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Liu H, Meng L, Wang J, Qin C, Feng R, Chen Y, Chen P, Zhu Q, Ma M, Teng J, Ding X. Enlarged perivascular spaces in alcohol-related brain damage induced by dyslipidemia. J Cereb Blood Flow Metab 2024:271678X241251570. [PMID: 38700501 DOI: 10.1177/0271678x241251570] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/05/2024]
Abstract
Perivascular spaces (PVSs) as the anatomical basis of the glymphatic system, are increasingly recognized as potential imaging biomarkers of neurological conditions. However, it is not clear whether enlarged PVSs are associated with alcohol-related brain damage (ARBD). We aimed to investigate the effect of long-term alcohol exposure on dyslipidemia and the glymphatic system in ARBD. We found that patients with ARBD exhibited significantly enlargement of PVSs in the frontal cortex and basal ganglia, as well as a notable increased levels of total cholesterol (TC) and triglycerides (TG). The anatomical changes of the glymphatic drainage system mentioned above were positively associated with TC and TG. To further explore whether enlarged PVSs affects the function of the glymphatic system in ARBD, we constructed long alcohol exposure and high fat diet mice models. The mouse model of long alcohol exposure exhibited increased levels of TC and TG, enlarged PVSs, the loss of aquaporin-4 polarity caused by reactive astrocytes and impaired glymphatic drainage function which ultimately caused cognitive deficits, in a similar way as high fat diet leading to impairment in glymphatic drainage. Our study highlights the contribution of dyslipidemia due to long-term alcohol abuse in the impairment of the glymphatic drainage system.
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Affiliation(s)
- Han Liu
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450000, China
- Henan Key Laboratory of Chronic Disease Prevention and Therapy & Intelligent Health Management, Henan 450052, China
| | - Lin Meng
- Department of Neurology, Zhengzhou Central Hospital, Zhengzhou, Henan 450000, China
| | - Jiuqi Wang
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450000, China
- Henan Key Laboratory of Chronic Disease Prevention and Therapy & Intelligent Health Management, Henan 450052, China
| | - Chi Qin
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450000, China
- Henan Key Laboratory of Chronic Disease Prevention and Therapy & Intelligent Health Management, Henan 450052, China
| | - Renyi Feng
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450000, China
- Henan Key Laboratory of Chronic Disease Prevention and Therapy & Intelligent Health Management, Henan 450052, China
| | - Yongkang Chen
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450000, China
- Henan Key Laboratory of Chronic Disease Prevention and Therapy & Intelligent Health Management, Henan 450052, China
| | - Pei Chen
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450000, China
- Henan Key Laboratory of Chronic Disease Prevention and Therapy & Intelligent Health Management, Henan 450052, China
| | - Qingyong Zhu
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450000, China
- Henan Key Laboratory of Chronic Disease Prevention and Therapy & Intelligent Health Management, Henan 450052, China
| | - Mingming Ma
- Department of Neurology, Henan Provincial People's Hospital, Zhengzhou, Henan 450000, China
| | - Junfang Teng
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450000, China
- Henan Key Laboratory of Chronic Disease Prevention and Therapy & Intelligent Health Management, Henan 450052, China
| | - Xuebing Ding
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450000, China
- Henan Key Laboratory of Chronic Disease Prevention and Therapy & Intelligent Health Management, Henan 450052, China
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Liu R, Berry R, Wang L, Chaudhari K, Winters A, Sun Y, Caballero C, Ampofo H, Shi Y, Thata B, Colon-Perez L, Sumien N, Yang SH. Experimental Ischemic Stroke Induces Secondary Bihemispheric White Matter Degeneration and Long-Term Cognitive Impairment. Transl Stroke Res 2024:10.1007/s12975-024-01241-0. [PMID: 38488999 DOI: 10.1007/s12975-024-01241-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 02/22/2024] [Accepted: 03/08/2024] [Indexed: 03/17/2024]
Abstract
Clinical studies have identified widespread white matter degeneration in ischemic stroke patients. However, contemporary research in stroke has predominately focused on the infarct and periinfarct penumbra regions. The involvement of white matter degeneration after ischemic stroke and its contribution to post-stroke cognitive impairment and dementia (PSCID) has remained less explored in experimental models. In this study, we examined the progression of locomotor and cognitive function up to 4 months after inducing ischemic stroke by middle cerebral artery occlusion in young adult rats. Despite evident ongoing locomotor recovery, long-term cognitive and affective impairments persisted after ischemic stroke, as indicated by Morris water maze, elevated plus maze, and open field performance. At 4 months after stroke, multimodal MRI was conducted to assess white matter degeneration. T2-weighted MRI (T2WI) unveiled bilateral cerebroventricular enlargement after ischemic stroke. Fluid Attenuated Inversion Recovery MRI (FLAIR) revealed white matter hyperintensities in the corpus callosum and fornix across bilateral hemispheres. A positive association between the volume of white matter hyperintensities and total cerebroventricular volume was noted in stroke rats. Further evidence of bilateral white matter degeneration was indicated by the reduction of fractional anisotropy and quantitative anisotropy at bilateral corpus callosum in diffusion-weighted MRI (DWI) analysis. Additionally, microglia and astrocyte activation were identified in the bilateral corpus callosum after stroke. Our study suggests that experimental ischemic stroke induced by MCAO in young rat replicate long-term cognitive impairment and bihemispheric white matter degeneration observed in ischemic stroke patients. This model provides an invaluable tool for unraveling the mechanisms underlying post-stroke secondary white matter degeneration and its contribution to PSCID.
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Affiliation(s)
- Ran Liu
- Department of Pharmacology and Neuroscience, University of North Texas Health Science Center, 3500 Camp Bowie Blvd, Fort Worth, TX, 76107, USA
| | - Raymond Berry
- Department of Pharmacology and Neuroscience, University of North Texas Health Science Center, 3500 Camp Bowie Blvd, Fort Worth, TX, 76107, USA
| | - Linshu Wang
- Department of Pharmacology and Neuroscience, University of North Texas Health Science Center, 3500 Camp Bowie Blvd, Fort Worth, TX, 76107, USA
| | - Kiran Chaudhari
- Department of Pharmacology and Neuroscience, University of North Texas Health Science Center, 3500 Camp Bowie Blvd, Fort Worth, TX, 76107, USA
| | - Ali Winters
- Department of Pharmacology and Neuroscience, University of North Texas Health Science Center, 3500 Camp Bowie Blvd, Fort Worth, TX, 76107, USA
| | - Yuanhong Sun
- Department of Pharmacology and Neuroscience, University of North Texas Health Science Center, 3500 Camp Bowie Blvd, Fort Worth, TX, 76107, USA
| | - Claire Caballero
- Department of Pharmacology and Neuroscience, University of North Texas Health Science Center, 3500 Camp Bowie Blvd, Fort Worth, TX, 76107, USA
| | - Hannah Ampofo
- Department of Pharmacology and Neuroscience, University of North Texas Health Science Center, 3500 Camp Bowie Blvd, Fort Worth, TX, 76107, USA
| | - Yiwei Shi
- Department of Pharmacology and Neuroscience, University of North Texas Health Science Center, 3500 Camp Bowie Blvd, Fort Worth, TX, 76107, USA
| | - Bibek Thata
- Department of Pharmacology and Neuroscience, University of North Texas Health Science Center, 3500 Camp Bowie Blvd, Fort Worth, TX, 76107, USA
| | - Luis Colon-Perez
- Department of Pharmacology and Neuroscience, University of North Texas Health Science Center, 3500 Camp Bowie Blvd, Fort Worth, TX, 76107, USA
| | - Nathalie Sumien
- Department of Pharmacology and Neuroscience, University of North Texas Health Science Center, 3500 Camp Bowie Blvd, Fort Worth, TX, 76107, USA
| | - Shao-Hua Yang
- Department of Pharmacology and Neuroscience, University of North Texas Health Science Center, 3500 Camp Bowie Blvd, Fort Worth, TX, 76107, USA.
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Wrzesień A, Andrzejewski K, Jampolska M, Kaczyńska K. Respiratory Dysfunction in Alzheimer's Disease-Consequence or Underlying Cause? Applying Animal Models to the Study of Respiratory Malfunctions. Int J Mol Sci 2024; 25:2327. [PMID: 38397004 PMCID: PMC10888758 DOI: 10.3390/ijms25042327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Revised: 02/09/2024] [Accepted: 02/14/2024] [Indexed: 02/25/2024] Open
Abstract
Alzheimer's disease (AD) is a neurodegenerative brain disease that is the most common cause of dementia among the elderly. In addition to dementia, which is the loss of cognitive function, including thinking, remembering, and reasoning, and behavioral abilities, AD patients also experience respiratory disturbances. The most common respiratory problems observed in AD patients are pneumonia, shortness of breath, respiratory muscle weakness, and obstructive sleep apnea (OSA). The latter is considered an outcome of Alzheimer's disease and is suggested to be a causative factor. While this narrative review addresses the bidirectional relationship between obstructive sleep apnea and Alzheimer's disease and reports on existing studies describing the most common respiratory disorders found in patients with Alzheimer's disease, its main purpose is to review all currently available studies using animal models of Alzheimer's disease to study respiratory impairments. These studies on animal models of AD are few in number but are crucial for establishing mechanisms, causation, implementing potential therapies for respiratory disorders, and ultimately applying these findings to clinical practice. This review summarizes what is already known in the context of research on respiratory disorders in animal models, while pointing out directions for future research.
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Affiliation(s)
| | | | | | - Katarzyna Kaczyńska
- Department of Respiration Physiology, Mossakowski Medical Research Institute, Polish Academy of Sciences, 02-106 Warsaw, Poland; (A.W.); (K.A.); (M.J.)
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Ferrante M, Boccato T, Toschi N. Enabling uncertainty estimation in neural networks through weight perturbation for improved Alzheimer's disease classification. Front Neuroinform 2024; 18:1346723. [PMID: 38380126 PMCID: PMC10876844 DOI: 10.3389/fninf.2024.1346723] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Accepted: 01/15/2024] [Indexed: 02/22/2024] Open
Abstract
Background The willingness to trust predictions formulated by automatic algorithms is key in a wide range of domains. However, a vast number of deep architectures are only able to formulate predictions without associated uncertainty. Purpose In this study, we propose a method to convert a standard neural network into a Bayesian neural network and estimate the variability of predictions by sampling different networks similar to the original one at each forward pass. Methods We combine our method with a tunable rejection-based approach that employs only the fraction of the data, i.e., the share that the model can classify with an uncertainty below a user-set threshold. We test our model in a large cohort of brain images from patients with Alzheimer's disease and healthy controls, discriminating the former and latter classes based on morphometric images exclusively. Results We demonstrate how combining estimated uncertainty with a rejection-based approach increases classification accuracy from 0.86 to 0.95 while retaining 75% of the test set. In addition, the model can select the cases to be recommended for, e.g., expert human evaluation due to excessive uncertainty. Importantly, our framework circumvents additional workload during the training phase by using our network "turned into Bayesian" to implicitly investigate the loss landscape in the neighborhood of each test sample in order to determine the reliability of the predictions. Conclusion We believe that being able to estimate the uncertainty of a prediction, along with tools that can modulate the behavior of the network to a degree of confidence that the user is informed about (and comfortable with), can represent a crucial step in the direction of user compliance and easier integration of deep learning tools into everyday tasks currently performed by human operators.
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Affiliation(s)
- Matteo Ferrante
- Department of Biomedicine and Prevention, University of Rome Tor Vergata, Rome, Italy
| | - Tommaso Boccato
- Department of Biomedicine and Prevention, University of Rome Tor Vergata, Rome, Italy
| | - Nicola Toschi
- Department of Biomedicine and Prevention, University of Rome Tor Vergata, Rome, Italy
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
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Wong D, Bellyou M, Li A, Prado MAM, Beauchet O, Annweiler C, Montero-Odasso M, Bartha R. Magnetic resonance spectroscopy in the hippocampus of adult APP/PS1 mice following chronic vitamin D deficiency. Behav Brain Res 2024; 457:114713. [PMID: 37838248 DOI: 10.1016/j.bbr.2023.114713] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 09/28/2023] [Accepted: 10/10/2023] [Indexed: 10/16/2023]
Abstract
Vitamin D (VitD) deficiency can exacerbate AD progression and may cause changes in brain metabolite levels that can be detected by magnetic resonance spectroscopy (MRS). The purpose of this study was to determine whether chronic VitD deficiency in an AD mouse model caused persistent metabolite levels changes in the hippocampus associated with memory performance. Six-month-old APPSwe/PS1ΔE9 (APP/PS1) mice (N = 14 mice/group) were fed either a VitD deficient (VitD-) diet or a control diet. Metabolite level changes in the hippocampus were evaluated by 1H MRS using a 9.4 T MRI. Ventricle volume was assessed by imaging and spatial memory was evaluated using the Barnes maze. All measurements were made at 6, 9, 12, and 15 months of age. At 15 months of age, amyloid plaque load and astrocyte number were evaluated histologically (N = 4 mice/group). Levels of N-acetyl aspartate and creatine were lower in VitD- mice compared to control diet mice at 12 months of age. VitD deficiency did not change ventricle volume. Lactate levels increased over time in VitD- mice and increases from 12 to 15 months were negatively correlated with changes in primary latency to the target hole in the Barns Maze. VitD- mice showed improved spatial memory performance compared to control diet mice. VitD- mice also had more astrocytes in the cortex and hippocampus at 15 months than control diet mice. This study suggests that severe VitD deficiency in APP/PS1 mice may lead to compensatory changes in metabolite and astrocyte levels that contribute to improved performance on spatial memory tasks.
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Affiliation(s)
- Dickson Wong
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada; Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - Miranda Bellyou
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - Alex Li
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - Marco A M Prado
- Department of Anatomy and Cell Biology, Western University, London, ON, Canada; Department of Physiology and Pharmacology, Western University, London, ON, Canada
| | | | - Cédric Annweiler
- Department of Geriatric Medicine and Memory Clinic, Research Center on Autonomy and Longevity, University Hospital, Angers, France
| | - Manuel Montero-Odasso
- Department of Medicine, Division of Geriatric Medicine, Parkwood Hospital, Western University, London, ON, Canada; Department of Epidemiology and Biostatistics, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada; Lawson Health Research Institute, London, ON, Canada
| | - Robert Bartha
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada; Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada.
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Chao LL, Sullivan K, Krengel MH, Killiany RJ, Steele L, Klimas NG, Koo BB. The prevalence of mild cognitive impairment in Gulf War veterans: a follow-up study. Front Neurosci 2024; 17:1301066. [PMID: 38318196 PMCID: PMC10838998 DOI: 10.3389/fnins.2023.1301066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2023] [Accepted: 12/18/2023] [Indexed: 02/07/2024] Open
Abstract
Introduction Gulf War Illness (GWI), also called Chronic Multisymptom Illness (CMI), is a multi-faceted condition that plagues an estimated 250,000 Gulf War (GW) veterans. Symptoms of GWI/CMI include fatigue, pain, and cognitive dysfunction. We previously reported that 12% of a convenience sample of middle aged (median age 52 years) GW veterans met criteria for mild cognitive impairment (MCI), a clinical syndrome most prevalent in older adults (e.g., ≥70 years). The current study sought to replicate and extend this finding. Methods We used the actuarial neuropsychological criteria and the Montreal Cognitive Assessment (MoCA) to assess the cognitive status of 952 GW veterans. We also examined regional brain volumes in a subset of GW veterans (n = 368) who had three Tesla magnetic resonance images (MRIs). Results We replicated our previous finding of a greater than 10% rate of MCI in four additional cohorts of GW veterans. In the combined sample of 952 GW veterans (median age 51 years at time of cognitive testing), 17% met criteria for MCI. Veterans classified as MCI were more likely to have CMI, history of depression, and prolonged (≥31 days) deployment-related exposures to smoke from oil well fires and chemical nerve agents compared to veterans with unimpaired and intermediate cognitive status. We also replicated our previous finding of hippocampal atrophy in veterans with MCI, and found significant group differences in lateral ventricle volumes. Discussion Because MCI increases the risk for late-life dementia and impacts quality of life, it may be prudent to counsel GW veterans with cognitive dysfunction, CMI, history of depression, and high levels of exposures to deployment-related toxicants to adopt lifestyle habits that have been associated with lowering dementia risk. With the Food and Drug Administration's recent approval of and the VA's decision to cover the cost for anti-amyloid β (Aβ) therapies, a logical next step for this research is to determine if GW veterans with MCI have elevated Aβ in their brains.
