Irwin DJ, Jones DT, Jones L, Kantarci K, Karydas A, Kaufer DI, Kerwin DR, Knopman DS, Kraft R, Kramer JH, Kremers WK, Kukull WA, Litvan I, Ljubenkov PA, Lucente D, Lungu C, Mackenzie IR, Maldonado M, Manoochehri M, McGinnis SM, McKinley E, Mendez MF, Miller BL, Multani N, Onyike C, Padmanabhan J, Pantelyat A, Pearlman R, Petrucelli L, Potter M, Rademakers R, Ramos EM, Rankin KP, Rascovsky K, Roberson ED, Rogalski E, Sengdy P, Shaw LM, Syrjanen J, Tartaglia MC, Tatton N, Taylor J, Toga A, Trojanowski JQ, Weintraub S, Wang P, Wong B, Wszolek Z, Boxer AL, Boeve BF, Rosen HJ. Individualized atrophy scores predict dementia onset in familial frontotemporal lobar degeneration. Alzheimers Dement 2020; 16:37-48. [PMID: 31272932 PMCID: PMC6938544 DOI: 10.1016/j.jalz.2019.04.007] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
INTRODUCTION Some models of therapy for neurodegenerative diseases envision starting treatment before symptoms develop. Demonstrating that such treatments are effective requires accurate knowledge of when symptoms would have started without treatment. Familial frontotemporal lobar degeneration offers a unique opportunity to develop predictors of symptom onset. METHODS We created dementia risk scores in 268 familial frontotemporal lobar degeneration family members by entering covariate-adjusted standardized estimates of brain atrophy into a logistic regression to classify asymptomatic versus demented participants. The score's predictive value was tested in a separate group who were followed up longitudinally (stable vs. converted to dementia) using Cox proportional regressions with dementia risk score as the predictor. RESULTS Cross-validated logistic regression achieved good separation of asymptomatic versus demented (accuracy = 90%, SE = 0.06). Atrophy scores predicted conversion from asymptomatic or mildly/questionably symptomatic to dementia (HR = 1.51, 95% CI: [1.16,1.98]). DISCUSSION Individualized quantification of baseline brain atrophy is a promising predictor of progression in asymptomatic familial frontotemporal lobar degeneration mutation carriers.
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Affiliation(s)
- Adam M. Staffaroni
- Department of Neurology, Memory and Aging Center, University of California, San Francisco, San Francisco CA, USA
| | - Yann Cobigo
- Department of Neurology, Memory and Aging Center, University of California, San Francisco, San Francisco CA, USA
| | - Sheng-Yang M. Goh
- Department of Neurology, Memory and Aging Center, University of California, San Francisco, San Francisco CA, USA
| | - John Kornak
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco CA, USA
| | - Lynn Bajorek
- Department of Neurology, Memory and Aging Center, University of California, San Francisco, San Francisco CA, USA
| | - Kevin Chiang
- Department of Neurology, Memory and Aging Center, University of California, San Francisco, San Francisco CA, USA
| | - Brian Appleby
- Department of Neurology, Case Western Reserve University, Cleveland, OH, USA
| | - Jessica Bove
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yvette Bordelon
- Department of Neurology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Patrick Brannelly
- Tau Consortium, Rainwater Charitable Foundation, Fort Worth, TX, USA
| | | | - Christina Caso
- Department of Neurology, University of Washington, Seattle, WA, USA
| | - Giovanni Coppola
- Department of Neurology, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, USA
| | - Reilly Dever
- Department of Neurology, Memory and Aging Center, University of California, San Francisco, San Francisco CA, USA
| | | | - Bradford C. Dickerson
- Department of Neurology, Frontotemporal Disorders Unit, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Susan Dickinson
- Association for Frontotemporal Degeneration, Radnor, PA, USA
| | - Sophia Dominguez
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Kelly Faber
- National Cell Repository for Alzheimer’s Disease (NCRAD), Indiana University, Indianapolis, IN, USA
| | - Jessica Ferrall
- Department of Neurology, University of North Carolina, Chapel Hill, NC, USA
| | - Julie A. Fields
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
| | - Ann Fishman
- Department of Psychiatry, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Jamie Fong
- Department of Neurology, Memory and Aging Center, University of California, San Francisco, San Francisco CA, USA
