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Shen H, Liu K, Kong F, Ren M, Wang X, Wang S. Strategies for measuring concentrations and forms of amyloid-β peptides. Biosens Bioelectron 2024; 259:116405. [PMID: 38776801 DOI: 10.1016/j.bios.2024.116405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 05/01/2024] [Accepted: 05/17/2024] [Indexed: 05/25/2024]
Abstract
Alzheimer's disease (AD) is affecting more and more people worldwide without the effective treatment, while the existed pathological mechanism has been confirmed barely useful in the treatment. Amyloid-β peptide (Aβ), a main component of senile plaque, is regarded as the most promising target in AD treatment. Aβ clearance from AD brain seems to be a reliably therapeutic strategy, as the two exited drugs, GV-971 and aducanumab, are both developed based on it. However, doubt still exists. To exhaustive expound on the pathological mechanism of Aβ, rigorous analyses on the concentrations and aggregation forms are essential. Thus, it is attracting broad attention these years. However, most of the sensors have not been used in pathological studies, as the lack of the bridge between analytical chemist and pathologists. In this review, we made a brief introduce on Aβ-related pathological mechanism included in β-amyloid hypothesis to elucidate the detection conditions of sensor methods. Furthermore, a summary of the sensor methods was made, which were based on Aβ concentrations and form detections that have been developed in the past 10 years. As the greatest number of the sensors were built on fluorescent spectroscopy, electrochemistry, and Roman spectroscopy, detailed elucidation on them was made. Notably, the aggregation process is another important factor in revealing the progress of AD and developing the treatment methods, so the sensors on monitoring Aβ aggregation processes were also summarized.
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Affiliation(s)
- Hangyu Shen
- State Key Laboratory of Biobased Materials and Green Papermaking, Qilu University of Technology (Shandong Academy of Sciences), Jinan, Shandong, 250353, PR China
| | - Keyin Liu
- State Key Laboratory of Biobased Materials and Green Papermaking, Qilu University of Technology (Shandong Academy of Sciences), Jinan, Shandong, 250353, PR China
| | - Fangong Kong
- State Key Laboratory of Biobased Materials and Green Papermaking, Qilu University of Technology (Shandong Academy of Sciences), Jinan, Shandong, 250353, PR China
| | - Mingguang Ren
- State Key Laboratory of Biobased Materials and Green Papermaking, Qilu University of Technology (Shandong Academy of Sciences), Jinan, Shandong, 250353, PR China
| | - Xiaoying Wang
- State Key Laboratory of Biobased Materials and Green Papermaking, Qilu University of Technology (Shandong Academy of Sciences), Jinan, Shandong, 250353, PR China; Shandong Haizhibao Ocean Technology Co., Ltd, Weihai, Shandong, 264333, PR China.
| | - Shoujuan Wang
- State Key Laboratory of Biobased Materials and Green Papermaking, Qilu University of Technology (Shandong Academy of Sciences), Jinan, Shandong, 250353, PR China.
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2
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Assfaw AD, Schindler SE, Morris JC. Advances in blood biomarkers for Alzheimer disease (AD): A review. Kaohsiung J Med Sci 2024. [PMID: 38888066 DOI: 10.1002/kjm2.12870] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2024] [Accepted: 06/04/2024] [Indexed: 06/20/2024] Open
Abstract
Alzheimer disease (AD) and Alzheimer Disease and Related Dementias (AD/ADRD) are growing public health challenges globally affecting millions of older adults, necessitating concerted efforts to advance our understanding and management of these conditions. AD is a progressive neurodegenerative disorder characterized pathologically by amyloid plaques and tau neurofibrillary tangles that are the primary cause of dementia in older individuals. Early and accurate diagnosis of AD dementia is crucial for effective intervention and treatment but has proven challenging to accomplish. Although testing for AD brain pathology with cerebrospinal fluid (CSF) or positron emission tomography (PET) has been available for over 2 decades, most patients never underwent this testing because of inaccessibility, high out-of-pocket costs, perceived risks, and the lack of AD-specific treatments. However, in recent years, rapid progress has been made in developing blood biomarkers for AD/ADRD. Consequently, blood biomarkers have emerged as promising tools for non-invasive and cost-effective diagnosis, prognosis, and monitoring of AD progression. This review presents the evolving landscape of blood biomarkers in AD/ADRD and explores their potential applications in clinical practice for early detection, prognosis, and therapeutic interventions. It covers recent advances in blood biomarkers, including amyloid beta (Aβ) peptides, tau protein, neurofilament light chain (NfL), and glial fibrillary acidic protein (GFAP). It also discusses their diagnostic and prognostic utility while addressing associated challenges and limitations. Future research directions in this rapidly evolving field are also proposed.
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Affiliation(s)
- Araya Dimtsu Assfaw
- Department of Neurology, Knight Alzheimer Disease Research Center (Knight ADRC), Washington University School of Medicine, St. Louis, Missouri, USA
| | - Suzanne E Schindler
- Department of Neurology, Knight Alzheimer Disease Research Center (Knight ADRC), Washington University School of Medicine, St. Louis, Missouri, USA
| | - John C Morris
- Department of Neurology, Knight Alzheimer Disease Research Center (Knight ADRC), Washington University School of Medicine, St. Louis, Missouri, USA
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3
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Angelucci F, Ai AR, Piendel L, Cerman J, Hort J. Integrating AI in fighting advancing Alzheimer: diagnosis, prevention, treatment, monitoring, mechanisms, and clinical trials. Curr Opin Struct Biol 2024; 87:102857. [PMID: 38838385 DOI: 10.1016/j.sbi.2024.102857] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2024] [Revised: 04/15/2024] [Accepted: 05/12/2024] [Indexed: 06/07/2024]
Abstract
The application of artificial intelligence (AI) in neurology is a growing field offering opportunities to improve accuracy of diagnosis and treatment of complicated neuronal disorders, plus fostering a deeper understanding of the aetiologies of these diseases through AI-based analyses of large omics data. The most common neurodegenerative disease, Alzheimer's disease (AD), is characterized by brain accumulation of specific pathological proteins, accompanied by cognitive impairment. In this review, we summarize the latest progress on the use of AI in different AD-related fields, such as analysis of neuroimaging data enabling early and accurate AD diagnosis; prediction of AD progression, identification of patients at higher risk and evaluation of new treatments; improvement of the evaluation of drug response using AI algorithms to analyze patient clinical and neuroimaging data; the development of personalized AD therapies; and the use of AI-based techniques to improve the quality of daily life of AD patients and their caregivers.