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Affiliation(s)
- Linda L. Chao
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
- Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, San Francisco, CA, United States
- San Francisco Veterans Affairs Health Care System, San Francisco, CA, United States
| | - Kimberly Sullivan
- Department of Environmental Health, Boston University School of Public Health, Boston, MA, United States
| | - Maxine H. Krengel
- Department of Neurology, Boston University School of Medicine, Boston, MA, United States
| | - Ronald J. Killiany
- Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, MA, United States
| | - Lea Steele
- Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, United States
| | - Nancy G. Klimas
- Dr. Kiran C. Patel College of Osteopathic Medicine, Institute for Neuro-Immune Medicine, Nova Southeastern University, Fort Lauderdale, FL, United States
- Geriatric Research Education and Clinical Center (GRECC), Miami VA Medical Center, Miami, FL, United States
| | - Bang-Bong Koo
- Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, MA, United States
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Li M, Ma YH, Guo Y, Liu JY, Tan L. Associations of cerebrospinal fluid complement proteins with Alzheimer's pathology, cognition, and brain structure in non-dementia elderly. Alzheimers Res Ther 2024; 16:12. [PMID: 38238858 PMCID: PMC10795368 DOI: 10.1186/s13195-023-01377-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Accepted: 12/26/2023] [Indexed: 01/22/2024]
Abstract
BACKGROUND Cerebrospinal fluid (CSF) complement activation is a key part of neuroinflammation that occurs in the early stages of Alzheimer's disease (AD). However, the associations of CSF complement proteins with AD pathology, cognition, and structural neuroimaging biomarkers for AD have been rarely investigated. METHODS A total of 210 participants (125 mild cognitive impairment [MCI] patients and 85 normal controls) were included from Alzheimer's Disease Neuroimaging Initiative (ADNI) database who measured AD pathology, cognition, and neuroimaging at baseline and every 12 months. The mixed-effect linear models were utilized to investigate longitudinal associations of CSF complement proteins with AD pathology, cognition, and neuroimaging in cognitively normal (CN) and mild cognitive impairment (MCI) subjects. Causal mediation analyses were conducted to explore the potential mediators between CSF complement proteins and cognitive changes. RESULTS We found that the subjects with low CSF complement protein levels at baseline had worse outcomes in AD pathology, indicated by their lowest concentrations observed in A + and A + T + individuals. The reduced CSF complement proteins were associated with faster accumulation of tau among CN subjects and with cognitive decline and greater brain atrophy of specific regions among MCI subjects. Furthermore, mediation analyses showed that the effects of CSF complement proteins on cognitive performance were partially mediated by regional brain structures (mediation proportions range from 19.78 to 94.92%; p < 0.05). CONCLUSIONS This study demonstrated that CSF complement proteins were involved in the early progression of AD. Our results indicated that regional brain atrophy might be a plausible way to connect CSF complement protein levels and cognition.
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Affiliation(s)
- Meng Li
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Ya-Hui Ma
- Department of Neurology, The Affiliated Hospital of Qingdao University, Qingdao, 266071, China
| | - Yun Guo
- School of Clinical Medicine, Weifang Medical University, Weifang, China
| | - Jia-Yao Liu
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Lan Tan
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China.
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Zhao M, Riaz A, Saied IM, Shami Z, Arslan T. Dual-Planar Monopole Antenna-Based Remote Sensing System for Microwave Medical Applications. Sensors (Basel) 2024; 24:328. [PMID: 38257421 PMCID: PMC10818468 DOI: 10.3390/s24020328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 12/29/2023] [Accepted: 12/30/2023] [Indexed: 01/24/2024]
Abstract
Neurodegenerative diseases (NDs) can be life threatening and have chronic impacts on patients and society. Timely diagnosis and treatment are imperative to prevent deterioration. Conventional imaging modalities, such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and Positron Emission Tomography (PET), are expensive and not readily accessible to patients. Microwave sensing and imaging (MSI) systems are promising tools for monitoring pathological changes, namely the lateral ventricle enlargement associated with ND, in a non-invasive and convenient way. This paper presents a dual-planar monopole antenna-based remote sensing system for ND monitoring. First, planar monopole antennas were designed using the simulation software CST Studio Suite. The antenna analysis was carried out regarding the reflection coefficient, gain, radiation pattern, time domain characterization, E-field distribution, and Specific Absorption Rate (SAR). The designed antennas were then integrated with a controlling circuit as a remote sensing system. The system was experimentally validated on brain phantoms using a vector network analyzer and a laptop. The collected reflection coefficient data were processed using a radar-based imaging algorithm to reconstruct images indicating brain abnormality in ND. The results suggest that the system could serve as a low-cost and efficient tool for long-term monitoring of ND, particularly in clinics and care home scenarios.
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Affiliation(s)
- Minghui Zhao
- School of Engineering, The University of Edinburgh, Edinburgh EH9 3FF, UK; (A.R.); (I.M.S.); (Z.S.)
| | | | | | | | - Tughrul Arslan
- School of Engineering, The University of Edinburgh, Edinburgh EH9 3FF, UK; (A.R.); (I.M.S.); (Z.S.)
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Gozes I, Shapira G, Lobyntseva A, Shomron N. Unexpected gender differences in progressive supranuclear palsy reveal efficacy for davunetide in women. Transl Psychiatry 2023; 13:319. [PMID: 37845254 PMCID: PMC10579238 DOI: 10.1038/s41398-023-02618-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 09/28/2023] [Accepted: 10/03/2023] [Indexed: 10/18/2023] Open
Abstract
Progressive supranuclear palsy (PSP) is a pure tauopathy, implicating davunetide, enhancing Tau-microtubule interaction, as an ideal drug candidate. However, pooling patient data irrespective of sex concluded no efficacy. Here, analyzing sex-dependency in a 52 week-long- PSP clinical trial (involving over 200 patients) demonstrated clear baseline differences in brain ventricular volumes, a secondary endpoint. Dramatic baseline ventricular volume-dependent/volume increase correlations were observed in 52-week-placebo-treated females (r = 0.74, P = 2.36-9), whereas davunetide-treated females (like males) revealed no such effects. Assessment of primary endpoints, by the PSP Rating Scale (PSPRS) and markedly more so by the Schwab and England Activities of Daily Living (SEADL) scale, showed significantly faster deterioration in females, starting at trial week 13 (P = 0.01, and correlating with most other endpoints by week 52). Twice daily davunetide treatments slowed female disease progression and revealed significant protection according to the SEADL scale as early as at 39 weeks (P = 0.008), as well as protection of the bulbar and limb motor domains considered by the PSPRS, including speaking and swallowing difficulties caused by brain damage, and deterioration of fine motor skills, respectably (P = 0.01), at 52 weeks. Furthermore, at 52 weeks of trial, the exploratory Geriatric Depression Scale (GDS) significantly correlated with the SEADL scale deterioration in the female placebo group and demonstrated davunetide-mediated protection of females. Female-specific davunetide-mediated protection of ventricular volume corresponded to clinical efficacy. Together with the significantly slower disease progression seen in men, the results reveal sex-based drug efficacy differences, demonstrating the neuroprotective and disease-modifying impact of davunetide treatment for female PSP patients.
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Affiliation(s)
- Illana Gozes
- Elton Laboratory for Molecular Neuroendocrinology, Department of Human Molecular Genetics and Biochemistry, Faculty of Medicine, Adams Super Center for Brain Studies and Sagol School of Neuroscience, Tel Aviv University, 69978, Tel Aviv, Israel.
| | - Guy Shapira
- Department of Cell and Developmental Biology, Faculty of Medicine, Sagol School of Neuroscience, Edmond J Safra Center for Bioinformatics, Tel Aviv University, 69978, Tel Aviv, Israel
| | - Alexandra Lobyntseva
- Elton Laboratory for Molecular Neuroendocrinology, Department of Human Molecular Genetics and Biochemistry, Faculty of Medicine, Adams Super Center for Brain Studies and Sagol School of Neuroscience, Tel Aviv University, 69978, Tel Aviv, Israel
| | - Noam Shomron
- Department of Cell and Developmental Biology, Faculty of Medicine, Sagol School of Neuroscience, Edmond J Safra Center for Bioinformatics, Tel Aviv University, 69978, Tel Aviv, Israel
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Besser LM, Lovasi GS, Zambrano JJ, Camacho S, Dhanekula D, Michael YL, Garg P, Hirsch JA, Siscovick D, Hurvitz PM, Biggs ML, Galvin JE, Bartz TM, Longstreth WT. Neighborhood greenspace and neighborhood income associated with white matter grade worsening: Cardiovascular Health Study. Alzheimers Dement (Amst) 2023; 15:e12484. [PMID: 37885920 PMCID: PMC10598801 DOI: 10.1002/dad2.12484] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 08/28/2023] [Accepted: 09/11/2023] [Indexed: 10/28/2023]
Abstract
INTRODUCTION We examined whether a combined measure of neighborhood greenspace and neighborhood median income was associated with white matter hyperintensity (WMH) and ventricle size changes. METHODS The sample included 1260 cognitively normal ≥ 65-year-olds with two magnetic resonance images (MRI; ≈ 5 years apart). WMH and ventricular size were graded from 0 (least) to 9 (most) abnormal (worsening = increase of ≥1 grade from initial to follow-up MRI scans). The four-category neighborhood greenspace-income measure was based on median neighborhood greenspace and income values at initial MRI. Multivariable logistic regression tested associations between neighborhood greenspace-income and MRI measures (worsening vs. not). RESULTS White matter grade worsening was more likely for those in lower greenspace-lower income neighborhoods than higher greenspace-higher income neighborhoods (odds ratio = 1.73; 95% confidence interval = 1.19-2.51). DISCUSSION The combination of lower neighborhood income and lower greenspace may be a risk factor for worsening white matter grade on MRI. However, findings need to be replicated in more diverse cohorts. HIGHLIGHTS Population-based cohort of older adults (≥ 65 years) with greenspace and MRI dataCombined measure of neighborhood greenspace and neighborhood income at initial MRIMRI outcomes included white matter hyperintensities (WMH) and ventricular sizeLongitudinal change in MRI outcomes measured approximately 5 years apartWorsening WMH over time more likely for lower greenspace-lower income neighborhoods.
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Affiliation(s)
- Lilah M. Besser
- Comprehensive Center for Brain HealthDepartment of NeurologyUniversity of Miami Miller School of MedicineBoca RatonFloridaUSA
| | - Gina S. Lovasi
- Urban Health Collaborative and Department of Epidemiology and BiostatisticsDornslife School of Public HealthDrexel UniversityPhiladelphiaPennsylvaniaUSA
| | - Joyce Jimenez Zambrano
- Comprehensive Center for Brain HealthDepartment of NeurologyUniversity of Miami Miller School of MedicineBoca RatonFloridaUSA
| | - Simone Camacho
- Comprehensive Center for Brain HealthDepartment of NeurologyUniversity of Miami Miller School of MedicineBoca RatonFloridaUSA
| | | | - Yvonne L. Michael
- Urban Health Collaborative and Department of Epidemiology and BiostatisticsDornslife School of Public HealthDrexel UniversityPhiladelphiaPennsylvaniaUSA
| | - Parveen Garg
- Division of CardiologyKeck School of MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Jana A. Hirsch
- Urban Health Collaborative and Department of Epidemiology and BiostatisticsDornslife School of Public HealthDrexel UniversityPhiladelphiaPennsylvaniaUSA
| | - David Siscovick
- Division of ResearchEvaluation, and PolicyThe New York Academy of MedicineNew YorkNew YorkUSA
| | - Philip M. Hurvitz
- Center for Studies in Demography and Ecology and Urban Form LabUniversity of WashingtonSeattleWashingtonUSA
| | - Mary L. Biggs
- Department of BiostatisticsSchool of Public HealthUniversity of WashingtonSeattleWashingtonUSA
| | - James E. Galvin
- Comprehensive Center for Brain HealthDepartment of NeurologyMiller School of MedicineUniversity of MiamiMiamiFloridaUSA
| | - Traci M. Bartz
- Department of BiostatisticsUniversity of WashingtonSeattleWashingtonUSA
| | - W. T. Longstreth
- Departments of Neurology and EpidemiologyUniversity of WashingtonSeattleWashingtonUSA
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11
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Cao G, Zhang M, Wang Y, Zhang J, Han Y, Xu X, Huang J, Kang G. End-to-end automatic pathology localization for Alzheimer's disease diagnosis using structural MRI. Comput Biol Med 2023; 163:107110. [PMID: 37321102 DOI: 10.1016/j.compbiomed.2023.107110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 05/18/2023] [Accepted: 05/30/2023] [Indexed: 06/17/2023]
Abstract
Structural magnetic resonance imaging (sMRI) is an essential part of the clinical assessment of patients at risk of Alzheimer dementia. One key challenge in sMRI-based computer-aided dementia diagnosis is to localize local pathological regions for discriminative feature learning. Existing solutions predominantly depend on generating saliency maps for pathology localization and handle the localization task independently of the dementia diagnosis task, leading to a complex multi-stage training pipeline that is hard to optimize with weakly-supervised sMRI-level annotations. In this work, we aim to simplify the pathology localization task and construct an end-to-end automatic localization framework (AutoLoc) for Alzheimer's disease diagnosis. To this end, we first present an efficient pathology localization paradigm that directly predicts the coordinate of the most disease-related region in each sMRI slice. Then, we approximate the non-differentiable patch-cropping operation with the bilinear interpolation technique, which eliminates the barrier to gradient backpropagation and thus enables the joint optimization of localization and diagnosis tasks. Extensive experiments on commonly used ADNI and AIBL datasets demonstrate the superiority of our method. Especially, we achieve 93.38% and 81.12% accuracy on Alzheimer's disease classification and mild cognitive impairment conversion prediction tasks, respectively. Several important brain regions, such as rostral hippocampus and globus pallidus, are identified to be highly associated with Alzheimer's disease.
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Affiliation(s)
- Gongpeng Cao
- Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, No. 10 Xitucheng Road, Haidian District, Beijing, 100876, China
| | - Manli Zhang
- Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, No. 10 Xitucheng Road, Haidian District, Beijing, 100876, China
| | - Yiping Wang
- Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, No. 10 Xitucheng Road, Haidian District, Beijing, 100876, China
| | - Jing Zhang
- Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, No. 10 Xitucheng Road, Haidian District, Beijing, 100876, China
| | - Ying Han
- Department of Neurology, Xuanwu Hospital of Capital Medical University, No. 45 Changchun Street, Xicheng District, Beijing, 100053, China
| | - Xin Xu
- Department of Neurosurgery, Chinese PLA General Hospital, No. 28 Fuxing Road, Haidian District, Beijing, 100853, China
| | - Jinguo Huang
- Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, No. 10 Xitucheng Road, Haidian District, Beijing, 100876, China.
| | - Guixia Kang
- Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, No. 10 Xitucheng Road, Haidian District, Beijing, 100876, China.
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12
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Zhao Y, Wang B, Liu CF, Faria AV, Miller MI, Caffo BS, Luo X. Identifying brain hierarchical structures associated with Alzheimer's disease using a regularized regression method with tree predictors. Biometrics 2023; 79:2333-2345. [PMID: 36263865 PMCID: PMC10115907 DOI: 10.1111/biom.13775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Accepted: 10/03/2022] [Indexed: 11/30/2022]
Abstract
Brain segmentation at different levels is generally represented as hierarchical trees. Brain regional atrophy at specific levels was found to be marginally associated with Alzheimer's disease outcomes. In this study, we propose an ℓ1 -type regularization for predictors that follow a hierarchical tree structure. Considering a tree as a directed acyclic graph, we interpret the model parameters from a path analysis perspective. Under this concept, the proposed penalty regulates the total effect of each predictor on the outcome. With regularity conditions, it is shown that under the proposed regularization, the estimator of the model coefficient is consistent in ℓ2 -norm and the model selection is also consistent. When applied to a brain sMRI dataset acquired from the Alzheimer's Disease Neuroimaging Initiative (ADNI), the proposed approach identifies brain regions where atrophy in these regions demonstrates the declination in memory. With regularization on the total effects, the findings suggest that the impact of atrophy on memory deficits is localized from small brain regions, but at various levels of brain segmentation. Data used in preparation of this paper were obtained from the ADNI database.