| | - Tatiana Foroud
- National Cell Repository for Alzheimer’s Disease (NCRAD), Indiana University, Indianapolis, IN, USA
| | | | | | - Debra Gearhart
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - Behnaz Ghazanfari
- Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, Ontario, Canada
- Division of Neurology, Department of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Nupur Ghoshal
- Departments of Neurology and Psychiatry, Washington University School of Medicine, Washington University, St. Louis, MO, USA
| | - Jill Goldman
- Department of Neurology, Columbia University, New York, NY, USA
- Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University, New York, NY, USA
| | | | | | - Ian Grant
- Department of Neurology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Murray Grossman
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Dana Haley
- Department of Neurology, Mayo Clinic, Jacksonville, FL, USA
| | - Hilary W. Heuer
- Department of Neurology, Memory and Aging Center, University of California, San Francisco, San Francisco CA, USA
| | - Ging-Yuek Hsiung
- Division of Neurology, Department of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
| | - Edward D. Huey
- Department of Neurology, Columbia University, New York, NY, USA
- Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University, New York, NY, USA
| | - David J. Irwin
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - David T. Jones
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - Lynne Jones
- Department of Radiology, Washington University School of Medicine, Washington University, St. Louis, MO, USA
| | - Kejal Kantarci
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - Anna Karydas
- Department of Neurology, Memory and Aging Center, University of California, San Francisco, San Francisco CA, USA
| | - Daniel I. Kaufer
- Department of Neurology, University of North Carolina, Chapel Hill, NC, USA
| | - Diana R. Kerwin
- Department of Neurology and Neurotherapeutics, Center for Alzheimer’s and Neurodegenerative Diseases, The University of Texas, Southwestern Medical Center at Dallas, Dallas, TX, USA
- Department of Internal Medicine, The University of Texas, Southwestern Medical Center at Dallas, Dallas, TX, USA
| | | | - Ruth Kraft
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - Joel H. Kramer
- Department of Neurology, Memory and Aging Center, University of California, San Francisco, San Francisco CA, USA
| | - Walter K. Kremers
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Walter A. Kukull
- National Alzheimer Coordinating Center (NACC), University of Washington, Seattle, WA, USA
| | - Irene Litvan
- Department of Neurosciences, Parkinson and Other Movement Disorders Center, University of California, San Diego, San Diego, CA, USA
| | - Peter A. Ljubenkov
- Department of Neurology, Memory and Aging Center, University of California, San Francisco, San Francisco CA, USA
| | - Diane Lucente
- Department of Neurology, Frontotemporal Disorders Unit, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Codrin Lungu
- National Institute of Neurological Disorders and Stroke (NINDS), Bethesda, MD, USA
| | - Ian R. Mackenzie
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia, Canada
| | - Miranda Maldonado
- Department of Neurology, University of California, Los Angeles, Los Angeles, CA, USA
| | | | - Scott M. McGinnis
- Department of Neurology, Frontotemporal Disorders Unit, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Emily McKinley
- Department of Neurology, Alzheimer’s Disease Center, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Mario F. Mendez
- Department of Neurology, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, USA
| | - Bruce L. Miller
- Department of Neurology, Memory and Aging Center, University of California, San Francisco, San Francisco CA, USA
| | - Namita Multani
- Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, Ontario, Canada
- Division of Neurology, Department of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Chiadi Onyike
- Department of Geriatric Psychiatry and Neuropsychiatry, Johns Hopkins University, Baltimore, MD, USA
| | - Jaya Padmanabhan
- Department of Neurology, Frontotemporal Disorders Unit, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Alex Pantelyat