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Affiliation(s)
- Francesco Angelucci
- Memory Clinic, Department of Neurology, Second Faculty of Medicine, Charles University and Motol University Hospital, Prague, Czech Republic.
| | - Alice Ruixue Ai
- Department of Clinical Molecular Biology, University of Oslo and Akershus University Hospital, 1478 Lørenskog, Norway
| | - Lydia Piendel
- Memory Clinic, Department of Neurology, Second Faculty of Medicine, Charles University and Motol University Hospital, Prague, Czech Republic; Augusta University/University of Georgia Medical Partnership, Medical College of Georgia, Athens, GA, USA
| | - Jiri Cerman
- Memory Clinic, Department of Neurology, Second Faculty of Medicine, Charles University and Motol University Hospital, Prague, Czech Republic
| | - Jakub Hort
- Memory Clinic, Department of Neurology, Second Faculty of Medicine, Charles University and Motol University Hospital, Prague, Czech Republic; INDRC, International Neurodegenerative Disorders Research Center, Prague, Czech Republic
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4
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Ly MT, Adler J, Ton Loy AF, Edmonds EC, Bondi MW, Delano-Wood L. Comparing neuropsychological, typical, and ADNI criteria for the diagnosis of mild cognitive impairment in Vietnam-era veterans. J Int Neuropsychol Soc 2024; 30:439-447. [PMID: 38263745 DOI: 10.1017/s135561772301144x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/25/2024]
Abstract
OBJECTIVE Neuropsychological criteria for mild cognitive impairment (MCI) more accurately predict progression to Alzheimer's disease (AD) and are more strongly associated with AD biomarkers and neuroimaging profiles than ADNI criteria. However, research to date has been conducted in relatively healthy samples with few comorbidities. Given that history of traumatic brain injury (TBI) and post-traumatic stress disorder (PTSD) are risk factors for AD and common in Veterans, we compared neuropsychological, typical (Petersen/Winblad), and ADNI criteria for MCI in Vietnam-era Veterans with histories of TBI or PTSD. METHOD 267 Veterans (mean age = 69.8) from the DOD-ADNI study were evaluated for MCI using neuropsychological, typical, and ADNI criteria. Linear regressions adjusting for age and education assessed associations between MCI status and AD biomarker levels (cerebrospinal fluid [CSF] p-tau181, t-tau, and Aβ42) by diagnostic criteria. Logistic regressions adjusting for age and education assessed the effects of TBI severity and PTSD symptom severity simultaneously on MCI classification by each criteria. RESULTS Agreement between criteria was poor. Neuropsychological criteria identified more Veterans with MCI than typical or ADNI criteria, and were associated with higher CSF p-tau181 and t-tau. Typical and ADNI criteria were not associated with CSF biomarkers. PTSD symptom severity predicted MCI diagnosis by neuropsychological and ADNI criteria. History of moderate/severe TBI predicted MCI by typical and ADNI criteria. CONCLUSIONS MCI diagnosis using sensitive neuropsychological criteria is more strongly associated with AD biomarkers than conventional diagnostic methods. MCI diagnostics in Veterans would benefit from incorporation of comprehensive neuropsychological methods and consideration of the impact of PTSD.
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Affiliation(s)
- Monica T Ly
- Veterans Affairs San Diego Healthcare System, San Diego, CA, USA
- Department of Psychiatry, University of California San Diego Health, La Jolla, CA, USA
| | - Jennifer Adler
- Veterans Affairs San Diego Healthcare System, San Diego, CA, USA
- Department of Psychiatry, University of California San Diego Health, La Jolla, CA, USA
| | - Adan F Ton Loy
- Veterans Affairs San Diego Healthcare System, San Diego, CA, USA
| | - Emily C Edmonds
- Banner Alzheimer's Institute, Tucson, AZ, USA
- Departments of Neurology and Psychology, University of Arizona, Tucson, AZ, USA
| | - Mark W Bondi
- Veterans Affairs San Diego Healthcare System, San Diego, CA, USA
- Department of Psychiatry, University of California San Diego Health, La Jolla, CA, USA
| | - Lisa Delano-Wood
- Veterans Affairs San Diego Healthcare System, San Diego, CA, USA
- Department of Psychiatry, University of California San Diego Health, La Jolla, CA, USA
- Center for Stress and Mental Health, VA San Diego Healthcare System, San Diego, CA, USA
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5
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Odusami M, Maskeliūnas R, Damaševičius R, Misra S. Machine learning with multimodal neuroimaging data to classify stages of Alzheimer's disease: a systematic review and meta-analysis. Cogn Neurodyn 2024; 18:775-794. [PMID: 38826669 PMCID: PMC11143094 DOI: 10.1007/s11571-023-09993-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 06/23/2023] [Accepted: 07/17/2023] [Indexed: 06/04/2024] Open
Abstract
In recent years, Alzheimer's disease (AD) has been a serious threat to human health. Researchers and clinicians alike encounter a significant obstacle when trying to accurately identify and classify AD stages. Several studies have shown that multimodal neuroimaging input can assist in providing valuable insights into the structural and functional changes in the brain related to AD. Machine learning (ML) algorithms can accurately categorize AD phases by identifying patterns and linkages in multimodal neuroimaging data using powerful computational methods. This study aims to assess the contribution of ML methods to the accurate classification of the stages of AD using multimodal neuroimaging data. A systematic search is carried out in IEEE Xplore, Science Direct/Elsevier, ACM DigitalLibrary, and PubMed databases with forward snowballing performed on Google Scholar. The quantitative analysis used 47 studies. The explainable analysis was performed on the classification algorithm and fusion methods used in the selected studies. The pooled sensitivity and specificity, including diagnostic efficiency, were evaluated by conducting a meta-analysis based on a bivariate model with the hierarchical summary receiver operating characteristics (ROC) curve of multimodal neuroimaging data and ML methods in the classification of AD stages. Wilcoxon signed-rank test is further used to statistically compare the accuracy scores of the existing models. With a 95% confidence interval of 78.87-87.71%, the combined sensitivity for separating participants with mild cognitive impairment (MCI) from healthy control (NC) participants was 83.77%; for separating participants with AD from NC, it was 94.60% (90.76%, 96.89%); for separating participants with progressive MCI (pMCI) from stable MCI (sMCI), it was 80.41% (74.73%, 85.06%). With a 95% confidence interval (78.87%, 87.71%), the Pooled sensitivity for distinguishing mild cognitive impairment (MCI) from healthy control (NC) participants was 83.77%, with a 95% confidence interval (90.76%, 96.89%), the Pooled sensitivity for distinguishing AD from NC was 94.60%, likewise (MCI) from healthy control (NC) participants was 83.77% progressive MCI (pMCI) from stable MCI (sMCI) was 80.41% (74.73%, 85.06%), and early MCI (EMCI) from NC was 86.63% (82.43%, 89.95%). Pooled specificity for differentiating MCI from NC was 79.16% (70.97%, 87.71%), AD from NC was 93.49% (91.60%, 94.90%), pMCI from sMCI was 81.44% (76.32%, 85.66%), and EMCI from NC was 85.68% (81.62%, 88.96%). The Wilcoxon signed rank test showed a low P-value across all the classification tasks. Multimodal neuroimaging data with ML is a promising future in classifying the stages of AD but more research is required to increase the validity of its application in clinical practice.