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Affiliation(s)
- Yi Zhao
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Bingkai Wang
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Chin-Fu Liu
- Center for Imaging Science, Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Andreia V. Faria
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Michael I. Miller
- Center for Imaging Science, Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Brian S. Caffo
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Xi Luo
- Department of Biostatistics and Data Science, The University of Texas Health Science Center at Houston, Houston, Texas, USA
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13
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Bachmann D, Buchmann A, Studer S, Saake A, Rauen K, Zuber I, Gruber E, Nitsch RM, Hock C, Gietl A, Treyer V. Age-, sex-, and pathology-related variability in brain structure and cognition. Transl Psychiatry 2023; 13:278. [PMID: 37574523 PMCID: PMC10423720 DOI: 10.1038/s41398-023-02572-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 07/20/2023] [Accepted: 07/24/2023] [Indexed: 08/15/2023] Open
Abstract
This work aimed to investigate potential pathways linking age and imaging measures to early age- and pathology-related changes in cognition. We used [18F]-Flutemetamol (amyloid) and [18F]-Flortaucipir (tau) positron emission tomography (PET), structural MRI, and neuropsychological assessment from 232 elderly individuals aged 50-89 years (46.1% women, 23% APOE-ε4 carrier, 23.3% MCI). Tau-PET was available for a subsample of 93 individuals. Structural equation models were used to evaluate cross-sectional pathways between age, amyloid and tau burden, grey matter thickness and volumes, white matter hyperintensity volume, lateral ventricle volume, and cognition. Our results show that age is associated with worse outcomes in most of the measures examined and had similar negative effects on episodic memory and executive functions. While increased lateral ventricle volume was consistently associated with executive function dysfunction, participants with mild cognitive impairment drove associations between structural measures and episodic memory. Both age and amyloid-PET could be associated with medial temporal lobe tau, depending on whether we used a continuous or a dichotomous amyloid variable. Tau burden in entorhinal cortex was related to worse episodic memory in individuals with increased amyloid burden (Centiloid >12) independently of medial temporal lobe atrophy. Testing models for sex differences revealed that amyloid burden was more strongly associated with regional atrophy in women compared with men. These associations were likely mediated by higher tau burden in women. These results indicate that influences of pathological pathways on cognition and sex-specific vulnerabilities are dissociable already in early stages of neuropathology and cognitive impairment.
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Affiliation(s)
- Dario Bachmann
- Institute for Regenerative Medicine, University of Zurich, Schlieren, Switzerland.
- Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland.
| | - Andreas Buchmann
- Institute for Regenerative Medicine, University of Zurich, Schlieren, Switzerland
| | - Sandro Studer
- Institute for Regenerative Medicine, University of Zurich, Schlieren, Switzerland
| | - Antje Saake
- Institute for Regenerative Medicine, University of Zurich, Schlieren, Switzerland
| | - Katrin Rauen
- Institute for Regenerative Medicine, University of Zurich, Schlieren, Switzerland
- Department of Geriatric Psychiatry, Psychiatric Hospital Zurich, University of Zurich, Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich, Zurich, Switzerland
| | - Isabelle Zuber
- Institute for Regenerative Medicine, University of Zurich, Schlieren, Switzerland
| | - Esmeralda Gruber
- Institute for Regenerative Medicine, University of Zurich, Schlieren, Switzerland
| | - Roger M Nitsch
- Institute for Regenerative Medicine, University of Zurich, Schlieren, Switzerland
- Neurimmune AG, Schlieren, Switzerland
| | - Christoph Hock
- Institute for Regenerative Medicine, University of Zurich, Schlieren, Switzerland
- Neurimmune AG, Schlieren, Switzerland
| | - Anton Gietl
- Institute for Regenerative Medicine, University of Zurich, Schlieren, Switzerland
- Department of Geriatric Psychiatry, Psychiatric Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Valerie Treyer
- Institute for Regenerative Medicine, University of Zurich, Schlieren, Switzerland
- Department of Nuclear Medicine, University Hospital Zurich, University of Zurich, Zurich, Switzerland
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14
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Wu S, Venkataraman A, Ghosal S. GIRUS-net: A Multimodal Deep Learning Model Identifying Imaging and Genetic Biomarkers Linked to Alzheimer's Disease Severity. Annu Int Conf IEEE Eng Med Biol Soc 2023; 2023:1-4. [PMID: 38083359 PMCID: PMC11005466 DOI: 10.1109/embc40787.2023.10341000] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
We introduce an explainable deep neural architecture that combines brain structure with genetic influence to improve disease severity prediction in Alzheimer's disease. Our framework consists of an encoder, a decoder, and a rank-consistent ordinal regression module. The encoder projects neural imaging and genetics data into a low-dimensional latent space regularized by the decoder. The ordinal regression module guides the feature embedding process to find discriminative patterns representative of disease severity. We also add a learnable dropout layer that learns feature importance and extracts explainable biomarkers from the data. We evaluate our model using structural MRI (sMRI) and Single Nucleotide Polymorphism (SNP) data provided by the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. In 2-class severity classification comparison, our model has a median F-score of 0.86 (baseline median F-score range: 0.57-0.81). In 3-class classification comparison, our model's median F-score is 0.50 (baseline range: 0.17 - 0.41). In 4-class classification comparison, our model's median F-score is 0.40 (baseline range: 0.14 - 0.39). We demonstrate that our model provides improved disease diagnosis alongside sparse and clinically relevant biomarkers.Clinical relevance-This study provides a deep-learning model that can predict Alzheimer's disease severity levels while identifying consistent and clinically relevant biomarkers.
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Affiliation(s)
- Sarah Wu
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Archana Venkataraman
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA
- Department of Electrical and Computer Engineering, Boston University, Boston, MA, USA
| | - Sayan Ghosal
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA
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15
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van der Haar D, Moustafa A, Warren SL, Alashwal H, van Zyl T. An Alzheimer's disease category progression sub-grouping analysis using manifold learning on ADNI. Sci Rep 2023; 13:10483. [PMID: 37380746 DOI: 10.1038/s41598-023-37569-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Accepted: 06/23/2023] [Indexed: 06/30/2023] Open
Abstract
Many current statistical and machine learning methods have been used to explore Alzheimer's disease (AD) and its associated patterns that contribute to the disease. However, there has been limited success in understanding the relationship between cognitive tests, biomarker data, and patient AD category progressions. In this work, we perform exploratory data analysis of AD health record data by analyzing various learned lower dimensional manifolds to separate early-stage AD categories further. Specifically, we used Spectral embedding, Multidimensional scaling, Isomap, t-Distributed Stochastic Neighbour Embedding, Uniform Manifold Approximation and Projection, and sparse denoising autoencoder based manifolds on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. We then determine the clustering potential of the learned embeddings and then determine if category sub-groupings or sub-categories can be found. We then used a Kruskal-sWallis H test to determine the statistical significance of the discovered AD subcategories. Our results show that the existing AD categories do exhibit sub-groupings, especially in mild cognitive impairment transitions in many of the tested manifolds, showing there may be a need for further subcategories to describe AD progression.
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Affiliation(s)
- Dustin van der Haar
- Academy of Computer Science and Software Engineering, University of Johannesburg, Gauteng, South Africa.
| | - Ahmed Moustafa
- Department of Human Anatomy and Physiology, University of Johannesburg, Gauteng, South Africa
- School of Psychology, Faculty of Society and Design, Bond University, Gold Coast, QLD, Australia
| | - Samuel L Warren
- School of Psychology, Faculty of Society and Design, Bond University, Gold Coast, QLD, Australia
| | - Hany Alashwal
- College of Information Technology, United Arab Emirates University, Al-Ain, United Arab Emirates
| | - Terence van Zyl
- Institute for Intelligent Systems, University of Johannesburg, Gauteng, South Africa
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16
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Kress GT, Popa ES, Thompson PM, Bookheimer SY, Thomopoulos SI, Ching CRK, Zheng H, Hirsh DA, Merrill DA, Panos SE, Raji CA, Siddarth P, Bramen JE. Preliminary validation of a structural magnetic resonance imaging metric for tracking dementia-related neurodegeneration and future decline. Neuroimage Clin 2023; 39:103458. [PMID: 37421927 PMCID: PMC10338152 DOI: 10.1016/j.nicl.2023.103458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Accepted: 06/20/2023] [Indexed: 07/10/2023]
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative disease characterized by cognitive decline and atrophy in the medial temporal lobe (MTL) and subsequent brain regions. Structural magnetic resonance imaging (sMRI) has been widely used in research and clinical care for diagnosis and monitoring AD progression. However, atrophy patterns are complex and vary by patient. To address this issue, researchers have made efforts to develop more concise metrics that can summarize AD-specific atrophy. Many of these methods can be difficult to interpret clinically, hampering adoption. In this study, we introduce a novel index which we call an "AD-NeuroScore," that uses a modified Euclidean-inspired distance function to calculate differences between regional brain volumes associated with cognitive decline. The index is adjusted for intracranial volume (ICV), age, sex, and scanner model. We validated AD-NeuroScore using 929 older adults from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, with a mean age of 72.7 years (SD = 6.3; 55.1-91.5) and cognitively normal (CN), mild cognitive impairment (MCI), or AD diagnoses. Our validation results showed that AD-NeuroScore was significantly associated with diagnosis and disease severity scores (measured by MMSE, CDR-SB, and ADAS-11) at baseline. Furthermore, baseline AD-NeuroScore was associated with both changes in diagnosis and disease severity scores at all time points with available data. The performance of AD-NeuroScore was equivalent or superior to adjusted hippocampal volume (AHV), a widely used metric in AD research. Further, AD-NeuroScore typically performed as well as or sometimes better when compared to other existing sMRI-based metrics. In conclusion, we have introduced a new metric, AD-NeuroScore, which shows promising results in detecting AD, benchmarking disease severity, and predicting disease progression. AD-NeuroScore differentiates itself from other metrics by being clinically practical and interpretable.
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Affiliation(s)
- Gavin T Kress
- Pacific Brain Health Center, Pacific Neuroscience Institute Foundation, Santa Monica, CA 90404, USA; Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Emily S Popa
- Pacific Brain Health Center, Pacific Neuroscience Institute Foundation, Santa Monica, CA 90404, USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA 90292, USA
| | - Susan Y Bookheimer
- Pacific Brain Health Center, Pacific Neuroscience Institute Foundation, Santa Monica, CA 90404, USA; David Geffen School of Medicine, University of California, Los Angeles, Westwood, CA 90095, USA
| | - Sophia I Thomopoulos
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA 90292, USA
| | - Christopher R K Ching
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA 90292, USA
| | - Hong Zheng
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA 90292, USA
| | - Daniel A Hirsh
- Pacific Brain Health Center, Pacific Neuroscience Institute Foundation, Santa Monica, CA 90404, USA.
| | - David A Merrill
- Pacific Brain Health Center, Pacific Neuroscience Institute Foundation, Santa Monica, CA 90404, USA; Department of Translational Neurosciences and Neurotherapeutics, Providence Saint John's Cancer Institute, Santa Monica, CA 90404, USA; UCLA Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, Westwood, CA 90095, USA
| | - Stella E Panos
- Pacific Brain Health Center, Pacific Neuroscience Institute Foundation, Santa Monica, CA 90404, USA
| | - Cyrus A Raji
- Mallinckrodt Institute of Radiology, Washington University, St. Louis, MO, USA
| | - Prabha Siddarth
- Pacific Brain Health Center, Pacific Neuroscience Institute Foundation, Santa Monica, CA 90404, USA; UCLA Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, Westwood, CA 90095, USA
| | - Jennifer E Bramen
- Pacific Brain Health Center, Pacific Neuroscience Institute Foundation, Santa Monica, CA 90404, USA.
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17
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Zhou L, Li Y, Sweeney EM, Wang XH, Kuceyeski A, Chiang GC, Ivanidze J, Wang Y, Gauthier SA, de Leon MJ, Nguyen TD. Association of brain tissue cerebrospinal fluid fraction with age in healthy cognitively normal adults. Front Aging Neurosci 2023; 15:1162001. [PMID: 37396667 PMCID: PMC10312090 DOI: 10.3389/fnagi.2023.1162001] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 05/31/2023] [Indexed: 07/04/2023] Open
Abstract
Background and purpose Our objective was to apply multi-compartment T2 relaxometry in cognitively normal individuals aged 20-80 years to study the effect of aging on the parenchymal CSF fraction (CSFF), a potential measure of the subvoxel CSF space. Materials and methods A total of 60 volunteers (age range, 22-80 years) were enrolled. Voxel-wise maps of short-T2 myelin water fraction (MWF), intermediate-T2 intra/extra-cellular water fraction (IEWF), and long-T2 CSFF were obtained using fast acquisition with spiral trajectory and adiabatic T2prep (FAST-T2) sequence and three-pool non-linear least squares fitting. Multiple linear regression analyses were performed to study the association between age and regional MWF, IEWF, and CSFF measurements, adjusting for sex and region of interest (ROI) volume. ROIs include the cerebral white matter (WM), cerebral cortex, and subcortical deep gray matter (GM). In each model, a quadratic term for age was tested using an ANOVA test. A Spearman's correlation between the normalized lateral ventricle volume, a measure of organ-level CSF space, and the regional CSFF, a measure of tissue-level CSF space, was computed. Results Regression analyses showed that there was a statistically significant quadratic relationship with age for CSFF in the cortex (p = 0.018), MWF in the cerebral WM (p = 0.033), deep GM (p = 0.017) and cortex (p = 0.029); and IEWF in the deep GM (p = 0.033). There was a statistically highly significant positive linear relationship between age and regional CSFF in the cerebral WM (p < 0.001) and deep GM (p < 0.001). In addition, there was a statistically significant negative linear association between IEWF and age in the cerebral WM (p = 0.017) and cortex (p < 0.001). In the univariate correlation analysis, the normalized lateral ventricle volume correlated with the regional CSFF measurement in the cerebral WM (ρ = 0.64, p < 0.001), cortex (ρ = 0.62, p < 0.001), and deep GM (ρ = 0.66, p < 0.001). Conclusion Our cross-sectional data demonstrate that brain tissue water in different compartments shows complex age-dependent patterns. Parenchymal CSFF, a measure of subvoxel CSF-like water in the brain tissue, is quadratically associated with age in the cerebral cortex and linearly associated with age in the cerebral deep GM and WM.
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Affiliation(s)
- Liangdong Zhou
- Department of Radiology, Weill Cornell Medicine, New York, NY, United States
| | - Yi Li
- Department of Radiology, Weill Cornell Medicine, New York, NY, United States
| | - Elizabeth M. Sweeney
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, PA, United States
| | - Xiuyuan H. Wang
- Department of Radiology, Weill Cornell Medicine, New York, NY, United States
| | - Amy Kuceyeski
- Department of Radiology, Weill Cornell Medicine, New York, NY, United States
- Department of Statistics and Data Science, Cornell University, Ithaca, NY, United States
| | - Gloria C. Chiang
- Department of Radiology, Weill Cornell Medicine, New York, NY, United States
| | - Jana Ivanidze
- Department of Radiology, Weill Cornell Medicine, New York, NY, United States
| | - Yi Wang
- Department of Radiology, Weill Cornell Medicine, New York, NY, United States
- Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY, United States
| | - Susan A. Gauthier
- Department of Radiology, Weill Cornell Medicine, New York, NY, United States
| | - Mony J. de Leon
- Department of Radiology, Weill Cornell Medicine, New York, NY, United States
| | - Thanh D. Nguyen
- Department of Radiology, Weill Cornell Medicine, New York, NY, United States
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18
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Liu C, Downey RJ, Mu Y, Richer N, Hwang J, Shah VA, Sato SD, Clark DJ, Hass CJ, Manini TM, Seidler RD, Ferris DP. Comparison of EEG Source Localization Using Simplified and Anatomically Accurate Head Models in Younger and Older Adults. IEEE Trans Neural Syst Rehabil Eng 2023; 31:2591-2602. [PMID: 37252873 PMCID: PMC10336858 DOI: 10.1109/tnsre.2023.3281356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
Accuracy of electroencephalography (EEG) source localization relies on the volume conduction head model. A previous analysis of young adults has shown that simplified head models have larger source localization errors when compared with head models based on magnetic resonance images (MRIs). As obtaining individual MRIs may not always be feasible, researchers often use generic head models based on template MRIs. It is unclear how much error would be introduced using template MRI head models in older adults that likely have differences in brain structure compared to young adults. The primary goal of this study was to determine the error caused by using simplified head models without individual-specific MRIs in both younger and older adults. We collected high-density EEG during uneven terrain walking and motor imagery for 15 younger (22±3 years) and 21 older adults (74±5 years) and obtained [Formula: see text]-weighted MRI for each individual. We performed equivalent dipole fitting after independent component analysis to obtain brain source locations using four forward modeling pipelines with increasing complexity. These pipelines included: 1) a generic head model with template electrode positions or 2) digitized electrode positions, 3) individual-specific head models with digitized electrode positions using simplified tissue segmentation, or 4) anatomically accurate segmentation. We found that when compared to the anatomically accurate individual-specific head models, performing dipole fitting with generic head models led to similar source localization discrepancies (up to 2 cm) for younger and older adults. Co-registering digitized electrode locations to the generic head models reduced source localization discrepancies by ∼ 6 mm. Additionally, we found that source depths generally increased with skull conductivity for the representative young adult but not as much for the older adult. Our results can help inform a more accurate interpretation of brain areas in EEG studies when individual MRIs are unavailable.