- Department of Neurology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | | | - Len Petrucelli
- Department of Neurosciences, Mayo Clinic, Jacksonville, FL, USA
| | - Madeline Potter
- National Cell Repository for Alzheimer’s Disease (NCRAD), Indiana University, Indianapolis, IN, USA
| | - Rosa Rademakers
- Department of Neurosciences, Mayo Clinic, Jacksonville, FL, USA
| | - Eliana Marisa Ramos
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, USA
| | - Katherine P. Rankin
- Department of Neurology, Memory and Aging Center, University of California, San Francisco, San Francisco CA, USA
| | - Katya Rascovsky
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Erik D. Roberson
- Department of Neurology, Alzheimer’s Disease Center, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Emily Rogalski
- Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Pheth Sengdy
- Division of Neurology, Department of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
| | - Leslie M. Shaw
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jeremy Syrjanen
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - M. Carmela Tartaglia
- Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, Ontario, Canada
- Division of Neurology, Department of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Nadine Tatton
- Association for Frontotemporal Degeneration, Radnor, PA, USA
| | - Joanne Taylor
- Department of Neurology, Memory and Aging Center, University of California, San Francisco, San Francisco CA, USA
| | - Arthur Toga
- Departments of Ophthalmology, Neurology, Psychiatry and the Behavioral Sciences, Radiology and Engineering, Laboratory of Neuroimaging (LONI), USC, Los Angeles, CA, USA
| | - John Q. Trojanowski
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sandra Weintraub
- Department of Neurology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Ping Wang
- Department of Neurology, Memory and Aging Center, University of California, San Francisco, San Francisco CA, USA
| | - Bonnie Wong
- Department of Neurology, Frontotemporal Disorders Unit, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Adam L. Boxer
- Department of Neurology, Memory and Aging Center, University of California, San Francisco, San Francisco CA, USA
| | - Brad F. Boeve
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - Howard J. Rosen
- Department of Neurology, Memory and Aging Center, University of California, San Francisco, San Francisco CA, USA
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Khan S, Barve KH, Kumar MS. Recent Advancements in Pathogenesis, Diagnostics and Treatment of Alzheimer's Disease. Curr Neuropharmacol 2020; 18:1106-1125. [PMID: 32484110 PMCID: PMC7709159 DOI: 10.2174/1570159x18666200528142429] [Citation(s) in RCA: 262] [Impact Index Per Article: 65.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Revised: 05/06/2020] [Accepted: 05/25/2020] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND The only conclusive way to diagnose Alzheimer's is to carry out brain autopsy of the patient's brain tissue and ascertain whether the subject had Alzheimer's or any other form of dementia. However, due to the non-feasibility of such methods, to diagnose and conclude the conditions, medical practitioners use tests that examine a patient's mental ability. OBJECTIVE Accurate diagnosis at an early stage is the need of the hour for initiation of therapy. The cause for most Alzheimer's cases still remains unknown except where genetic distinctions have been observed. Thus, a standard drug regimen ensues in every Alzheimer's patient, irrespective of the cause, which may not always be beneficial in halting or reversing the disease progression. To provide a better life to such patients by suppressing existing symptoms, early diagnosis, curative therapy, site-specific delivery of drugs, and application of hyphenated methods like artificial intelligence need to be brought into the main field of Alzheimer's therapeutics. METHODS In this review, we have compiled existing hypotheses to explain the cause of the disease, and highlighted gene therapy, immunotherapy, peptidomimetics, metal chelators, probiotics and quantum dots as advancements in the existing strategies to manage Alzheimer's. CONCLUSION Biomarkers, brain-imaging, and theranostics, along with artificial intelligence, are understood to be the future of the management of Alzheimer's.