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Affiliation(s)
- Modupe Odusami
- Department of Multimedia Engineering, Kaunas University of Technology, Kaunas, Lithuania
| | - Rytis Maskeliūnas
- Department of Multimedia Engineering, Kaunas University of Technology, Kaunas, Lithuania
| | | | - Sanjay Misra
- Department of Applied Data Science, Institute for Energy Technology, Halden, Norway
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Soyer A, Goutal S, Leterrier S, Marie S, Larrat B, Selingue E, Winkeler A, Sarazin M, Bottlaender M, Tournier N. [ 18F]2-fluoro-2-deoxy-sorbitol ([ 18F]FDS) PET imaging repurposed for quantitative estimation of blood-brain barrier permeability in a rat model of Alzheimer's disease. ANNALES PHARMACEUTIQUES FRANÇAISES 2024:S0003-4509(24)00061-0. [PMID: 38657857 DOI: 10.1016/j.pharma.2024.04.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Revised: 04/05/2024] [Accepted: 04/17/2024] [Indexed: 04/26/2024]
Abstract
Numerous studies suggest that blood-brain barrier (BBB) dysfunction may contribute to the progression of Alzheimer's disease (AD). Clinically available neuroimaging methods are needed for quantitative "scoring" of BBB permeability in AD patients. [18F]2-fluoro-2-deoxy-sorbitol ([18F]FDS), which can be easily obtained from simple chemical reduction of commercial [18F]2-fluoro-2-deoxy-glucose ([18F]FDG), was investigated as a small-molecule marker of BBB permeability, in a pre-clinical model of AD using in vivo PET imaging. Chemical reduction of [18F]FDG to [18F]FDS was obtained with a 100% conversion yield. Dynamic PET acquisitions were performed in the APP/PS1 rat model of AD (TgF344-AD, n=3) compared with age-matched littermates (WT, n=4). The brain uptake of [18F]FDS was determined in selected brain regions, delineated from a coregistered rat brain template. The brain uptake of [18F]FDS in the brain regions of AD rats versus WT rats was compared using a 2-way ANOVA. The uptake of [18F]FDS was significantly higher in the whole brain of AD rats, as compared with WT rats (P<0.001), suggesting increased BBB permeability. Enhanced brain uptake of [18F]FDS in AD rats was significantly different across brain regions (P<0.001). Minimum difference was observed in the amygdala (+89.0±7.6%, P<0.001) and maximum difference was observed in the midbrain (+177.8±29.2%, P<0.001). [18F]FDS, initially proposed as radio-pharmaceutical to estimate renal filtration using PET imaging, can be repurposed for non-invasive and quantitative determination of BBB permeability in vivo. Making the best with the quantitative properties of PET imaging, it was possible to estimate the extent of enhanced BBB permeability in a rat model of AD.
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Affiliation(s)
- Amélie Soyer
- Service hospitalier Frédéric-Joliot, laboratoire d'imagerie biomédicale multimodale (BioMaps), Inserm, CNRS, CEA, université Paris-Saclay, 91401 Orsay, France
| | - Sébastien Goutal
- Service hospitalier Frédéric-Joliot, laboratoire d'imagerie biomédicale multimodale (BioMaps), Inserm, CNRS, CEA, université Paris-Saclay, 91401 Orsay, France
| | - Sarah Leterrier
- Service hospitalier Frédéric-Joliot, laboratoire d'imagerie biomédicale multimodale (BioMaps), Inserm, CNRS, CEA, université Paris-Saclay, 91401 Orsay, France
| | - Solène Marie
- Service hospitalier Frédéric-Joliot, laboratoire d'imagerie biomédicale multimodale (BioMaps), Inserm, CNRS, CEA, université Paris-Saclay, 91401 Orsay, France
| | - Benoit Larrat
- Centre d'études de Saclay, CEA, CNRS, NeuroSpin/BAOBAB, Paris-Saclay University, 91191 Gif-sur-Yvette, France
| | - Erwan Selingue
- Centre d'études de Saclay, CEA, CNRS, NeuroSpin/BAOBAB, Paris-Saclay University, 91191 Gif-sur-Yvette, France
| | - Alexandra Winkeler
- Service hospitalier Frédéric-Joliot, laboratoire d'imagerie biomédicale multimodale (BioMaps), Inserm, CNRS, CEA, université Paris-Saclay, 91401 Orsay, France
| | - Marie Sarazin
- Service hospitalier Frédéric-Joliot, laboratoire d'imagerie biomédicale multimodale (BioMaps), Inserm, CNRS, CEA, université Paris-Saclay, 91401 Orsay, France
| | - Michel Bottlaender
- Service hospitalier Frédéric-Joliot, laboratoire d'imagerie biomédicale multimodale (BioMaps), Inserm, CNRS, CEA, université Paris-Saclay, 91401 Orsay, France
| | - Nicolas Tournier
- Service hospitalier Frédéric-Joliot, laboratoire d'imagerie biomédicale multimodale (BioMaps), Inserm, CNRS, CEA, université Paris-Saclay, 91401 Orsay, France.