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Reekes TH, Ledbetter CR, Alexander JS, Stokes KY, Pardue S, Bhuiyan MAN, Patterson JC, Lofton KT, Kevil CG, Disbrow EA. Elevated plasma sulfides are associated with cognitive dysfunction and brain atrophy in human Alzheimer's disease and related dementias. Redox Biol 2023; 62:102633. [PMID: 36924684 PMCID: PMC10026043 DOI: 10.1016/j.redox.2023.102633] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 02/10/2023] [Indexed: 02/19/2023] Open
Abstract
Emerging evidence indicates that vascular stress is an important contributor to the pathophysiology of Alzheimer's disease and related dementias (ADRD). Hydrogen sulfide (H2S) and its metabolites (acid-labile (e.g., iron-sulfur clusters) and bound (e.g., per-, poly-) sulfides) have been shown to modulate both vascular and neuronal homeostasis. We recently reported that elevated plasma sulfides were associated with cognitive dysfunction and measures of microvascular disease in ADRD. Here we extend our previous work to show associations between elevated sulfides and magnetic resonance-based metrics of brain atrophy and white matter integrity. Elevated bound sulfides were associated with decreased grey matter volume, while increased acid labile sulfides were associated with decreased white matter integrity and greater ventricular volume. These findings are consistent with alterations in sulfide metabolism in ADRD which may represent maladaptive responses to oxidative stress.
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Affiliation(s)
- Tyler H Reekes
- Department of Pharmacology, Toxicology & Neuroscience, LSU Health Shreveport, United States; Center for Brain Health, LSU Health Shreveport, United States
| | - Christina R Ledbetter
- Center for Brain Health, LSU Health Shreveport, United States; Department of Neurosurgery, LSU Health Shreveport, United States
| | - J Steven Alexander
- Center for Brain Health, LSU Health Shreveport, United States; Center for Cardiovascular Diseases and Sciences, LSU Health Shreveport, United States; Department of Neurology, LSU Health Shreveport, United States; Department of Molecular and Cellular Physiology, LSU Health Shreveport, United States
| | - Karen Y Stokes
- Center for Brain Health, LSU Health Shreveport, United States; Center for Cardiovascular Diseases and Sciences, LSU Health Shreveport, United States; Department of Molecular and Cellular Physiology, LSU Health Shreveport, United States
| | - Sibile Pardue
- Center for Cardiovascular Diseases and Sciences, LSU Health Shreveport, United States; Department of Pathology and Translational Pathobiology, LSU Health Shreveport, United States
| | | | - James C Patterson
- Center for Brain Health, LSU Health Shreveport, United States; Department of Psychiatry and Behavioral Medicine, LSU Health Shreveport, United States
| | - Katelyn T Lofton
- Center for Brain Health, LSU Health Shreveport, United States; Department of Neurology, LSU Health Shreveport, United States; Department of Psychiatry and Behavioral Medicine, LSU Health Shreveport, United States
| | - Christopher G Kevil
- Center for Brain Health, LSU Health Shreveport, United States; Center for Cardiovascular Diseases and Sciences, LSU Health Shreveport, United States; Department of Pathology and Translational Pathobiology, LSU Health Shreveport, United States.
| | - Elizabeth A Disbrow
- Department of Pharmacology, Toxicology & Neuroscience, LSU Health Shreveport, United States; Center for Brain Health, LSU Health Shreveport, United States; Center for Cardiovascular Diseases and Sciences, LSU Health Shreveport, United States; Department of Neurology, LSU Health Shreveport, United States.
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20
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Mummery CJ, Börjesson-Hanson A, Blackburn DJ, Vijverberg EGB, De Deyn PP, Ducharme S, Jonsson M, Schneider A, Rinne JO, Ludolph AC, Bodenschatz R, Kordasiewicz H, Swayze EE, Fitzsimmons B, Mignon L, Moore KM, Yun C, Baumann T, Li D, Norris DA, Crean R, Graham DL, Huang E, Ratti E, Bennett CF, Junge C, Lane RM. Tau-targeting antisense oligonucleotide MAPT Rx in mild Alzheimer's disease: a phase 1b, randomized, placebo-controlled trial. Nat Med 2023; 29:1437-1447. [PMID: 37095250 PMCID: PMC10287562 DOI: 10.1038/s41591-023-02326-3] [Citation(s) in RCA: 48] [Impact Index Per Article: 48.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Accepted: 03/29/2023] [Indexed: 04/26/2023]
Abstract
Tau plays a key role in Alzheimer's disease (AD) pathophysiology, and accumulating evidence suggests that lowering tau may reduce this pathology. We sought to inhibit MAPT expression with a tau-targeting antisense oligonucleotide (MAPTRx) and reduce tau levels in patients with mild AD. A randomized, double-blind, placebo-controlled, multiple-ascending dose phase 1b trial evaluated the safety, pharmacokinetics and target engagement of MAPTRx. Four ascending dose cohorts were enrolled sequentially and randomized 3:1 to intrathecal bolus administrations of MAPTRx or placebo every 4 or 12 weeks during the 13-week treatment period, followed by a 23 week post-treatment period. The primary endpoint was safety. The secondary endpoint was MAPTRx pharmacokinetics in cerebrospinal fluid (CSF). The prespecified key exploratory outcome was CSF total-tau protein concentration. Forty-six patients enrolled in the trial, of whom 34 were randomized to MAPTRx and 12 to placebo. Adverse events were reported in 94% of MAPTRx-treated patients and 75% of placebo-treated patients; all were mild or moderate. No serious adverse events were reported in MAPTRx-treated patients. Dose-dependent reduction in the CSF total-tau concentration was observed with greater than 50% mean reduction from baseline at 24 weeks post-last dose in the 60 mg (four doses) and 115 mg (two doses) MAPTRx groups. Clinicaltrials.gov registration number: NCT03186989 .
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Affiliation(s)
- Catherine J Mummery
- Dementia Research Centre, National Hospital for Neurology and Neurosurgery, University College London, London, UK.
| | | | - Daniel J Blackburn
- Sheffield Teaching Hospital NHS Foundation Trust, NIHR Sheffield Clinical Research Facility and NIHR Sheffield Biomedical Research Centre, Royal Hallamshire Hospital, Sheffield, UK
| | - Everard G B Vijverberg
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Peter Paul De Deyn
- University Medical Center Groningen / RUG, Alzheimer Center Groningen, Groningen, the Netherlands
| | - Simon Ducharme
- Douglas Mental Health University Institute and McConnell Brain Imaging Centre of the Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Michael Jonsson
- Memory Clinic, Psychiatry - Cognition and Geriatric Psychiatry, Sahlgrenska University Hospital, Gothenburg/Molndal, Sweden
| | - Anja Schneider
- German Center for Neurodegenerative Diseases, DZNE, and Department of Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital Bonn, Bonn, Germany
| | - Juha O Rinne
- CRST Oy; Turku PET Centre University of Turku and Turku University Hospital, Turku, Finland
| | - Albert C Ludolph
- Department of Neurology University of Ulm and DZNE, Ulm, Germany
| | - Ralf Bodenschatz
- Pharmakologisches Studienzentrum Chemnitz GmbH Mittweida, Mittweida, Germany
| | | | | | | | | | | | - Chris Yun
- Ionis Pharmaceuticals, Carlsbad, CA, USA
| | | | - Dan Li
- Ionis Pharmaceuticals, Carlsbad, CA, USA
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21
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Ratan Y, Rajput A, Maleysm S, Pareek A, Jain V, Pareek A, Kaur R, Singh G. An Insight into Cellular and Molecular Mechanisms Underlying the Pathogenesis of Neurodegeneration in Alzheimer's Disease. Biomedicines 2023; 11:biomedicines11051398. [PMID: 37239068 DOI: 10.3390/biomedicines11051398] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 05/04/2023] [Accepted: 05/06/2023] [Indexed: 05/28/2023] Open
Abstract
Alzheimer's disease (AD) is the most prominent neurodegenerative disorder in the aging population. It is characterized by cognitive decline, gradual neurodegeneration, and the development of amyloid-β (Aβ)-plaques and neurofibrillary tangles, which constitute hyperphosphorylated tau. The early stages of neurodegeneration in AD include the loss of neurons, followed by synaptic impairment. Since the discovery of AD, substantial factual research has surfaced that outlines the disease's causes, molecular mechanisms, and prospective therapeutics, but a successful cure for the disease has not yet been discovered. This may be attributed to the complicated pathogenesis of AD, the absence of a well-defined molecular mechanism, and the constrained diagnostic resources and treatment options. To address the aforementioned challenges, extensive disease modeling is essential to fully comprehend the underlying mechanisms of AD, making it easier to design and develop effective treatment strategies. Emerging evidence over the past few decades supports the critical role of Aβ and tau in AD pathogenesis and the participation of glial cells in different molecular and cellular pathways. This review extensively discusses the current understanding concerning Aβ- and tau-associated molecular mechanisms and glial dysfunction in AD. Moreover, the critical risk factors associated with AD including genetics, aging, environmental variables, lifestyle habits, medical conditions, viral/bacterial infections, and psychiatric factors have been summarized. The present study will entice researchers to more thoroughly comprehend and explore the current status of the molecular mechanism of AD, which may assist in AD drug development in the forthcoming era.
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Affiliation(s)
- Yashumati Ratan
- Department of Pharmacy, Banasthali Vidyapith, Banasthali 304022, Rajasthan, India
| | - Aishwarya Rajput
- Department of Pharmacy, Banasthali Vidyapith, Banasthali 304022, Rajasthan, India
| | - Sushmita Maleysm
- Department of Bioscience & Biotechnology, Banasthali Vidyapith, Banasthali 304022, Rajasthan, India
| | - Aaushi Pareek
- Department of Pharmacy, Banasthali Vidyapith, Banasthali 304022, Rajasthan, India
| | - Vivek Jain
- Department of Pharmaceutical Sciences, Mohan Lal Sukhadia University, Udaipur 313001, Rajasthan, India
| | - Ashutosh Pareek
- Department of Pharmacy, Banasthali Vidyapith, Banasthali 304022, Rajasthan, India
| | - Ranjeet Kaur
- Adesh Institute of Dental Sciences and Research, Bathinda 151101, Punjab, India
| | - Gurjit Singh
- Department of Biomedical Engineering, University of Illinois Chicago, Chicago, IL 60607, USA
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22
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Park HJ, Lee JY, Yang JJ, Kim HJ, Kim YS, Kim JY, Choi YY. Prediction of Amyloid β-Positivity with both MRI Parameters and Cognitive Function Using Machine Learning. J Korean Soc Radiol 2023; 84:638-652. [PMID: 37325007 PMCID: PMC10265247 DOI: 10.3348/jksr.2022.0084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 09/05/2022] [Accepted: 10/02/2022] [Indexed: 06/17/2023]
Abstract
Purpose To investigate the MRI markers for the prediction of amyloid β (Aβ)-positivity in mild cognitive impairment (MCI) and Alzheimer's disease (AD), and to evaluate the differences in MRI markers between Aβ-positive (Aβ [+]) and -negative groups using the machine learning (ML) method. Materials and Methods This study included 139 patients with MCI and AD who underwent amyloid PET-CT and brain MRI. Patients were divided into Aβ (+) (n = 84) and Aβ-negative (n = 55) groups. Visual analysis was performed with the Fazekas scale of white matter hyperintensity (WMH) and cerebral microbleeds (CMB) scores. The WMH volume and regional brain volume were quantitatively measured. The multivariable logistic regression and ML using support vector machine, and logistic regression were used to identify the best MRI predictors of Aβ-positivity. Results The Fazekas scale of WMH (p = 0.02) and CMB scores (p = 0.04) were higher in Aβ (+). The volumes of hippocampus, entorhinal cortex, and precuneus were smaller in Aβ (+) (p < 0.05). The third ventricle volume was larger in Aβ (+) (p = 0.002). The logistic regression of ML showed a good accuracy (81.1%) with mini-mental state examination (MMSE) and regional brain volumes. Conclusion The application of ML using the MMSE, third ventricle, and hippocampal volume is helpful in predicting Aβ-positivity with a good accuracy.
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23
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Klingenberg M, Stark D, Eitel F, Budding C, Habes M, Ritter K. Higher performance for women than men in MRI-based Alzheimer's disease detection. Alzheimers Res Ther 2023; 15:84. [PMID: 37081528 PMCID: PMC10116672 DOI: 10.1186/s13195-023-01225-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 04/03/2023] [Indexed: 04/22/2023]
Abstract
INTRODUCTION Although machine learning classifiers have been frequently used to detect Alzheimer's disease (AD) based on structural brain MRI data, potential bias with respect to sex and age has not yet been addressed. Here, we examine a state-of-the-art AD classifier for potential sex and age bias even in the case of balanced training data. METHODS Based on an age- and sex-balanced cohort of 432 subjects (306 healthy controls, 126 subjects with AD) extracted from the ADNI data base, we trained a convolutional neural network to detect AD in MRI brain scans and performed ten different random training-validation-test splits to increase robustness of the results. Classifier decisions for single subjects were explained using layer-wise relevance propagation. RESULTS The classifier performed significantly better for women (balanced accuracy [Formula: see text]) than for men ([Formula: see text]). No significant differences were found in clinical AD scores, ruling out a disparity in disease severity as a cause for the performance difference. Analysis of the explanations revealed a larger variance in regional brain areas for male subjects compared to female subjects. DISCUSSION The identified sex differences cannot be attributed to an imbalanced training dataset and therefore point to the importance of examining and reporting classifier performance across population subgroups to increase transparency and algorithmic fairness. Collecting more data especially among underrepresented subgroups and balancing the dataset are important but do not always guarantee a fair outcome.
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Affiliation(s)
- Malte Klingenberg
- Charité - Universitätsmedizin Berlin (corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health), Department of Psychiatry and Neurosciences, Berlin, Germany
- Bernstein Center for Computational Neuroscience, Berlin, Germany
| | - Didem Stark
- Charité - Universitätsmedizin Berlin (corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health), Department of Psychiatry and Neurosciences, Berlin, Germany
- Bernstein Center for Computational Neuroscience, Berlin, Germany
| | - Fabian Eitel
- Charité - Universitätsmedizin Berlin (corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health), Department of Psychiatry and Neurosciences, Berlin, Germany
- Bernstein Center for Computational Neuroscience, Berlin, Germany
| | - Céline Budding
- Eindhoven University of Technology, Eindhoven, Netherlands
| | - Mohamad Habes
- Neuroimage Analytics Laboratory and Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Kerstin Ritter
- Charité - Universitätsmedizin Berlin (corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health), Department of Psychiatry and Neurosciences, Berlin, Germany.
- Bernstein Center for Computational Neuroscience, Berlin, Germany.