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Affiliation(s)
- Sahil Khan
- SVKM’S NMIMS, Shobhaben Pratapbhai Patel School of Pharmacy and Technology Management, V.L. Mehta Road, Vile Parle West, Mumbai-400056, India
| | - Kalyani H. Barve
- SVKM’S NMIMS, Shobhaben Pratapbhai Patel School of Pharmacy and Technology Management, V.L. Mehta Road, Vile Parle West, Mumbai-400056, India
| | - Maushmi S. Kumar
- SVKM’S NMIMS, Shobhaben Pratapbhai Patel School of Pharmacy and Technology Management, V.L. Mehta Road, Vile Parle West, Mumbai-400056, India
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49
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The association between hippocampal subfield volumes in mild cognitive impairment and conversion to Alzheimer's disease. Brain Res 2019; 1728:146591. [PMID: 31816319 DOI: 10.1016/j.brainres.2019.146591] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2019] [Revised: 11/25/2019] [Accepted: 12/04/2019] [Indexed: 11/20/2022]
Abstract
The hippocampal complex, strongly implicated in Alzheimer's disease (AD), is a region with functionally and structurally distinct subfields. Hippocampal subfield volumes may represent a more sensitive indicators of AD development than total hippocampal volume. We aimed to identify which subfield is the most predictive measure for an AD diagnosis and which is the most specific indicator of the conversion from mild cognitive impairment (MCI) to AD. We analyzed longitudinal structural neuroimaging data of 1350 individuals, 350 healthy controls (HC), 650 MCI and 350 AD, using FreeSurfer v6.0. The linear regression models corrected for total hippocampal volume revealed that subicular fields are the most predictive measures of AD diagnosis. Hippocampal fissure volume was significantly associated with conversion from MCI to AD. Our findings suggest that subicular hippocampal fields are most predictive of AD diagnosis, which has clinical implications for early detection. Specifically, subicular and hippocampal fissure volume measures may be used to select MCI participants who are most likely to convert in AD in clinical trials.
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50
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Wang Z, Williams VJ, Stephens KA, Kim CM, Bai L, Zhang M, Salat DH. The effect of white matter signal abnormalities on default mode network connectivity in mild cognitive impairment. Hum Brain Mapp 2019; 41:1237-1248. [PMID: 31742814 PMCID: PMC7267894 DOI: 10.1002/hbm.24871] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2019] [Revised: 10/04/2019] [Accepted: 11/12/2019] [Indexed: 01/18/2023] Open
Abstract
Regions within the default mode network (DMN) are particularly vulnerable to Alzheimer's disease pathology and mechanisms of DMN disruption in mild cognitive impairment (MCI) are still unclear. White matter lesions are presumed to be mechanistically linked to vascular dysfunction whereas cortical atrophy may be related to neurodegeneration. We examined associations between DMN seed‐based connectivity, white matter lesion load, and cortical atrophy in MCI and cognitively healthy controls. MCI showed decreased functional connectivity (FC) between the precuneus‐seed and bilateral lateral temporal cortex (LTC), medial prefrontal cortex (mPFC), posterior cingulate cortex, and inferior parietal lobe compared to those with controls. When controlling for white matter lesion volume, DMN connectivity differences between groups were diminished within bilateral LTC, although were significantly increased in the mPFC explained by significant regional associations between white matter lesion volume and DMN connectivity only in the MCI group. When controlling for cortical thickness, DMN FC was similarly decreased across both groups. These findings suggest that white matter lesions and cortical atrophy are differentially associated with alterations in FC patterns in MCI. Associations between white matter lesions and DMN connectivity in MCI further support at least a partial but important vascular contribution to age‐associated neural and cognitive impairment.
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Affiliation(s)
- Zhuonan Wang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts.,Department of Medical Imaging, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Victoria J Williams
- Alzheimer's Clinical and Translational Research Unit, Department of Neurology, Massachusetts General Hospital, Charlestown, Massachusetts
| | - Kimberly A Stephens
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts
| | - Chan-Mi Kim
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts
| | - Lijun Bai
- The Key Laboratory of Biomedical Information Engineering, Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Ming Zhang
- Department of Medical Imaging, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - David H Salat
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts.,Neuroimaging Research for Veterans Center, VA Boston Healthcare System, Boston, Massachusetts
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