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Chen A, Shea D, Daggett V. Performance of SOBA-AD blood test in discriminating Alzheimer's disease patients from cognitively unimpaired controls in two independent cohorts. Sci Rep 2024; 14:7946. [PMID: 38575622 PMCID: PMC10995183 DOI: 10.1038/s41598-024-57107-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Accepted: 03/14/2024] [Indexed: 04/06/2024] Open
Abstract
Amyloid-beta (Aβ) toxic oligomers are critical early players in the molecular pathology of Alzheimer's disease (AD). We have developed a Soluble Oligomer Binding Assay (SOBA-AD) for detection of these Aβ oligomers that contain α-sheet secondary structure that discriminates plasma samples from patients on the AD continuum from non-AD controls. We tested 265 plasma samples from two independent cohorts to investigate the performance of SOBA-AD. Testing was performed at two different sites, with different personnel, reagents, and instrumentation. Across two cohorts, SOBA-AD discriminated AD patients from cognitively unimpaired (CU) subjects with 100% sensitivity, > 95% specificity, and > 98% area under the curve (AUC) (95% CI 0.95-1.00). A SOBA-AD positive readout, reflecting α-sheet toxic oligomer burden, was found in AD patients, and not in controls, providing separation of the two populations, aside from 5 SOBA-AD positive controls. Based on an earlier SOBA-AD study, the Aβ oligomers detected in these CU subjects may represent preclinical cases of AD. The results presented here support the value of SOBA-AD as a promising blood-based tool for the detection and confirmation of AD.
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Affiliation(s)
- Amy Chen
- AltPep Corporation, 1150 Eastlake Avenue N, Suite 800, Seattle, WA, 98109, USA
| | - Dylan Shea
- AltPep Corporation, 1150 Eastlake Avenue N, Suite 800, Seattle, WA, 98109, USA
- University of Washington, Box 355610, Seattle, WA, 98195-5610, USA
| | - Valerie Daggett
- AltPep Corporation, 1150 Eastlake Avenue N, Suite 800, Seattle, WA, 98109, USA.
- University of Washington, Box 355610, Seattle, WA, 98195-5610, USA.
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8
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Shaheen H, Melnik R, Singh S. Data-driven Stochastic Model for Quantifying the Interplay Between Amyloid-beta and Calcium Levels in Alzheimer's Disease. Stat Anal Data Min 2024; 17:e11679. [PMID: 38646460 PMCID: PMC11031189 DOI: 10.1002/sam.11679] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2023] [Accepted: 03/23/2024] [Indexed: 04/23/2024]
Abstract
The abnormal aggregation of extracellular amyloid-β ( A β ) in senile plaques resulting in calcium C a + 2 dyshomeostasis is one of the primary symptoms of Alzheimer's disease (AD). Significant research efforts have been devoted in the past to better understand the underlying molecular mechanisms driving A β deposition and C a + 2 dysregulation. Importantly, synaptic impairments, neuronal loss, and cognitive failure in AD patients are all related to the buildup of intraneuronal A β accumulation. Moreover, increasing evidence show a feed-forward loop between A β and C a + 2 levels, i.e. A β disrupts neuronal C a + 2 levels, which in turn affects the formation of A β . To better understand this interaction, we report a novel stochastic model where we analyze the positive feedback loop between A β and C a + 2 using ADNI data. A good therapeutic treatment plan for AD requires precise predictions. Stochastic models offer an appropriate framework for modelling AD since AD studies are observational in nature and involve regular patient visits. The etiology of AD may be described as a multi-state disease process using the approximate Bayesian computation method. So, utilizing ADNI data from 2-year visits for AD patients, we employ this method to investigate the interplay between A β and C a + 2 levels at various disease development phases. Incorporating the ADNI data in our physics-based Bayesian model, we discovered that a sufficiently large disruption in either A β metabolism or intracellular C a + 2 homeostasis causes the relative growth rate in both C a + 2 and A β , which corresponds to the development of AD. The imbalance of C a + 2 ions causes A β disorders by directly or indirectly affecting a variety of cellular and subcellular processes, and the altered homeostasis may worsen the abnormalities of C a + 2 ion transportation and deposition. This suggests that altering the C a + 2 balance or the balance between A β and C a + 2 by chelating them may be able to reduce disorders associated with AD and open up new research possibilities for AD therapy.
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Affiliation(s)
- Hina Shaheen
- Faculty of Science, University of Manitoba, Winnipeg, MB R3T 2N2, Canada
| | - Roderick Melnik
- MS2Discovery Interdisciplinary Research Institute, Wilfrid Laurier University, Waterloo, ON N2L 3C5, Canada
| | - Sundeep Singh
- Faculty of Sustainable Design Engineering, University of Prince Edward Island, Charlottetown, PE C1A 4P3, Canada
| | - The Alzheimer’s Disease Neuroimaging Initiative
- Data used in preparation of this article were generated by the Alzheimer’s Disease Metabolomics Consortium (ADMC). As such, the investigators within the ADMC provided data, but did not participate in the analysis or writing of this report. A complete listing of ADMC investigators can be found at: https://sites.duke.edu/adnimetab/team/
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9
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Ching CRK, Kang MJY, Thompson PM. Large-Scale Neuroimaging of Mental Illness. Curr Top Behav Neurosci 2024. [PMID: 38554248 DOI: 10.1007/7854_2024_462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/01/2024]
Abstract
Neuroimaging has provided important insights into the brain variations related to mental illness. Inconsistencies in prior studies, however, call for methods that lead to more replicable and generalizable brain markers that can reliably predict illness severity, treatment course, and prognosis. A paradigm shift is underway with large-scale international research teams actively pooling data and resources to drive consensus findings and test emerging methods aimed at achieving the goals of precision psychiatry. In parallel with large-scale psychiatric genomics studies, international consortia combining neuroimaging data are mapping the transdiagnostic brain signatures of mental illness on an unprecedented scale. This chapter discusses the major challenges, recent findings, and a roadmap for developing better neuroimaging-based tools and markers for mental illness.
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Affiliation(s)
- 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, USA
| | - Melody J Y Kang
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, 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, USA
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10
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Cheng J, Wang H, Wei S, Mei J, Liu F, Zhang G. Alzheimer's disease prediction algorithm based on de-correlation constraint and multi-modal feature interaction. Comput Biol Med 2024; 170:108000. [PMID: 38232453 DOI: 10.1016/j.compbiomed.2024.108000] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 12/25/2023] [Accepted: 01/13/2024] [Indexed: 01/19/2024]
Abstract
Alzheimer's disease (AD) is a neurodegenerative disease characterized by various pathological changes. Utilizing multimodal data from Fluorodeoxyglucose positron emission tomography(FDG-PET) and Magnetic Resonance Imaging(MRI) of the brain can offer comprehensive information about the lesions from different perspectives and improve the accuracy of prediction. However, there are significant differences in the feature space of multimodal data. Commonly, the simple concatenation of multimodal features can cause the model to struggle in distinguishing and utilizing the complementary information between different modalities, thus affecting the accuracy of predictions. Therefore, we propose an AD prediction model based on de-correlation constraint and multi-modal feature interaction. This model consists of the following three parts: (1) The feature extractor employs residual connections and attention mechanisms to capture distinctive lesion features from FDG-PET and MRI data within their respective modalities. (2) The de-correlation constraint function enhances the model's capacity to extract complementary information from different modalities by reducing the feature similarity between them. (3) The mutual attention feature fusion module interacts with the features within and between modalities to enhance the modal-specific features and adaptively adjust the weights of these features based on information from other modalities. The experimental results on ADNI database demonstrate that the proposed model achieves a prediction accuracy of 86.79% for AD, MCI and NC, which is higher than the existing multi-modal AD prediction models.