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24
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Mirkin S, Albensi BC. Should artificial intelligence be used in conjunction with Neuroimaging in the diagnosis of Alzheimer's disease? Front Aging Neurosci 2023; 15:1094233. [PMID: 37187577 PMCID: PMC10177660 DOI: 10.3389/fnagi.2023.1094233] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Accepted: 03/27/2023] [Indexed: 05/17/2023] Open
Abstract
Alzheimer's disease (AD) is a progressive, neurodegenerative disorder that affects memory, thinking, behavior, and other cognitive functions. Although there is no cure, detecting AD early is important for the development of a therapeutic plan and a care plan that may preserve cognitive function and prevent irreversible damage. Neuroimaging, such as magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET), has served as a critical tool in establishing diagnostic indicators of AD during the preclinical stage. However, as neuroimaging technology quickly advances, there is a challenge in analyzing and interpreting vast amounts of brain imaging data. Given these limitations, there is great interest in using artificial Intelligence (AI) to assist in this process. AI introduces limitless possibilities in the future diagnosis of AD, yet there is still resistance from the healthcare community to incorporate AI in the clinical setting. The goal of this review is to answer the question of whether AI should be used in conjunction with neuroimaging in the diagnosis of AD. To answer the question, the possible benefits and disadvantages of AI are discussed. The main advantages of AI are its potential to improve diagnostic accuracy, improve the efficiency in analyzing radiographic data, reduce physician burnout, and advance precision medicine. The disadvantages include generalization and data shortage, lack of in vivo gold standard, skepticism in the medical community, potential for physician bias, and concerns over patient information, privacy, and safety. Although the challenges present fundamental concerns and must be addressed when the time comes, it would be unethical not to use AI if it can improve patient health and outcome.
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Affiliation(s)
- Sophia Mirkin
- Dr. Kiran C. Patel College of Osteopathic Medicine, Nova Southeastern University, Fort Lauderdale, FL, United States
| | - Benedict C. Albensi
- Barry and Judy Silverman College of Pharmacy, Nova Southeastern University, Fort Lauderdale, FL, United States
- St. Boniface Hospital Research, Winnipeg, MB, Canada
- University of Manitoba, Winnipeg, MB, Canada
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25
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Wang X, Ye T, Zhou W, Zhang J. Uncovering heterogeneous cognitive trajectories in mild cognitive impairment: a data-driven approach. Alzheimers Res Ther 2023; 15:57. [PMID: 36941651 PMCID: PMC10026406 DOI: 10.1186/s13195-023-01205-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Accepted: 03/12/2023] [Indexed: 03/23/2023]
Abstract
BACKGROUND Given the complex and progressive nature of mild cognitive impairment (MCI), the ability to delineate and understand the heterogeneous cognitive trajectories is crucial for developing personalized medicine and informing trial design. The primary goals of this study were to examine whether different cognitive trajectories can be identified within subjects with MCI and, if present, to characterize each trajectory in relation to changes in all major Alzheimer's disease (AD) biomarkers over time. METHODS Individuals with a diagnosis of MCI at the first visit and ≥ 1 follow-up cognitive assessment were selected from the Alzheimer's Disease Neuroimaging Initiative database (n = 936; age 73 ± 8; 40% female; 16 ± 3 years of education; 50% APOE4 carriers). Based on the Alzheimer's Disease Assessment Scale-Cognitive Subscale-13 (ADAS-Cog-13) total scores from baseline up to 5 years follow-up, a non-parametric k-means longitudinal clustering method was performed to obtain clusters of individuals with similar patterns of cognitive decline. We further conducted a series of linear mixed-effects models to study the associations of cluster membership with longitudinal changes in other cognitive measures, neurodegeneration, and in vivo AD pathologies. RESULTS Four distinct cognitive trajectories emerged. Cluster 1 consisted of 255 individuals (27%) with a nearly non-existent rate of change in the ADAS-Cog-13 over 5 years of follow-up and a healthy-looking biomarker profile. Individuals in the cluster 2 (n = 336, 35%) and 3 (n = 240, 26%) groups showed relatively mild and moderate cognitive decline trajectories, respectively. Cluster 4, comprising about 11% of our study sample (n = 105), exhibited an aggressive cognitive decline trajectory and was characterized by a pronouncedly abnormal biomarker profile. CONCLUSIONS Individuals with MCI show substantial heterogeneity in cognitive decline. Our findings may potentially contribute to improved trial design and patient stratification.
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Affiliation(s)
- Xiwu Wang
- Department of Psychiatry, Wenzhou Seventh People's Hospital, Wenzhou, China
| | - Teng Ye
- Department of Ultrasound, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Wenjun Zhou
- Research and Development, Hangzhou Shansier Medical Technologies Co., Ltd., Hangzhou, China.
| | - Jie Zhang
- Department of Data Science, Hangzhou Shansier Medical Technologies Co., Ltd., Hangzhou, China.
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26
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Thambisetty M, Howard R. Lecanemab trial in AD brings hope but requires greater clarity. Nat Rev Neurol 2023; 19:132-3. [PMID: 36609712 DOI: 10.1038/s41582-022-00768-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
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27
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Humphrey CM, Hooker JW, Thapa M, Wilcox MJ, Ostrowski D, Ostrowski TD. Synaptic loss and gliosis in the nucleus tractus solitarii with streptozotocin-induced Alzheimer's disease. Brain Res 2023; 1801:148202. [PMID: 36521513 PMCID: PMC9840699 DOI: 10.1016/j.brainres.2022.148202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 11/21/2022] [Accepted: 12/09/2022] [Indexed: 12/14/2022]
Abstract
Obstructive sleep apnea is highly prevalent in Alzheimer's disease (AD). However, brainstem centers controlling respiration have received little attention in AD research, and mechanisms behind respiratory dysfunction in AD are not understood. The nucleus tractus solitarii (nTS) is an important brainstem center for respiratory control and chemoreflex function. Alterations of nTS integrity, like those shown in AD patients, likely affect neuronal processing and adequate control of breathing. We used the streptozotocin-induced rat model of AD (STZ-AD) to analyze cellular changes in the nTS that corroborate previously documented respiratory dysfunction. We used 2 common dosages of STZ (2 and 3 mg/kg STZ) for model induction and evaluated the early impact on cell populations in the nTS. The hippocampus served as control region to identify site-specific effects of STZ. There was significant atrophy in the caudal nTS of the 3 mg/kg STZ-AD group only, an area known to integrate chemoafferent information. Also, the hippocampus had significant atrophy with the highest STZ dosage tested. Both STZ-AD groups showed respiratory dysfunction along with multiple indices for astroglial and microglial activation. These changes were primarily located in the caudal and intermediate nTS. While there was no change of astrocytes in the hippocampus, microglial activation was accompanied by a reduction in synaptic density. Together, our data demonstrate that STZ-AD induces site-specific effects on all major cell types, primarily in the caudal/intermediate nTS. Both STZ dosages used in this study produced a similar outcome and can be used for future studies examining the initial symptoms of STZ-AD.
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Affiliation(s)
- Chuma M Humphrey
- Department of Physiology, Kirksville College of Osteopathic Medicine, A.T. Still University, 800 W. Jefferson St., Kirksville, MO, USA
| | - John W Hooker
- Department of Physiology, Kirksville College of Osteopathic Medicine, A.T. Still University, 800 W. Jefferson St., Kirksville, MO, USA
| | - Mahima Thapa
- Department of Biology, Truman State University, 100 E. Normal Ave., Kirksville, MO, USA
| | - Mason J Wilcox
- Department of Biology, Truman State University, 100 E. Normal Ave., Kirksville, MO, USA
| | - Daniela Ostrowski
- Department of Biology, Truman State University, 100 E. Normal Ave., Kirksville, MO, USA
| | - Tim D Ostrowski
- Department of Physiology, Kirksville College of Osteopathic Medicine, A.T. Still University, 800 W. Jefferson St., Kirksville, MO, USA.
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28
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Cano A, Esteban-de-Antonio E, Bernuz M, Puerta R, García-González P, de Rojas I, Olivé C, Pérez-Cordón A, Montrreal L, Núñez-Llaves R, Sotolongo-Grau Ó, Alarcón-Martín E, Valero S, Alegret M, Martín E, Martino-Adami PV, Ettcheto M, Camins A, Vivas A, Gomez-Chiari M, Tejero MÁ, Orellana A, Tárraga L, Marquié M, Ramírez A, Martí M, Pividori MI, Boada M, Ruíz A. Plasma extracellular vesicles reveal early molecular differences in amyloid positive patients with early-onset mild cognitive impairment. J Nanobiotechnology 2023; 21:54. [PMID: 36788617 PMCID: PMC9930227 DOI: 10.1186/s12951-023-01793-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Accepted: 01/24/2023] [Indexed: 02/16/2023] Open
Abstract
In the clinical course of Alzheimer's disease (AD) development, the dementia phase is commonly preceded by a prodromal AD phase, which is mainly characterized by reaching the highest levels of Aβ and p-tau-mediated neuronal injury and a mild cognitive impairment (MCI) clinical status. Because of that, most AD cases are diagnosed when neuronal damage is already established and irreversible. Therefore, a differential diagnosis of MCI causes in these prodromal stages is one of the greatest challenges for clinicians. Blood biomarkers are emerging as desirable tools for pre-screening purposes, but the current results are still being analyzed and much more data is needed to be implemented in clinical practice. Because of that, plasma extracellular vesicles (pEVs) are gaining popularity as a new source of biomarkers for the early stages of AD development. To identify an exosome proteomics signature linked to prodromal AD, we performed a cross-sectional study in a cohort of early-onset MCI (EOMCI) patients in which 184 biomarkers were measured in pEVs, cerebrospinal fluid (CSF), and plasma samples using multiplex PEA technology of Olink© proteomics. The obtained results showed that proteins measured in pEVs from EOMCI patients with established amyloidosis correlated with CSF p-tau181 levels, brain ventricle volume changes, brain hyperintensities, and MMSE scores. In addition, the correlations of pEVs proteins with different parameters distinguished between EOMCI Aβ( +) and Aβ(-) patients, whereas the CSF or plasma proteome did not. In conclusion, our findings suggest that pEVs may be able to provide information regarding the initial amyloidotic changes of AD. Circulating exosomes may acquire a pathological protein signature of AD before raw plasma, becoming potential biomarkers for identifying subjects at the earliest stages of AD development.
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Affiliation(s)
- Amanda Cano
- Ace Alzheimer Center Barcelona - International University of Catalunya (UIC), C/Marquès de Sentmenat, 57, 08029, Barcelona, Spain. .,Biomedical Research Networking Centre in Neurodegenerative Diseases (CIBERNED), Madrid, Spain.
| | - Ester Esteban-de-Antonio
- grid.410675.10000 0001 2325 3084Ace Alzheimer Center Barcelona – International University of Catalunya (UIC), C/Marquès de Sentmenat, 57, 08029 Barcelona, Spain
| | - Mireia Bernuz
- grid.7080.f0000 0001 2296 0625Grup de Sensors I Biosensors, Departament de Química, Universitat Autònoma de Barcelona, 08193 Bellaterra, Spain
| | - Raquel Puerta
- grid.410675.10000 0001 2325 3084Ace Alzheimer Center Barcelona – International University of Catalunya (UIC), C/Marquès de Sentmenat, 57, 08029 Barcelona, Spain
| | - Pablo García-González
- grid.410675.10000 0001 2325 3084Ace Alzheimer Center Barcelona – International University of Catalunya (UIC), C/Marquès de Sentmenat, 57, 08029 Barcelona, Spain ,grid.418264.d0000 0004 1762 4012Biomedical Research Networking Centre in Neurodegenerative Diseases (CIBERNED), Madrid, Spain
| | - Itziar de Rojas
- grid.410675.10000 0001 2325 3084Ace Alzheimer Center Barcelona – International University of Catalunya (UIC), C/Marquès de Sentmenat, 57, 08029 Barcelona, Spain ,grid.418264.d0000 0004 1762 4012Biomedical Research Networking Centre in Neurodegenerative Diseases (CIBERNED), Madrid, Spain
| | - Claudia Olivé
- grid.410675.10000 0001 2325 3084Ace Alzheimer Center Barcelona – International University of Catalunya (UIC), C/Marquès de Sentmenat, 57, 08029 Barcelona, Spain
| | - Alba Pérez-Cordón
- grid.410675.10000 0001 2325 3084Ace Alzheimer Center Barcelona – International University of Catalunya (UIC), C/Marquès de Sentmenat, 57, 08029 Barcelona, Spain
| | - Laura Montrreal
- grid.410675.10000 0001 2325 3084Ace Alzheimer Center Barcelona – International University of Catalunya (UIC), C/Marquès de Sentmenat, 57, 08029 Barcelona, Spain
| | - Raúl Núñez-Llaves
- grid.410675.10000 0001 2325 3084Ace Alzheimer Center Barcelona – International University of Catalunya (UIC), C/Marquès de Sentmenat, 57, 08029 Barcelona, Spain
| | - Óscar Sotolongo-Grau
- grid.410675.10000 0001 2325 3084Ace Alzheimer Center Barcelona – International University of Catalunya (UIC), C/Marquès de Sentmenat, 57, 08029 Barcelona, Spain
| | - Emilio Alarcón-Martín
- grid.410675.10000 0001 2325 3084Ace Alzheimer Center Barcelona – International University of Catalunya (UIC), C/Marquès de Sentmenat, 57, 08029 Barcelona, Spain
| | - Sergi Valero
- grid.410675.10000 0001 2325 3084Ace Alzheimer Center Barcelona – International University of Catalunya (UIC), C/Marquès de Sentmenat, 57, 08029 Barcelona, Spain ,grid.418264.d0000 0004 1762 4012Biomedical Research Networking Centre in Neurodegenerative Diseases (CIBERNED), Madrid, Spain
| | - Montserrat Alegret
- grid.410675.10000 0001 2325 3084Ace Alzheimer Center Barcelona – International University of Catalunya (UIC), C/Marquès de Sentmenat, 57, 08029 Barcelona, Spain ,grid.418264.d0000 0004 1762 4012Biomedical Research Networking Centre in Neurodegenerative Diseases (CIBERNED), Madrid, Spain
| | - Elvira Martín
- grid.410675.10000 0001 2325 3084Ace Alzheimer Center Barcelona – International University of Catalunya (UIC), C/Marquès de Sentmenat, 57, 08029 Barcelona, Spain
| | - Pamela V. Martino-Adami
- grid.6190.e0000 0000 8580 3777Division of Neurogenetics and Molecular Psychiatry, Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital Cologne, University of Cologne, 50937 Cologne, Germany
| | - Miren Ettcheto
- grid.418264.d0000 0004 1762 4012Biomedical Research Networking Centre in Neurodegenerative Diseases (CIBERNED), Madrid, Spain ,grid.5841.80000 0004 1937 0247Department of Pharmacology, Toxicology and Therapeutic Chemistry, Faculty of Pharmacy and Food Sciences, University of Barcelona, 08028 Barcelona, Spain ,grid.5841.80000 0004 1937 0247Institute of Neurosciences, University of Barcelona, Barcelona, Spain
| | - Antonio Camins
- grid.418264.d0000 0004 1762 4012Biomedical Research Networking Centre in Neurodegenerative Diseases (CIBERNED), Madrid, Spain ,grid.5841.80000 0004 1937 0247Department of Pharmacology, Toxicology and Therapeutic Chemistry, Faculty of Pharmacy and Food Sciences, University of Barcelona, 08028 Barcelona, Spain ,grid.5841.80000 0004 1937 0247Institute of Neurosciences, University of Barcelona, Barcelona, Spain
| | - Assumpta Vivas
- Departament de Diagnòstic Per La Imatge, Clínica Corachan, Barcelona, Spain
| | - Marta Gomez-Chiari
- Departament de Diagnòstic Per La Imatge, Clínica Corachan, Barcelona, Spain
| | | | - Adelina Orellana
- grid.410675.10000 0001 2325 3084Ace Alzheimer Center Barcelona – International University of Catalunya (UIC), C/Marquès de Sentmenat, 57, 08029 Barcelona, Spain ,grid.418264.d0000 0004 1762 4012Biomedical Research Networking Centre in Neurodegenerative Diseases (CIBERNED), Madrid, Spain
| | - Lluís Tárraga
- grid.410675.10000 0001 2325 3084Ace Alzheimer Center Barcelona – International University of Catalunya (UIC), C/Marquès de Sentmenat, 57, 08029 Barcelona, Spain ,grid.418264.d0000 0004 1762 4012Biomedical Research Networking Centre in Neurodegenerative Diseases (CIBERNED), Madrid, Spain
| | - Marta Marquié
- grid.410675.10000 0001 2325 3084Ace Alzheimer Center Barcelona – International University of Catalunya (UIC), C/Marquès de Sentmenat, 57, 08029 Barcelona, Spain ,grid.418264.d0000 0004 1762 4012Biomedical Research Networking Centre in Neurodegenerative Diseases (CIBERNED), Madrid, Spain
| | - Alfredo Ramírez
- grid.6190.e0000 0000 8580 3777Division of Neurogenetics and Molecular Psychiatry, Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital Cologne, University of Cologne, 50937 Cologne, Germany ,grid.15090.3d0000 0000 8786 803XDepartment of Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital Bonn, Medical Faculty, 53127 Bonn, Germany ,grid.424247.30000 0004 0438 0426German Center for Neurodegenerative Diseases (DZNE), 53127 Bonn, Germany ,Department of Psychiatry and Glenn, Biggs Institute for Alzheimer’s and Neurodegenerative Diseases, San Antonio, TX 78229 USA ,grid.6190.e0000 0000 8580 3777Cluster of Excellence Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, 50931 Cologne, Germany
| | - Mercè Martí
- grid.7080.f0000 0001 2296 0625Grup de Sensors I Biosensors, Departament de Química, Universitat Autònoma de Barcelona, 08193 Bellaterra, Spain
| | - María Isabel Pividori
- grid.7080.f0000 0001 2296 0625Grup de Sensors I Biosensors, Departament de Química, Universitat Autònoma de Barcelona, 08193 Bellaterra, Spain ,grid.7080.f0000 0001 2296 0625Biosensing and Bioanalysis Group, Institut de Biotecnologia I de Biomedicina (IBB-UAB), Mòdul B Parc de Recerca UAB, Campus Universitat Autònoma de Barcelona, 08193 Bellaterra, Spain
| | - Mercè Boada
- grid.410675.10000 0001 2325 3084Ace Alzheimer Center Barcelona – International University of Catalunya (UIC), C/Marquès de Sentmenat, 57, 08029 Barcelona, Spain ,grid.418264.d0000 0004 1762 4012Biomedical Research Networking Centre in Neurodegenerative Diseases (CIBERNED), Madrid, Spain
| | - Agustín Ruíz
- Ace Alzheimer Center Barcelona - International University of Catalunya (UIC), C/Marquès de Sentmenat, 57, 08029, Barcelona, Spain. .,Biomedical Research Networking Centre in Neurodegenerative Diseases (CIBERNED), Madrid, Spain.