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Affiliation(s)
- Jiayuan Cheng
- Anhui Provincial International Joint Research Center for Advanced Technology in Medical Imaging, Anhui University, Hefei, China; School of Computer Science and Technology, Anhui University, Hefei, China
| | - Huabin Wang
- Anhui Provincial International Joint Research Center for Advanced Technology in Medical Imaging, Anhui University, Hefei, China; School of Computer Science and Technology, Anhui University, Hefei, China.
| | - Shicheng Wei
- School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, Australia
| | - Jiahao Mei
- Anhui Provincial International Joint Research Center for Advanced Technology in Medical Imaging, Anhui University, Hefei, China; School of Computer Science and Technology, Anhui University, Hefei, China
| | - Fei Liu
- School of Engineering, Monash University Malaysia, Kuala Lumpur, Malaysia
| | - Gong Zhang
- Anhui Provincial International Joint Research Center for Advanced Technology in Medical Imaging, Anhui University, Hefei, China; Hubei Key Laboratory of Intelligent Conveying Technology and Device, Hubei Polytechnic University, Huangshi, China
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11
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Parekh P, Badachhape AA, Tanifum EA, Annapragada AV, Ghaghada KB. Advances in nanoprobes for molecular MRI of Alzheimer's disease. WILEY INTERDISCIPLINARY REVIEWS. NANOMEDICINE AND NANOBIOTECHNOLOGY 2024; 16:e1946. [PMID: 38426638 PMCID: PMC10983770 DOI: 10.1002/wnan.1946] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 01/11/2024] [Accepted: 01/30/2024] [Indexed: 03/02/2024]
Abstract
Alzheimer's disease is the most common cause of dementia and a leading cause of mortality in the elderly population. Diagnosis of Alzheimer's disease has traditionally relied on evaluation of clinical symptoms for cognitive impairment with a definitive diagnosis requiring post-mortem demonstration of neuropathology. However, advances in disease pathogenesis have revealed that patients exhibit Alzheimer's disease pathology several decades before the manifestation of clinical symptoms. Magnetic resonance imaging (MRI) plays an important role in the management of patients with Alzheimer's disease. The clinical availability of molecular MRI (mMRI) contrast agents can revolutionize the diagnosis of Alzheimer's disease. In this article, we review advances in nanoparticle contrast agents, also referred to as nanoprobes, for mMRI of Alzheimer's disease. This article is categorized under: Diagnostic Tools > In Vivo Nanodiagnostics and Imaging Therapeutic Approaches and Drug Discovery > Nanomedicine for Neurological Disease.
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Affiliation(s)
- Parag Parekh
- Department of Radiology, Baylor College of Medicine, Houston, Texas 77030
- Department of Radiology, Texas Children's Hospital, Houston, Texas 77030
| | - Andrew A. Badachhape
- Department of Radiology, Baylor College of Medicine, Houston, Texas 77030
- Department of Radiology, Texas Children's Hospital, Houston, Texas 77030
| | - Eric A. Tanifum
- Department of Radiology, Baylor College of Medicine, Houston, Texas 77030
- Department of Radiology, Texas Children's Hospital, Houston, Texas 77030
| | - Ananth V. Annapragada
- Department of Radiology, Baylor College of Medicine, Houston, Texas 77030
- Department of Radiology, Texas Children's Hospital, Houston, Texas 77030
| | - Ketan B. Ghaghada
- Department of Radiology, Baylor College of Medicine, Houston, Texas 77030
- Department of Radiology, Texas Children's Hospital, Houston, Texas 77030
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Jia J, Ning Y, Chen M, Wang S, Yang H, Li F, Ding J, Li Y, Zhao B, Lyu J, Yang S, Yan X, Wang Y, Qin W, Wang Q, Li Y, Zhang J, Liang F, Liao Z, Wang S. Biomarker Changes during 20 Years Preceding Alzheimer's Disease. N Engl J Med 2024; 390:712-722. [PMID: 38381674 DOI: 10.1056/nejmoa2310168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/23/2024]
Abstract
BACKGROUND Biomarker changes that occur in the period between normal cognition and the diagnosis of sporadic Alzheimer's disease have not been extensively investigated in longitudinal studies. METHODS We conducted a multicenter, nested case-control study of Alzheimer's disease biomarkers in cognitively normal participants who were enrolled in the China Cognition and Aging Study from January 2000 through December 2020. A subgroup of these participants underwent testing of cerebrospinal fluid (CSF), cognitive assessments, and brain imaging at 2-year-to-3-year intervals. A total of 648 participants in whom Alzheimer's disease developed were matched with 648 participants who had normal cognition, and the temporal trajectories of CSF biochemical marker concentrations, cognitive testing, and imaging were analyzed in the two groups. RESULTS The median follow-up was 19.9 years (interquartile range, 19.5 to 20.2). CSF and imaging biomarkers in the Alzheimer's disease group diverged from those in the cognitively normal group at the following estimated number of years before diagnosis: amyloid-beta (Aβ)42, 18 years; the ratio of Aβ42 to Aβ40, 14 years; phosphorylated tau 181, 11 years; total tau, 10 years; neurofilament light chain, 9 years; hippocampal volume, 8 years; and cognitive decline, 6 years. As cognitive impairment progressed, the changes in CSF biomarker levels in the Alzheimer's disease group initially accelerated and then slowed. CONCLUSIONS In this study involving Chinese participants during the 20 years preceding clinical diagnosis of sporadic Alzheimer's disease, we observed the time courses of CSF biomarkers, the times before diagnosis at which they diverged from the biomarkers from a matched group of participants who remained cognitively normal, and the temporal order in which the biomarkers became abnormal. (Funded by the Key Project of the National Natural Science Foundation of China and others; ClinicalTrials.gov number, NCT03653156.).