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Wen C, Chen A, Wang X, Pan W. Variable selection in additive models via hierarchical sparse penalty. CAN J STAT 2023. [DOI: 10.1002/cjs.11752] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Affiliation(s)
- Canhong Wen
- Department of Statistics and Finance School of Management University of Science and Technology of China Hefei 230026 China
| | - Anan Chen
- Department of Statistics and Finance School of Management University of Science and Technology of China Hefei 230026 China
| | - Xueqin Wang
- Department of Statistics and Finance School of Management University of Science and Technology of China Hefei 230026 China
| | - Wenliang Pan
- Key Laboratory of Systems and Control Academy of Mathematics and Systems Science, Chinese Academy of Sciences Beijing 100190 China
- Faculty of Innovation Engineering Macau University of Science and Technology Macao China
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Gao Y, Lawless RD, Li M, Zhao Y, Schilling KG, Xu L, Shafer AT, Beason-Held LL, Resnick SM, Rogers BP, Ding Z, Anderson AW, Landman BA, Gore JC. Automatic Preprocessing Pipeline for White Matter Functional Analyses of Large-Scale Databases. Proc SPIE Int Soc Opt Eng 2023; 12464:124640U. [PMID: 37600506 PMCID: PMC10437151 DOI: 10.1117/12.2653132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/22/2023]
Abstract
Recently, increasing evidence suggests that fMRI signals in white matter (WM), conventionally ignored as nuisance, are robustly detectable using appropriate processing methods and are related to neural activity, while changes in WM with aging and degeneration are also well documented. These findings suggest variations in patterns of BOLD signals in WM should be investigated. However, existing fMRI analysis tools, which were designed for processing gray matter signals, are not well suited for large-scale processing of WM signals in fMRI data. We developed an automatic pipeline for high-performance preprocessing of fMRI images with emphasis on quantifying changes in BOLD signals in WM in an aging population. At the image processing level, the pipeline integrated existing software modules with fine parameter tunings and modifications to better extract weaker WM signals. The preprocessing results primarily included whole-brain time-courses, functional connectivity, maps and tissue masks in a common space. At the job execution level, this pipeline exploited a local XNAT to store datasets and results, while using DAX tool to automatic distribute batch jobs that run on high-performance computing clusters. Through the pipeline, 5,034 fMRI/T1 scans were preprocessed. The intraclass correlation coefficient (ICC) of test-retest experiment based on the preprocessed data is 0.52 - 0.86 (N=1000), indicating a high reliability of our pipeline, comparable to previously reported ICC in gray matter experiments. This preprocessing pipeline highly facilitates our future analyses on WM functional alterations in aging and may be of benefit to a larger community interested in WM fMRI studies.
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Affiliation(s)
- Yurui Gao
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Richard D Lawless
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Muwei Li
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Yu Zhao
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Kurt G Schilling
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Lyuan Xu
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Andrea T Shafer
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Lori L Beason-Held
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Baxter P Rogers
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Zhaohua Ding
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Adam W Anderson
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Bennett A Landman
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
- Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
- Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - John C Gore
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
- Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
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Ribarič S. Detecting Early Cognitive Decline in Alzheimer's Disease with Brain Synaptic Structural and Functional Evaluation. Biomedicines 2023; 11:biomedicines11020355. [PMID: 36830892 PMCID: PMC9952956 DOI: 10.3390/biomedicines11020355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 01/22/2023] [Accepted: 01/24/2023] [Indexed: 01/28/2023] Open
Abstract
Early cognitive decline in patients with Alzheimer's (AD) is associated with quantifiable structural and functional connectivity changes in the brain. AD dysregulation of Aβ and tau metabolism progressively disrupt normal synaptic function, leading to loss of synapses, decreased hippocampal synaptic density and early hippocampal atrophy. Advances in brain imaging techniques in living patients have enabled the transition from clinical signs and symptoms-based AD diagnosis to biomarkers-based diagnosis, with functional brain imaging techniques, quantitative EEG, and body fluids sampling. The hippocampus has a central role in semantic and episodic memory processing. This cognitive function is critically dependent on normal intrahippocampal connections and normal hippocampal functional connectivity with many cortical regions, including the perirhinal and the entorhinal cortex, parahippocampal cortex, association regions in the temporal and parietal lobes, and prefrontal cortex. Therefore, decreased hippocampal synaptic density is reflected in the altered functional connectivity of intrinsic brain networks (aka large-scale networks), including the parietal memory, default mode, and salience networks. This narrative review discusses recent critical issues related to detecting AD-associated early cognitive decline with brain synaptic structural and functional markers in high-risk or neuropsychologically diagnosed patients with subjective cognitive impairment or mild cognitive impairment.
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Affiliation(s)
- Samo Ribarič
- Faculty of Medicine, Institute of Pathophysiology, University of Ljubljana, Zaloška 4, SI-1000 Ljubljana, Slovenia
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32
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Burgelman M, Dujardin P, Vandendriessche C, Vandenbroucke RE. Free complement and complement containing extracellular vesicles as potential biomarkers for neuroinflammatory and neurodegenerative disorders. Front Immunol 2023; 13:1055050. [PMID: 36741417 PMCID: PMC9896008 DOI: 10.3389/fimmu.2022.1055050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 12/07/2022] [Indexed: 01/21/2023] Open
Abstract
The complement system is implicated in a broad range of neuroinflammatory disorders such as Alzheimer's disease (AD) and multiple sclerosis (MS). Consequently, measuring complement levels in biofluids could serve as a potential biomarker for these diseases. Indeed, complement levels are shown to be altered in patients compared to controls, and some studies reported a correlation between the level of free complement in biofluids and disease progression, severity or the response to therapeutics. Overall, they are not (yet) suitable as a diagnostic tool due to heterogeneity of reported results. Moreover, measurement of free complement proteins has the disadvantage that information on their origin is lost, which might be of value in a multi-parameter approach for disease prediction and stratification. In light of this, extracellular vesicles (EVs) could provide a platform to improve the diagnostic power of complement proteins. EVs are nanosized double membrane particles that are secreted by essentially every cell type and resemble the (status of the) cell of origin. Interestingly, EVs can contain complement proteins, while the cellular origin can still be determined by the presence of EV surface markers. In this review, we summarize the current knowledge and future opportunities on the use of free and EV-associated complement proteins as biomarkers for neuroinflammatory and neurodegenerative disorders.
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Affiliation(s)
- Marlies Burgelman
- VIB Center for Inflammation Research, VIB, Ghent, Belgium,Department of Biomedical Molecular Biology, Ghent University, Ghent, Belgium
| | - Pieter Dujardin
- VIB Center for Inflammation Research, VIB, Ghent, Belgium,Department of Biomedical Molecular Biology, Ghent University, Ghent, Belgium
| | - Charysse Vandendriessche
- VIB Center for Inflammation Research, VIB, Ghent, Belgium,Department of Biomedical Molecular Biology, Ghent University, Ghent, Belgium
| | - Roosmarijn E. Vandenbroucke
- VIB Center for Inflammation Research, VIB, Ghent, Belgium,Department of Biomedical Molecular Biology, Ghent University, Ghent, Belgium,*Correspondence: Roosmarijn E. Vandenbroucke,
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33
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Garg N, Choudhry MS, Bodade RM. A review on Alzheimer's disease classification from normal controls and mild cognitive impairment using structural MR images. J Neurosci Methods 2023; 384:109745. [PMID: 36395961 DOI: 10.1016/j.jneumeth.2022.109745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 10/04/2022] [Accepted: 11/11/2022] [Indexed: 11/16/2022]
Abstract
Alzheimer's disease (AD) is an irreversible neurodegenerative brain disorder that degrades the memory and cognitive ability in elderly people. The main reason for memory loss and reduction in cognitive ability is the structural changes in the brain that occur due to neuronal loss. These structural changes are most conspicuous in the hippocampus, cortex, and grey matter and can be assessed by using neuroimaging techniques viz. Positron Emission Tomography (PET), structural Magnetic Resonance Imaging (MRI) and functional MRI (fMRI), etc. Out of these neuroimaging techniques, structural MRI has evolved as the best technique as it indicates the best soft tissue contrast and high spatial resolution which is important for AD detection. Currently, the focus of researchers is on predicting the conversion of Mild Cognitive Impairment (MCI) into AD. MCI represents the transition state between expected cognitive changes with normal aging and Alzheimer's disease. Not every MCI patient progresses into Alzheimer's disease. MCI can develop into stable MCI (sMCI, patients are called non-converters) or into progressive MCI (pMCI, patients are diagnosed as MCI converters). This paper discusses the prognosis of MCI to AD conversion and presents a review of structural MRI-based studies for AD detection. AD detection framework includes feature extraction, feature selection, and classification process. This paper reviews the studies for AD detection based on different feature extraction methods and machine learning algorithms for classification. The performance of various feature extraction methods has been compared and it has been observed that the wavelet transform-based feature extraction method would give promising results for AD classification. The present study indicates that researchers are successful in classifying AD from Normal Controls (NrmC) but, it still requires a lot of work to be done for MCI/ NrmC and MCI/AD, which would help in detecting AD at its early stage.
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Affiliation(s)
- Neha Garg
- Delhi Technological University, Department of Electronics and Communication, Delhi 110042, India.
| | - Mahipal Singh Choudhry
- Delhi Technological University, Department of Electronics and Communication, Delhi 110042, India.
| | - Rajesh M Bodade
- Military College of Telecommunication Engineering (MCTE), Mhow, Indore 453441, Madhya Pradesh, India.
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Farina MP, Saenz J, Crimmins EM. Does adding MRI and CSF-based biomarkers improve cognitive status classification based on cognitive performance questionnaires? PLoS One 2023; 18:e0285220. [PMID: 37155663 PMCID: PMC10166486 DOI: 10.1371/journal.pone.0285220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 04/17/2023] [Indexed: 05/10/2023] Open
Abstract
BACKGROUND Cognitive status classification (e.g. dementia, cognitive impairment without dementia, and normal) based on cognitive performance questionnaires has been widely used in population-based studies, providing insight into the population dynamics of dementia. However, researchers have raised concerns about the accuracy of cognitive assessments. MRI and CSF biomarkers may provide improved classification, but the potential improvement in classification in population-based studies is relatively unknown. METHODS Data come from the Alzheimer's Disease Neuroimaging Initiative (ADNI). We examined whether the addition of MRI and CSF biomarkers improved cognitive status classification based on cognitive status questionnaires (MMSE). We estimated several multinomial logistic regression models with different combinations of MMSE and CSF/MRI biomarkers. Based on these models, we also predicted prevalence of each cognitive status category using a model with MMSE only and a model with MMSE + MRI + CSF measures and compared them to diagnosed prevalence. RESULTS Our analysis showed a slight improvement in variance explained (pseudo-R2) between the model with MMSE only and the model including MMSE and MRI/CSF biomarkers; the pseudo-R2 increased from .401 to .445. Additionally, in evaluating differences in predicted prevalence for each cognitive status, we found a small improvement in the predicted prevalence of cognitively normal individuals between the MMSE only model and the model with MMSE and CSF/MRI biomarkers (3.1% improvement). We found no improvement in the correct prediction of dementia prevalence. CONCLUSION MRI and CSF biomarkers, while important for understanding dementia pathology in clinical research, were not found to substantially improve cognitive status classification based on cognitive status performance, which may limit adoption in population-based surveys due to costs, training, and invasiveness associated with their collection.
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Affiliation(s)
- Mateo P Farina
- School of Gerontology, University of Southern California, Los Angeles, California, United States of America
- Human Development and Family Sciences, University of Texas at Austin, Austin, Texas, United States of America
| | - Joseph Saenz
- Edson College of Nursing and Health Innovation, Arizona State University, Phoenix, Arizona, United States of America
| | - Eileen M Crimmins
- School of Gerontology, University of Southern California, Los Angeles, California, United States of America
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Hosokawa T, Kinoshita K, Ihara S, Nakagawa K, Iguchi U, Hirabayashi M, Mutoh T, Sawada N, Kuwana T, Yamaguchi J. Relationship between brain volume reduction during the acute phase of sepsis and activities of daily living in elderly patients: A prospective cohort study. PLoS One 2023; 18:e0284886. [PMID: 37192211 DOI: 10.1371/journal.pone.0284886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2022] [Accepted: 04/11/2023] [Indexed: 05/18/2023] Open
Abstract
Brain damage in acute sepsis may be associated with poor long-term outcomes that impair reintegration into society. We aimed to clarify whether brain volume reduction occurs during the acute phase of sepsis in patients with acute brain damage. In this prospective, noninterventional observational study, brain volume reduction was evaluated by comparing head computed tomography findings at admission with those obtained during hospitalization. We examined the association between brain volume reduction and performance of the activities of daily living in 85 consecutive patients (mean age, 77 ± 12.7 years) with sepsis or septic shock. The bicaudate ratio increased in 38/58 (65.5%) patients, Evans index increased in 35/58 (60.3%) patients, and brain volume by volumetry decreased in 46/58 (79.3%) patients from the first to the second measurement, with significant increases in the bicaudate ratio (P < 0.0001) and Evans index (P = 0.0005) and a significant decrease in the brain volume by volumetry (P < 0.0001). The change rate for brain volume by volumetry was significantly correlated with the Katz index (ρ = -0.3790, P = 0.0094). In the acute phase of sepsis in this sample of older patients, 60-79% of patients showed decreased brain volumes. This was associated with a decreased capacity for performing activities of daily living.
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Affiliation(s)
- Toru Hosokawa
- Department of Acute Medicine, Division of Emergency and Critical Care Medicine, Nihon University School of Medicine, Tokyo, Japan
| | - Kosaku Kinoshita
- Department of Acute Medicine, Division of Emergency and Critical Care Medicine, Nihon University School of Medicine, Tokyo, Japan
| | - Shingo Ihara
- Department of Acute Medicine, Division of Emergency and Critical Care Medicine, Nihon University School of Medicine, Tokyo, Japan
| | - Katsuhiro Nakagawa
- Department of Acute Medicine, Division of Emergency and Critical Care Medicine, Nihon University School of Medicine, Tokyo, Japan
| | - Umefumi Iguchi
- Department of Acute Medicine, Division of Emergency and Critical Care Medicine, Nihon University School of Medicine, Tokyo, Japan
| | - Marina Hirabayashi
- Department of Acute Medicine, Division of Emergency and Critical Care Medicine, Nihon University School of Medicine, Tokyo, Japan
| | - Tomokazu Mutoh
- Department of Acute Medicine, Division of Emergency and Critical Care Medicine, Nihon University School of Medicine, Tokyo, Japan
| | - Nami Sawada
- Department of Acute Medicine, Division of Emergency and Critical Care Medicine, Nihon University School of Medicine, Tokyo, Japan
| | - Tsukasa Kuwana
- Department of Acute Medicine, Division of Emergency and Critical Care Medicine, Nihon University School of Medicine, Tokyo, Japan
| | - Junko Yamaguchi
- Department of Acute Medicine, Division of Emergency and Critical Care Medicine, Nihon University School of Medicine, Tokyo, Japan
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Agger MP, Danielsen ER, Carstensen MS, Nguyen NM, Horning M, Henney MA, Jensen CBR, Baandrup AO, Kjær TW, Madsen KH, Miskowiak K, Petersen PM, Høgh P. Safety, Feasibility, and Potential Clinical Efficacy of 40 Hz Invisible Spectral Flicker versus Placebo in Patients with Mild-to-Moderate Alzheimer's Disease: A Randomized, Placebo-Controlled, Double-Blinded, Pilot Study. J Alzheimers Dis 2023; 92:653-665. [PMID: 36776073 DOI: 10.3233/jad-221238] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/12/2023]
Abstract
BACKGROUND Recent studies suggested induction of 40 Hz neural activity as a potential treatment for Alzheimer's disease (AD). However, prolonged exposure to flickering light raises adherence and safety concerns, encouraging investigation of tolerable light stimulation protocols. OBJECTIVE To investigate the safety, feasibility, and exploratory measures of efficacy. METHODS This two-stage randomized placebo-controlled double-blinded clinical trial, recruited first cognitive healthy participants (n = 3/2 active/placebo), and subsequently patients with mild-to-moderate AD (n = 5/6, active/placebo). Participants were randomized 1:1 to receive either active intervention with 40 Hz Invisible Spectral Flicker (ISF) or placebo intervention with color and intensity matched non-flickering white light. RESULTS Few and mild adverse events were observed. Adherence was above 86.1% of intended treatment days, with participants remaining in front of the device for >51.3 min (60 max) and directed gaze >34.9 min. Secondary outcomes of cognition indicate a tendency towards improvement in the active group compared to placebo (mean: -2.6/1.5, SD: 6.58/6.53, active/placebo) at week 6. Changes in hippocampal and ventricular volume also showed no tendency of improvement in the active group at week 6 compared to placebo. At week 12, a potential delayed effect of the intervention was seen on the volume of the hippocampus in the active group compared to placebo (mean: 0.34/-2.03, SD: 3.26/1.18, active/placebo), and the ventricular volume active group (mean: -0.36/2.50, SD: 1.89/2.05, active/placebo), compared to placebo. CONCLUSION Treatment with 40 Hz ISF offers no significant safety or adherence concerns. Potential impact on secondary outcomes must be tested in larger scale clinical trials.
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Affiliation(s)
- Mikkel Pejstrup Agger
- Department of Neurology, Zealand University Hospital, Denmark
- Department of Clinical Medicine, University of Copenhagen, Denmark
| | | | | | | | - Maibritt Horning
- Department of Neurology, Zealand University Hospital, Denmark
- Department of Clinical Medicine, University of Copenhagen, Denmark
| | - Mark Alexander Henney
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Denmark
| | | | | | - Troels Wesenberg Kjær
- Department of Neurology, Zealand University Hospital, Denmark
- Department of Clinical Medicine, University of Copenhagen, Denmark
| | - Kristoffer Hougaard Madsen
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Denmark
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital - Amager and Hvidovre, Copenhagen, Denmark
| | - Kamilla Miskowiak
- Neurocognition and Emotion in Affective Disorders (NEAD) Group, Copenhagen Affective Disorder Research Centre (CADIC), Psychiatric Centre Copenhagen, Copenhagen University Hospital, Rigshospitalet, Denmark
| | | | - Peter Høgh
- Department of Neurology, Zealand University Hospital, Denmark
- Department of Clinical Medicine, University of Copenhagen, Denmark
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Naz N, Naqvi SF, Hohn N, Whelan K, Littler P, Roncaroli F, Robinson AC, Miyan JA. Cerebral Folate Metabolism in Post-Mortem Alzheimer's Disease Tissues: A Small Cohort Study. Int J Mol Sci 2022; 24. [PMID: 36614107 DOI: 10.3390/ijms24010660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Accepted: 12/27/2022] [Indexed: 01/03/2023] Open
Abstract
We investigated the cerebral folate system in post-mortem brains and matched cerebrospinal fluid (CSF) samples from subjects with definite Alzheimer's disease (AD) (n = 21) and neuropathologically normal brains (n = 21) using immunohistochemistry, Western blot and dot blot. In AD the CSF showed a significant decrease in 10-formyl tetrahydrofolate dehydrogenase (FDH), a critical folate binding protein and enzyme in the CSF, as well as in the main folate transporter, folate receptor alpha (FRα) and folate. In tissue, we found a switch in the pathway of folate supply to the cerebral cortex in AD compared to neurologically normal brains. FRα switched from entry through FDH-positive astrocytes in normal, to entry through glial fibrillary acidic protein (GFAP)-positive astrocytes in the AD cortex. Moreover, this switch correlated with an apparent change in metabolic direction to hypermethylation of neurons in AD. Our data suggest that the reduction in FDH in CSF prohibits FRα-folate entry via FDH-positive astrocytes and promotes entry through the GFAP pathway directly to neurons for hypermethylation. This data may explain some of the cognitive decline not attributable to the loss of neurons alone and presents a target for potential treatment.
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Palavicini JP, Ding L, Pan M, Qiu S, Wang H, Shen Q, Dupree JL, Han X. Sulfatide Deficiency, an Early Alzheimer's Lipidomic Signature, Causes Brain Ventricular Enlargement in the Absence of Classical Neuropathological Hallmarks. Int J Mol Sci 2022; 24:233. [PMID: 36613677 PMCID: PMC9820719 DOI: 10.3390/ijms24010233] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 12/13/2022] [Accepted: 12/20/2022] [Indexed: 12/25/2022] Open
Abstract
Alzheimer's disease (AD) is a neurodegenerative disease characterized by progressive memory loss and a decline in activities of daily life. Ventricular enlargement has been associated with worse performance on global cognitive tests and AD. Our previous studies demonstrated that brain sulfatides, myelin-enriched lipids, are dramatically reduced in subjects at the earliest clinically recognizable AD stages via an apolipoprotein E (APOE)-dependent and isoform-specific process. Herein, we provided pre-clinical evidence that sulfatide deficiency is causally associated with brain ventricular enlargement. Specifically, taking advantage of genetic mouse models of global and adult-onset sulfatide deficiency, we demonstrated that sulfatide losses cause ventricular enlargement without significantly affecting hippocampal or whole brain volumes using histological and magnetic resonance imaging approaches. Mild decreases in sulfatide content and mild increases in ventricular areas were also observed in human APOE4 compared to APOE2 knock-in mice. Finally, we provided Western blot and immunofluorescence evidence that aquaporin-4, the most prevalent aquaporin channel in the central nervous system (CNS) that provides fast water transportation and regulates cerebrospinal fluid in the ventricles, is significantly increased under sulfatide-deficient conditions, while other major brain aquaporins (e.g., aquaporin-1) are not altered. In short, we unraveled a novel and causal association between sulfatide deficiency and ventricular enlargement. Finally, we propose putative mechanisms by which sulfatide deficiency may induce ventricular enlargement.
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Affiliation(s)
- Juan Pablo Palavicini
- Sam and Ann Barshop Institute for Longevity and Aging Studies, University of Texas Health Science Center at San Antonio, San Antonio, TX 78229, USA
- Division of Diabetes, Department of Medicine, University of Texas Health Science Center at San Antonio, San Antonio, TX 78229, USA
| | - Lin Ding
- Sam and Ann Barshop Institute for Longevity and Aging Studies, University of Texas Health Science Center at San Antonio, San Antonio, TX 78229, USA
- Department of Biochemistry and Molecular Biology, Soochow University Medical College, Suzhou 215123, China
| | - Meixia Pan
- Sam and Ann Barshop Institute for Longevity and Aging Studies, University of Texas Health Science Center at San Antonio, San Antonio, TX 78229, USA
| | - Shulan Qiu
- Sam and Ann Barshop Institute for Longevity and Aging Studies, University of Texas Health Science Center at San Antonio, San Antonio, TX 78229, USA
| | - Hu Wang
- Sam and Ann Barshop Institute for Longevity and Aging Studies, University of Texas Health Science Center at San Antonio, San Antonio, TX 78229, USA
| | - Qiang Shen
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, San Antonio, TX 78229, USA
- Department of Radiology, University of Texas Health Science Center at San Antonio, San Antonio, TX 78229, USA
| | - Jeffrey L. Dupree
- Department of Anatomy and Neurobiology, Virginia Commonwealth University, Richmond, VA 23284, USA
- Research Service, McGuire Veterans Affairs Medical Center, Richmond, VA 23249, USA
| | - Xianlin Han
- Sam and Ann Barshop Institute for Longevity and Aging Studies, University of Texas Health Science Center at San Antonio, San Antonio, TX 78229, USA
- Division of Diabetes, Department of Medicine, University of Texas Health Science Center at San Antonio, San Antonio, TX 78229, USA
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Zubrikhina M, Abramova O, Yarkin V, Ushakov V, Ochneva A, Bernstein A, Burnaev E, Andreyuk D, Savilov V, Kurmishev M, Syunyakov T, Karpenko O, Andryushchenko A, Kostyuk G, Sharaev M. Machine learning approaches to Mild Cognitive Impairment detection based on structural MRI data and morphometric features. COGN SYST RES 2022. [DOI: 10.1016/j.cogsys.2022.12.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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Greene AN, Solomon MB, Privette Vinnedge LM. Novel molecular mechanisms in Alzheimer’s disease: The potential role of DEK in disease pathogenesis. Front Aging Neurosci 2022; 14:1018180. [PMID: 36275000 PMCID: PMC9582447 DOI: 10.3389/fnagi.2022.1018180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Accepted: 09/20/2022] [Indexed: 11/13/2022] Open
Abstract
Alzheimer’s disease and age-related dementias (AD/ADRD) are debilitating diseases that exact a significant physical, emotional, cognitive, and financial toll on the individual and their social network. While genetic risk factors for early-onset AD have been identified, the molecular and genetic drivers of late-onset AD, the most common subtype, remain a mystery. Current treatment options are limited for the 35 million people in the United States with AD/ADRD. Thus, it is critically important to identify novel molecular mechanisms of dementia-related pathology that may be targets for the development of new interventions. Here, we summarize the overarching concepts regarding AD/ADRD pathogenesis. Then, we highlight one potential molecular driver of AD/ADRD, the chromatin remodeling protein DEK. We discuss in vitro, in vivo, and ex vivo findings, from our group and others, that link DEK loss with the cellular, molecular, and behavioral signatures of AD/ADRD. These include associations between DEK loss and cellular and molecular hallmarks of AD/ADRD, including apoptosis, Tau expression, and Tau hyperphosphorylation. We also briefly discuss work that suggests sex-specific differences in the role of DEK in AD/ADRD pathogenesis. Finally, we discuss future directions for exploiting the DEK protein as a novel player and potential therapeutic target for the treatment of AD/ADRD.
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Affiliation(s)
- Allie N. Greene
- Neuroscience Graduate Program, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Matia B. Solomon
- Neuroscience Graduate Program, University of Cincinnati College of Medicine, Cincinnati, OH, United States
- Department of Psychology, University of Cincinnati, Cincinnati, OH, United States
| | - Lisa M. Privette Vinnedge
- Division of Oncology, Cancer and Blood Diseases Institute, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, United States
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States
- *Correspondence: Lisa M. Privette Vinnedge,
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Han J, Kim MN, Lee HW, Jeong SY, Lee SW, Yoon U, Kang K. Distinct volumetric features of cerebrospinal fluid distribution in idiopathic normal-pressure hydrocephalus and Alzheimer's disease. Fluids Barriers CNS 2022; 19:66. [PMID: 36045420 PMCID: PMC9434899 DOI: 10.1186/s12987-022-00362-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 07/13/2022] [Indexed: 12/04/2022] Open
Abstract
Objective The aims of the study were to measure the cerebrospinal fluid (CSF) volumes in the lateral ventricle, high-convexity subarachnoid space, and Sylvian fissure region in patients with idiopathic normal-pressure hydrocephalus (INPH) and Alzheimer’s disease (AD), and to evaluate differences in these volumes between INPH and AD groups and healthy controls. Methods Forty-nine INPH patients, 59 AD patients, and 26 healthy controls were imaged with automated three-dimensional volumetric MRI. Results INPH patients had larger lateral ventricles and CSF spaces of the Sylvian fissure region and smaller high-convexity subarachnoid spaces than other groups, and AD patients had larger lateral ventricles and CSF spaces of the Sylvian fissure region than the control group. The INPH group showed a negative correlation between lateral ventricle and high-convexity subarachnoid space volumes, while the AD group showed a positive correlation between lateral ventricle volume and volume for CSF spaces of the Sylvian fissure region. The ratio of lateral ventricle to high-convexity subarachnoid space volumes yielded an area under the curve of 0.990, differentiating INPH from AD. Conclusions Associations between CSF volumes suggest that there might be different mechanisms between INPH and AD to explain their respective lateral ventricular dilations. The ratio of lateral ventricle to high-convexity subarachnoid space volumes distinguishes INPH from AD with good diagnostic sensitivity and specificity. We propose to refer to this ratio as the VOSS (ventricle over subarachnoid space) index.
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Affiliation(s)
- Jaehwan Han
- Department of Biomedical Engineering, School of Medicine, Kyungpook National University, Daegu, South Korea
| | - Myoung Nam Kim
- Department of Biomedical Engineering, School of Medicine, Kyungpook National University, Daegu, South Korea
| | - Ho-Won Lee
- Department of Neurology, School of Medicine, Kyungpook National University, 680 Gukchaebosang-ro, Jung-gu, Daegu, 41944, South Korea.,Brain Science and Engineering Institute, Kyungpook National University, Daegu, South Korea
| | - Shin Young Jeong
- Department of Nuclear Medicine, School of Medicine, Kyungpook National University, Daegu, South Korea
| | - Sang-Woo Lee
- Department of Nuclear Medicine, School of Medicine, Kyungpook National University, Daegu, South Korea
| | - Uicheul Yoon
- Department of Biomedical Engineering, Daegu Catholic University, 13-13 Hayang- ro, Hayang-eup, Gyeongsan, Gyeongbuk, 38430, South Korea.
| | - Kyunghun Kang
- Department of Neurology, School of Medicine, Kyungpook National University, 680 Gukchaebosang-ro, Jung-gu, Daegu, 41944, South Korea.