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Affiliation(s)
- Jianping Jia
- From the Innovation Center for Neurological Disorders, Department of Neurology, Xuanwu Hospital (J.J., Y.N., M.C., Shuheng Wang, H.Y., F. Li, J.D., Yan Li, B.Z., W.Q., Q.W., Ying Li), Beijing Key Laboratory of Geriatric Cognitive Disorders, Clinical Center for Neurodegenerative Disease and Memory Impairment (J.J.), the Center of Alzheimer's Disease, Beijing Institute of Brain Disorders, Collaborative Innovation Center for Brain Disorders (J.J.), and the Department of Neurology, Beijing Anding Hospital (Y.W.), Capital Medical University, Key Laboratory of Neurodegenerative Diseases, Ministry of Education (J.J.), the Center for Cognitive Disorders, Beijing Geriatric Hospital (J.L.), and the Department of Neurology, Beijing Jishuitan Hospital (X.Y.), Beijing, the Department of Neurology, Daqing Oilfield General Hospital, Daqing (S.Y.), the Department of Neurology, the 960th Hospital of the People's Liberation Army, Jinan (J.Z.), the Department of Neurology, Baotou Central Hospital, Baotou (F. Liang), the Department of Psychiatry, Zhejiang Provincial People's Hospital, Hangzhou (Z.L.), and the Department of Neurology, Second Hospital of Hebei Medical University, Shijiazhuang (Shan Wang) - all in China
| | - Yuye Ning
- From the Innovation Center for Neurological Disorders, Department of Neurology, Xuanwu Hospital (J.J., Y.N., M.C., Shuheng Wang, H.Y., F. Li, J.D., Yan Li, B.Z., W.Q., Q.W., Ying Li), Beijing Key Laboratory of Geriatric Cognitive Disorders, Clinical Center for Neurodegenerative Disease and Memory Impairment (J.J.), the Center of Alzheimer's Disease, Beijing Institute of Brain Disorders, Collaborative Innovation Center for Brain Disorders (J.J.), and the Department of Neurology, Beijing Anding Hospital (Y.W.), Capital Medical University, Key Laboratory of Neurodegenerative Diseases, Ministry of Education (J.J.), the Center for Cognitive Disorders, Beijing Geriatric Hospital (J.L.), and the Department of Neurology, Beijing Jishuitan Hospital (X.Y.), Beijing, the Department of Neurology, Daqing Oilfield General Hospital, Daqing (S.Y.), the Department of Neurology, the 960th Hospital of the People's Liberation Army, Jinan (J.Z.), the Department of Neurology, Baotou Central Hospital, Baotou (F. Liang), the Department of Psychiatry, Zhejiang Provincial People's Hospital, Hangzhou (Z.L.), and the Department of Neurology, Second Hospital of Hebei Medical University, Shijiazhuang (Shan Wang) - all in China
| | - Meilin Chen
- From the Innovation Center for Neurological Disorders, Department of Neurology, Xuanwu Hospital (J.J., Y.N., M.C., Shuheng Wang, H.Y., F. Li, J.D., Yan Li, B.Z., W.Q., Q.W., Ying Li), Beijing Key Laboratory of Geriatric Cognitive Disorders, Clinical Center for Neurodegenerative Disease and Memory Impairment (J.J.), the Center of Alzheimer's Disease, Beijing Institute of Brain Disorders, Collaborative Innovation Center for Brain Disorders (J.J.), and the Department of Neurology, Beijing Anding Hospital (Y.W.), Capital Medical University, Key Laboratory of Neurodegenerative Diseases, Ministry of Education (J.J.), the Center for Cognitive Disorders, Beijing Geriatric Hospital (J.L.), and the Department of Neurology, Beijing Jishuitan Hospital (X.Y.), Beijing, the Department of Neurology, Daqing Oilfield General Hospital, Daqing (S.Y.), the Department of Neurology, the 960th Hospital of the People's Liberation Army, Jinan (J.Z.), the Department of Neurology, Baotou Central Hospital, Baotou (F. Liang), the Department of Psychiatry, Zhejiang Provincial People's Hospital, Hangzhou (Z.L.), and the Department of Neurology, Second Hospital of Hebei Medical University, Shijiazhuang (Shan Wang) - all in China
| | - Shuheng Wang
- From the Innovation Center for Neurological Disorders, Department of Neurology, Xuanwu Hospital (J.J., Y.N., M.C., Shuheng Wang, H.Y., F. Li, J.D., Yan Li, B.Z., W.Q., Q.W., Ying Li), Beijing Key Laboratory of Geriatric Cognitive Disorders, Clinical Center for Neurodegenerative Disease and Memory Impairment (J.J.), the Center of Alzheimer's Disease, Beijing Institute of Brain Disorders, Collaborative Innovation Center for Brain Disorders (J.J.), and the Department of Neurology, Beijing Anding Hospital (Y.W.), Capital Medical University, Key Laboratory of Neurodegenerative Diseases, Ministry of Education (J.J.), the Center for Cognitive Disorders, Beijing Geriatric Hospital (J.L.), and the Department of Neurology, Beijing Jishuitan Hospital (X.Y.), Beijing, the Department of Neurology, Daqing Oilfield General Hospital, Daqing (S.Y.), the Department of Neurology, the 960th Hospital of the People's Liberation Army, Jinan (J.Z.), the Department of Neurology, Baotou Central Hospital, Baotou (F. Liang), the Department of Psychiatry, Zhejiang Provincial People's Hospital, Hangzhou (Z.L.), and the Department of Neurology, Second Hospital of Hebei Medical University, Shijiazhuang (Shan Wang) - all in China
| | - Hao Yang
- From the Innovation Center for Neurological Disorders, Department of Neurology, Xuanwu Hospital (J.J., Y.N., M.C., Shuheng Wang, H.Y., F. Li, J.D., Yan Li, B.Z., W.Q., Q.W., Ying Li), Beijing Key Laboratory of Geriatric Cognitive Disorders, Clinical Center for Neurodegenerative Disease and Memory Impairment (J.J.), the Center of Alzheimer's Disease, Beijing Institute of Brain Disorders, Collaborative Innovation Center for Brain Disorders (J.J.), and the Department of Neurology, Beijing Anding Hospital (Y.W.), Capital Medical University, Key Laboratory of Neurodegenerative Diseases, Ministry of Education (J.J.), the Center for Cognitive Disorders, Beijing Geriatric Hospital (J.L.), and the Department of Neurology, Beijing Jishuitan Hospital (X.Y.), Beijing, the Department of Neurology, Daqing Oilfield General Hospital, Daqing (S.Y.), the Department of Neurology, the 960th Hospital of the People's Liberation Army, Jinan (J.Z.), the Department of Neurology, Baotou Central Hospital, Baotou (F. Liang), the Department of Psychiatry, Zhejiang Provincial People's Hospital, Hangzhou (Z.L.), and the Department of Neurology, Second Hospital of Hebei Medical University, Shijiazhuang (Shan Wang) - all in China
| | - Fangyu Li
- From the Innovation Center for Neurological Disorders, Department of Neurology, Xuanwu Hospital (J.J., Y.N., M.C., Shuheng Wang, H.Y., F. Li, J.D., Yan Li, B.Z., W.Q., Q.W., Ying Li), Beijing Key Laboratory of Geriatric Cognitive Disorders, Clinical Center for Neurodegenerative Disease and Memory Impairment (J.J.), the Center of Alzheimer's Disease, Beijing Institute of Brain Disorders, Collaborative Innovation Center for Brain Disorders (J.J.), and the Department of Neurology, Beijing Anding Hospital (Y.W.), Capital Medical University, Key Laboratory of Neurodegenerative Diseases, Ministry of Education (J.J.), the Center for Cognitive Disorders, Beijing Geriatric Hospital (J.L.), and the Department of Neurology, Beijing Jishuitan Hospital (X.Y.), Beijing, the Department of Neurology, Daqing Oilfield General Hospital, Daqing (S.Y.), the Department of Neurology, the 960th Hospital of the People's Liberation Army, Jinan (J.Z.), the Department of Neurology, Baotou Central Hospital, Baotou (F. Liang), the Department of Psychiatry, Zhejiang Provincial People's Hospital, Hangzhou (Z.L.), and the Department of Neurology, Second Hospital of Hebei Medical University, Shijiazhuang (Shan Wang) - all in China
| | - Jiayi Ding
- From the Innovation Center for Neurological Disorders, Department of Neurology, Xuanwu Hospital (J.J., Y.N., M.C., Shuheng Wang, H.Y., F. Li, J.D., Yan Li, B.Z., W.Q., Q.W., Ying Li), Beijing Key Laboratory of Geriatric Cognitive Disorders, Clinical Center for Neurodegenerative Disease and Memory Impairment (J.J.), the Center of Alzheimer's Disease, Beijing Institute of Brain Disorders, Collaborative Innovation Center for Brain Disorders (J.J.), and the Department of Neurology, Beijing Anding Hospital (Y.W.), Capital Medical University, Key Laboratory of Neurodegenerative Diseases, Ministry of Education (J.J.), the Center for Cognitive Disorders, Beijing Geriatric Hospital (J.L.), and the Department of Neurology, Beijing Jishuitan Hospital (X.Y.), Beijing, the Department of Neurology, Daqing Oilfield General Hospital, Daqing (S.Y.), the Department of Neurology, the 960th Hospital of the People's Liberation Army, Jinan (J.Z.), the Department of Neurology, Baotou Central Hospital, Baotou (F. Liang), the Department of Psychiatry, Zhejiang Provincial People's Hospital, Hangzhou (Z.L.), and the Department of Neurology, Second Hospital of Hebei Medical University, Shijiazhuang (Shan Wang) - all in China
| | - Yan Li
- From the Innovation Center for Neurological Disorders, Department of Neurology, Xuanwu Hospital (J.J., Y.N., M.C., Shuheng Wang, H.Y., F. Li, J.D., Yan Li, B.Z., W.Q., Q.W., Ying Li), Beijing Key Laboratory of Geriatric Cognitive Disorders, Clinical Center for Neurodegenerative Disease and Memory Impairment (J.J.), the Center of Alzheimer's Disease, Beijing Institute of Brain Disorders, Collaborative Innovation Center for Brain Disorders (J.J.), and the Department of Neurology, Beijing Anding Hospital (Y.W.), Capital Medical University, Key Laboratory of Neurodegenerative Diseases, Ministry of Education (J.J.), the Center for Cognitive Disorders, Beijing Geriatric Hospital (J.L.), and the Department of Neurology, Beijing Jishuitan Hospital (X.Y.), Beijing, the Department of Neurology, Daqing Oilfield General Hospital, Daqing (S.Y.), the Department of Neurology, the 960th Hospital of the People's Liberation Army, Jinan (J.Z.), the Department of Neurology, Baotou Central Hospital, Baotou (F. Liang), the Department of Psychiatry, Zhejiang Provincial People's Hospital, Hangzhou (Z.L.), and the Department of Neurology, Second Hospital of Hebei Medical University, Shijiazhuang (Shan Wang) - all in China
| | - Bote Zhao
- From the Innovation Center for Neurological Disorders, Department of Neurology, Xuanwu Hospital (J.J., Y.N., M.C., Shuheng Wang, H.Y., F. Li, J.D., Yan Li, B.Z., W.Q., Q.W., Ying Li), Beijing Key Laboratory of Geriatric Cognitive Disorders, Clinical Center for Neurodegenerative Disease and Memory Impairment (J.J.), the Center of Alzheimer's Disease, Beijing Institute of Brain Disorders, Collaborative Innovation Center for Brain Disorders (J.J.), and the Department of Neurology, Beijing Anding Hospital (Y.W.), Capital Medical University, Key Laboratory of Neurodegenerative Diseases, Ministry of Education (J.J.), the Center for Cognitive Disorders, Beijing Geriatric Hospital (J.L.), and the Department of Neurology, Beijing Jishuitan Hospital (X.Y.), Beijing, the Department of Neurology, Daqing Oilfield General Hospital, Daqing (S.Y.), the Department of Neurology, the 960th Hospital of the People's Liberation Army, Jinan (J.Z.), the Department of Neurology, Baotou Central Hospital, Baotou (F. Liang), the Department of Psychiatry, Zhejiang Provincial People's Hospital, Hangzhou (Z.L.), and the Department of Neurology, Second Hospital of Hebei Medical University, Shijiazhuang (Shan Wang) - all in China
| | - Jihui Lyu