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Veluppal A, sadhukhan D, gopinath V, swaminathan R. Differentiation of Alzheimer conditions in brain MR images using bidimensional multiscale entropy-based texture analysis of lateral ventricles. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Kaddu-Mulindwa D, Heit M, Wagenpfeil G, Bewarder M, Fassbender K, Behnke S, Yilmaz U, Fousse M. Fewer neurocognitive deficits and less brain atrophy by third ventricle measurement in PLWH treated with modern ART: A prospective analysis. Front Neurol 2022; 13:962535. [PMID: 36081869 PMCID: PMC9447481 DOI: 10.3389/fneur.2022.962535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 07/26/2022] [Indexed: 11/29/2022] Open
Abstract
Background Despite antiretroviral therapy, cognitive dysfunction seems to remain a major issue for people living with human immunodeficiency virus (PLWH). Previous studies showed a correlation between the width of the third ventricle (WTV) and neurocognitive disorders in PLWH. Patients and methods We investigated prevalence and correlation of neuropsychological disorders using WTV as a brain atrophy marker examined by transcranial sonography and MRI in PLWH and healthy age- and gender-matched controls. We used Becks Depression Inventory (BDI) for depression screening, the questionnaires Fatigue Severity Scale (FSS) for fatigue and Short-Form-36 (SF36) for quality of life (QoL) evaluation and Consortium to establish a registry for Alzheimer's disease (CERAD-PLUS) as neuropsychological test battery. Results 52 PLWH (47 males) and 28 non-infected controls (23 males) with a median age of 52 years (24–78 years) and 51 years (22–79) were analyzed. WTV correlated significantly with age (p < 0.01) but showed no significantly difference in PLWH (median = 3.4 mm) compared to healthy controls (median = 2.8 mm) (p = 0.085). PLWH had both significantly higher BDI-Scores (p = 0.005) and FSS-Scores (p = 0.012). Controls reported higher QoL (SF-36) with significant differences in most items. However, the overall cognitive performance (CERAD total score) showed no significant difference. The WTV of all subjects correlated with neurocognitive performance measured as CERAD total score (p = 0.009) and trail making tests A (p < 0.001) and B (p = 0.018). There was no correlation between the scores of BDI, FSS, SF-36, and CERAD-PLUS items and WTV. Conclusion WTV is considered as a predictor of cognitive deficits in neurodegenerative diseases. Nevertheless, we found no significant difference in WTV or overall cognitive performance between PLWH and controls. PLWH suffer more often from depression and fatigue and report reduced QoL when compared to healthy controls.
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Affiliation(s)
- Dominic Kaddu-Mulindwa
- Department of Hematology and Oncology, Saarland University Medical School, Homburg, Germany
| | - Matthias Heit
- Department of Hematology and Oncology, Saarland University Medical School, Homburg, Germany
| | - Gudrun Wagenpfeil
- Institute for Medical Biometrics, Epidemiology and Medical Computer Science, Saarland University Medical School, Homburg, Germany
| | - Moritz Bewarder
- Department of Hematology and Oncology, Saarland University Medical School, Homburg, Germany
| | - Klaus Fassbender
- Department of Neurology, Saarland University Medical School, Homburg, Germany
| | - Stefanie Behnke
- Department of Neurology, Saarland University Medical School, Homburg, Germany
| | - Umut Yilmaz
- Department of Neuroradiology, Saarland University Medical School, Homburg, Germany
| | - Mathias Fousse
- Department of Neurology, Saarland University Medical School, Homburg, Germany
- *Correspondence: Mathias Fousse
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44
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Kang K, Song X. Joint Modeling of Longitudinal Imaging and Survival Data. J Comput Graph Stat 2022. [DOI: 10.1080/10618600.2022.2102027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Affiliation(s)
- Kai Kang
- Department of Statistics, The Chinese University of Hong Kong, Hong Kong, China
| | - Xinyuan Song
- Department of Statistics, The Chinese University of Hong Kong, Hong Kong, China
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45
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de Mélo Silva Júnior ML, Diniz PRB, de Souza Vilanova MV, Basto GPT, Valença MM. Brain ventricles, CSF and cognition: a narrative review. Psychogeriatrics 2022; 22:544-552. [PMID: 35488797 DOI: 10.1111/psyg.12839] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 03/07/2022] [Accepted: 04/12/2022] [Indexed: 11/29/2022]
Abstract
The brain ventricles are structures that have been related to cognition since antiquity. They are essential components in the development and maintenance of brain functions. The aging process runs with the enlargement of ventricles and is related to a less selective blood-cerebrospinal fluid barrier and then a more toxic cerebrospinal fluid environment. The study of brain ventricles as a biological marker of aging is promissing because they are structures easily identified in neuroimaging studies, present good inter-rater reliability, and measures of them can identify brain atrophy earlier than cortical structures. The ventricular system also plays roles in the development of dementia, since dysfunction in the clearance of beta-amyloid protein is a key mechanism in sporadic Alzheimer's disease. The morphometric and volumetric studies of the brain ventricles can help to distinguish between healthy elderly and persons with mild cognitive impairment (MCI) and dementia. Brain ventricle data may contribute to the appropriate allocation of individuals in groups at higher risk for MCI-dementia progression in clinical trials and to measuring therapeutic responses in these studies, as well as providing differential diagnosis, such as normal pressure hydrocephalus. Here, we reviewed the pathophysiology of healthy aging and cognitive decline, focusing on the role of the choroid plexus and brain ventricles in this process.
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Affiliation(s)
- Mário Luciano de Mélo Silva Júnior
- Medical School, Universidade Federal de Pernambuco, Recife, Brazil.,Medical School, Centro Universitário Maurício de Nassau, Recife, Brazil.,Neurology Unit, Hospital da Restauração, Recife, Brazil
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Bordin V, Coluzzi D, Rivolta MW, Baselli G. Explainable AI Points to White Matter Hyperintensities for Alzheimer's Disease Identification: a Preliminary Study. Annu Int Conf IEEE Eng Med Biol Soc 2022; 2022:484-487. [PMID: 36086369 DOI: 10.1109/embc48229.2022.9871306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Deep Learning approaches are powerful tools in a great variety of classification tasks. However, they are limitedly accepted or trusted in clinical frameworks due to their typical "black box" outline: their architecture is well-known, but processes employed in classification are often inaccessible to humans. With this work, we explored the problem of "Explainable AI" (XAI) in Alzheimer's disease (AD) classification tasks. Data from a neuroimaging cohort (n = 251 from OASIS-3) of early-stage AD dementia and healthy controls (HC) were analysed. The MR scans were initially fed to a pre-trained DL model, which achieved good performance on the test set (AUC: 0.82, TPR: 0.78, TNR: 0.81). Results were then investigated by means of an XAI approach (Occlusion Sensitivity method) that provided measures of relevance (RV) as outcome. We compared RV values obtained within healthy tissues with those underlying white matter hyperintensity (WMH) lesions. The analysis was conducted on 4 different groups of data, obtained by stratifying correct and misclassified images according to the health condition of participants (AD/HC). Results highlighted that the DL model found favourable leveraging lesioned brain areas for AD identification. A statistically significant difference ( ) between WMH and healthy tissue contributions was indeed observed for AD recognition, differently from the HC case ( p=0.27). Clinical Relevance - This study, though preliminary, suggested that DL models might be trained to use known clinical information and reinforced the role of WMHs as neuroimaging biomarker for AD dementia. The outlined findings have a significant clinical relevance as they prepare the ground for a progressive increase in the level of trust laid in DL approaches.
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Jeevakumar V, Sefton R, Chan J, Gopinath B, Liew G, Shah TM, Siette J. Association between retinal markers and cognition in older adults: a systematic review. BMJ Open 2022; 12:e054657. [PMID: 35728906 PMCID: PMC9214387 DOI: 10.1136/bmjopen-2021-054657] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
OBJECTIVES To appraise the existing literature reporting an association between retinal markers and cognitive impairment in adults aged 65 years and over and to provide directions for future use of retinal scanning as a potential tool for dementia diagnosis. DESIGN Systematic review of peer-reviewed empirical articles investigating the association of retinal markers in assessing cognitive impairment. DATA SOURCES Three electronic databases, Medline, PsycINFO and EMBASE were searched from inception until March 2022. ELIGIBILITY CRITERIA All empirical articles in English investigating the association between retinal markers and cognition in humans aged ≥65 years using various retinal scanning methodologies were included. Studies with no explicit evaluation of retinal scanning and cognitive outcomes were excluded. Risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies tool. DATA EXTRACTION AND SYNTHESIS Data extraction was conducted by two authors (VJ, RS) and reviewed by another author (JS). Results were synthesised and described narratively. RESULTS Sixty-seven eligible studies examining 6815 older adults were included. Majority of studies were cross-sectional (n=60; 89.6%). Optical coherence tomography (OCT) was the most commonly used retinal scanning methodology to measure the thickness of retinal nerve fibre layer, the ganglion cell complex, choroid and macula. 51.1% of cross-sectional studies using OCT reported an association between the thinning of at least one retinal parameter and poor cognition. Longitudinal studies (n=6) using OCT also mostly identified significant reductions in retinal nerve fibre layer thickness with cognitive decline. Study quality was overall moderate. CONCLUSION Retinal nerve fibre layer thickness is linked with cognitive performance and therefore may have the potential to detect cognitive impairment in older adults. Further longitudinal studies are required to validate our synthesis and understand underlying mechanisms before recommending implementation of OCT as a dementia screening tool in clinical practice. PROSPERO REGISTRATION NUMBER CRD42020176757.
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Affiliation(s)
- Varshanie Jeevakumar
- Australian Institute of Health Innovation, Macquarie University, Macquarie Park, New South Wales, Australia
| | - Rebekah Sefton
- Australian Institute of Health Innovation, Macquarie University, Macquarie Park, New South Wales, Australia
| | - Joyce Chan
- New Look Eyewear, Maitland, New South Wales, Australia
| | - Bamini Gopinath
- Department of Linguistics, Australian Hearing Hub, Macquarie University, Macquarie Park, New South Wales, Australia
| | - Gerald Liew
- Centre for Vision Research, The University of Sydney, Sydney, New South Wales, Australia
| | - Tejal M Shah
- Macquarie Medical School, Macquarie University, North Ryde, New South Wales, Australia
| | - Joyce Siette
- Australian Institute of Health Innovation, Macquarie University, Macquarie Park, New South Wales, Australia
- MARCS Institute for Brain, Behaviour and Development, Western Sydney University, Westmead, New South Wales, Australia
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Kim J, Jeong M, Stiles WR, Choi HS. Neuroimaging Modalities in Alzheimer’s Disease: Diagnosis and Clinical Features. Int J Mol Sci 2022; 23:6079. [PMID: 35682758 PMCID: PMC9181385 DOI: 10.3390/ijms23116079] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Revised: 05/25/2022] [Accepted: 05/26/2022] [Indexed: 11/17/2022] Open
Abstract
Alzheimer’s disease (AD) is a neurodegenerative disease causing progressive cognitive decline until eventual death. AD affects millions of individuals worldwide in the absence of effective treatment options, and its clinical causes are still uncertain. The onset of dementia symptoms indicates severe neurodegeneration has already taken place. Therefore, AD diagnosis at an early stage is essential as it results in more effective therapy to slow its progression. The current clinical diagnosis of AD relies on mental examinations and brain imaging to determine whether patients meet diagnostic criteria, and biomedical research focuses on finding associated biomarkers by using neuroimaging techniques. Multiple clinical brain imaging modalities emerged as potential techniques to study AD, showing a range of capacity in their preciseness to identify the disease. This review presents the advantages and limitations of brain imaging modalities for AD diagnosis and discusses their clinical value.
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Miyan J, Buttercase C, Beswick E, Miyan S, Moshkdanian G, Naz N. Folate Related Pathway Gene Analysis Reveals a Novel Metabolic Variant Associated with Alzheimer’s Disease with a Change in Metabolic Profile. Metabolites 2022; 12:475. [PMID: 35736408 PMCID: PMC9230919 DOI: 10.3390/metabo12060475] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 05/20/2022] [Accepted: 05/20/2022] [Indexed: 11/30/2022] Open
Abstract
Metabolic disorders may be important potential causative pathways to Alzheimer’s disease (AD). Cerebrospinal fluid (CSF) decreasing output, raised intracranial pressure, and ventricular enlargement have all been linked to AD. Cerebral folate metabolism may be a key player since this is significantly affected by such changes in CSF, and genetic susceptibilities may exist in this pathway. In the current study, we aimed to identify whether any single nucleotide polymorphism (SNPs) affecting folate and the associated metabolic pathways were significantly associated with AD. We took a functional nutrigenomics approach to look for SNPs in genes for the linked folate, methylation, and biogenic amine neurotransmitter pathways. Changes in metabolism were found with the SNPs identified. An abnormal SNP in methylene tetrahydrofolate dehydrogenase 1 (MTHFD1) was significantly predictive of AD and associated with an increase in tissue glutathione. Individuals without these SNPs had normal levels of glutathione but significantly raised MTHFD1. Both changes would serve to decrease potentially neurotoxic levels of homocysteine. Seven additional genes were associated with Alzheimer’s and five with normal ageing. MTHFD1 presents a strong prediction of susceptibility and disease among the SNPs associated with AD. Associated physiological changes present potential biomarkers for identifying at-risk individuals.
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50
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Bahsoun MA, Khan MU, Mitha S, Ghazvanchahi A, Khosravani H, Jabehdar Maralani P, Tardif JC, Moody AR, Tyrrell PN, Khademi A. FLAIR MRI biomarkers of the normal appearing brain matter are related to cognition. Neuroimage Clin 2022; 34:102955. [PMID: 35180579 PMCID: PMC8857609 DOI: 10.1016/j.nicl.2022.102955] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 01/28/2022] [Accepted: 02/01/2022] [Indexed: 01/04/2023]
Abstract
Normal appearing brain matter (NABM) biomarkers in FLAIR MRI are related to cognition. NABM texture in FLAIR MRI is correlated to mean diffusivity (MD) in dMRI. Analysis conducted on large multicentre FLAIR MRI dataset: 1400 subjects, 87 centers. NABM biomarkers vary differently across age and MoCA categories. Biomarkers showed differences in patients with AD dementia and vascular disease.
A novel biomarker panel was proposed to quantify macro and microstructural biomarkers from the normal-appearing brain matter (NABM) in multicentre fluid-attenuation inversion recovery (FLAIR) MRI. The NABM is composed of the white and gray matter regions of the brain, with the lesions and cerebrospinal fluid removed. The primary hypothesis was that NABM biomarkers from FLAIR MRI are related to cognitive outcome as determined by MoCA score. There were three groups of features designed for this task based on 1) texture: microstructural integrity (MII), macrostructural damage (MAD), microstructural damage (MID), 2) intensity: median, skewness, kurtosis and 3) volume: NABM to ICV volume ratio. Biomarkers were extracted from over 1400 imaging volumes from more than 87 centres and unadjusted ANOVA analysis revealed significant differences in means of the MII, MAD, and NABM volume biomarkers across all cognitive groups. In an adjusted ANCOVA model, a significant relationship between MoCA categories was found that was dependent on subject age for MII, MAD, intensity, kurtosis and NABM volume biomarkers. These results demonstrate that structural brain changes in the NABM are related to cognitive outcome (with different relationships depending on the age of the subjects). Therefore these biomarkers have high potential for clinical translation. As a secondary hypothesis, we investigated whether texture features from FLAIR MRI can quantify microstructural changes related to how “structured” or “damaged” the tissue is. Based on correlation analysis with diffusion weighted MRI (dMRI), it was shown that FLAIR MRI texture biomarkers (MII and MAD) had strong correlations to mean diffusivity (MD) which is related to tissue degeneration in the GM and WM regions. As FLAIR MRI is routinely collected for clinical neurological examinations, novel biomarkers from FLAIR MRI could be used to supplement current clinical biomarkers and for monitoring disease progression. Biomarkers could also be used to stratify patients into homogeneous disease subgroups for clinical trials, or to learn more about mechanistic development of dementia disease.
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Affiliation(s)
- M-A Bahsoun
- Electrical, Computer and Biomedical Engineering Dept., Ryerson University, Toronto, ON, Canada
| | - M U Khan
- Electrical, Computer and Biomedical Engineering Dept., Ryerson University, Toronto, ON, Canada
| | - S Mitha
- Electrical, Computer and Biomedical Engineering Dept., Ryerson University, Toronto, ON, Canada
| | - A Ghazvanchahi
- Electrical, Computer and Biomedical Engineering Dept., Ryerson University, Toronto, ON, Canada
| | - H Khosravani
- Hurvitz Brain Sciences Program Division of Neurology, Department of Medicine, University of Toronto, Toronto, ON, Canada
| | | | - J-C Tardif
- Montreal Heart Institute, Montreal, QU, Canada; Department of Medicine, Université de Montréal, QU, Canada
| | - A R Moody
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
| | - P N Tyrrell
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada; Department of Statistical Sciences, University of Toronto, Toronto, ON, Canada; Institute of Medical Science, University of Toronto, Toronto, ON, Canada
| | - A Khademi
- Electrical, Computer and Biomedical Engineering Dept., Ryerson University, Toronto, ON, Canada; Keenan Research Center for Biomedical Science, St. Michael's Hospital, Unity Health Network, Toronto, ON, Canada; Institute for Biomedical Engineering, Science and Technology (iBEST), a partnership between St. Michael's Hospital and Ryerson University, Toronto, ON, Canada
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