- From the Innovation Center for Neurological Disorders, Department of Neurology, Xuanwu Hospital (J.J., Y.N., M.C., Shuheng Wang, H.Y., F. Li, J.D., Yan Li, B.Z., W.Q., Q.W., Ying Li), Beijing Key Laboratory of Geriatric Cognitive Disorders, Clinical Center for Neurodegenerative Disease and Memory Impairment (J.J.), the Center of Alzheimer's Disease, Beijing Institute of Brain Disorders, Collaborative Innovation Center for Brain Disorders (J.J.), and the Department of Neurology, Beijing Anding Hospital (Y.W.), Capital Medical University, Key Laboratory of Neurodegenerative Diseases, Ministry of Education (J.J.), the Center for Cognitive Disorders, Beijing Geriatric Hospital (J.L.), and the Department of Neurology, Beijing Jishuitan Hospital (X.Y.), Beijing, the Department of Neurology, Daqing Oilfield General Hospital, Daqing (S.Y.), the Department of Neurology, the 960th Hospital of the People's Liberation Army, Jinan (J.Z.), the Department of Neurology, Baotou Central Hospital, Baotou (F. Liang), the Department of Psychiatry, Zhejiang Provincial People's Hospital, Hangzhou (Z.L.), and the Department of Neurology, Second Hospital of Hebei Medical University, Shijiazhuang (Shan Wang) - all in China
| | - Shanshan Yang
- From the Innovation Center for Neurological Disorders, Department of Neurology, Xuanwu Hospital (J.J., Y.N., M.C., Shuheng Wang, H.Y., F. Li, J.D., Yan Li, B.Z., W.Q., Q.W., Ying Li), Beijing Key Laboratory of Geriatric Cognitive Disorders, Clinical Center for Neurodegenerative Disease and Memory Impairment (J.J.), the Center of Alzheimer's Disease, Beijing Institute of Brain Disorders, Collaborative Innovation Center for Brain Disorders (J.J.), and the Department of Neurology, Beijing Anding Hospital (Y.W.), Capital Medical University, Key Laboratory of Neurodegenerative Diseases, Ministry of Education (J.J.), the Center for Cognitive Disorders, Beijing Geriatric Hospital (J.L.), and the Department of Neurology, Beijing Jishuitan Hospital (X.Y.), Beijing, the Department of Neurology, Daqing Oilfield General Hospital, Daqing (S.Y.), the Department of Neurology, the 960th Hospital of the People's Liberation Army, Jinan (J.Z.), the Department of Neurology, Baotou Central Hospital, Baotou (F. Liang), the Department of Psychiatry, Zhejiang Provincial People's Hospital, Hangzhou (Z.L.), and the Department of Neurology, Second Hospital of Hebei Medical University, Shijiazhuang (Shan Wang) - all in China
| | - Xin Yan
- From the Innovation Center for Neurological Disorders, Department of Neurology, Xuanwu Hospital (J.J., Y.N., M.C., Shuheng Wang, H.Y., F. Li, J.D., Yan Li, B.Z., W.Q., Q.W., Ying Li), Beijing Key Laboratory of Geriatric Cognitive Disorders, Clinical Center for Neurodegenerative Disease and Memory Impairment (J.J.), the Center of Alzheimer's Disease, Beijing Institute of Brain Disorders, Collaborative Innovation Center for Brain Disorders (J.J.), and the Department of Neurology, Beijing Anding Hospital (Y.W.), Capital Medical University, Key Laboratory of Neurodegenerative Diseases, Ministry of Education (J.J.), the Center for Cognitive Disorders, Beijing Geriatric Hospital (J.L.), and the Department of Neurology, Beijing Jishuitan Hospital (X.Y.), Beijing, the Department of Neurology, Daqing Oilfield General Hospital, Daqing (S.Y.), the Department of Neurology, the 960th Hospital of the People's Liberation Army, Jinan (J.Z.), the Department of Neurology, Baotou Central Hospital, Baotou (F. Liang), the Department of Psychiatry, Zhejiang Provincial People's Hospital, Hangzhou (Z.L.), and the Department of Neurology, Second Hospital of Hebei Medical University, Shijiazhuang (Shan Wang) - all in China
| | - Yue Wang
- From the Innovation Center for Neurological Disorders, Department of Neurology, Xuanwu Hospital (J.J., Y.N., M.C., Shuheng Wang, H.Y., F. Li, J.D., Yan Li, B.Z., W.Q., Q.W., Ying Li), Beijing Key Laboratory of Geriatric Cognitive Disorders, Clinical Center for Neurodegenerative Disease and Memory Impairment (J.J.), the Center of Alzheimer's Disease, Beijing Institute of Brain Disorders, Collaborative Innovation Center for Brain Disorders (J.J.), and the Department of Neurology, Beijing Anding Hospital (Y.W.), Capital Medical University, Key Laboratory of Neurodegenerative Diseases, Ministry of Education (J.J.), the Center for Cognitive Disorders, Beijing Geriatric Hospital (J.L.), and the Department of Neurology, Beijing Jishuitan Hospital (X.Y.), Beijing, the Department of Neurology, Daqing Oilfield General Hospital, Daqing (S.Y.), the Department of Neurology, the 960th Hospital of the People's Liberation Army, Jinan (J.Z.), the Department of Neurology, Baotou Central Hospital, Baotou (F. Liang), the Department of Psychiatry, Zhejiang Provincial People's Hospital, Hangzhou (Z.L.), and the Department of Neurology, Second Hospital of Hebei Medical University, Shijiazhuang (Shan Wang) - all in China
| | - Wei Qin
- From the Innovation Center for Neurological Disorders, Department of Neurology, Xuanwu Hospital (J.J., Y.N., M.C., Shuheng Wang, H.Y., F. Li, J.D., Yan Li, B.Z., W.Q., Q.W., Ying Li), Beijing Key Laboratory of Geriatric Cognitive Disorders, Clinical Center for Neurodegenerative Disease and Memory Impairment (J.J.), the Center of Alzheimer's Disease, Beijing Institute of Brain Disorders, Collaborative Innovation Center for Brain Disorders (J.J.), and the Department of Neurology, Beijing Anding Hospital (Y.W.), Capital Medical University, Key Laboratory of Neurodegenerative Diseases, Ministry of Education (J.J.), the Center for Cognitive Disorders, Beijing Geriatric Hospital (J.L.), and the Department of Neurology, Beijing Jishuitan Hospital (X.Y.), Beijing, the Department of Neurology, Daqing Oilfield General Hospital, Daqing (S.Y.), the Department of Neurology, the 960th Hospital of the People's Liberation Army, Jinan (J.Z.), the Department of Neurology, Baotou Central Hospital, Baotou (F. Liang), the Department of Psychiatry, Zhejiang Provincial People's Hospital, Hangzhou (Z.L.), and the Department of Neurology, Second Hospital of Hebei Medical University, Shijiazhuang (Shan Wang) - all in China
| | - Qi Wang
- From the Innovation Center for Neurological Disorders, Department of Neurology, Xuanwu Hospital (J.J., Y.N., M.C., Shuheng Wang, H.Y., F. Li, J.D., Yan Li, B.Z., W.Q., Q.W., Ying Li), Beijing Key Laboratory of Geriatric Cognitive Disorders, Clinical Center for Neurodegenerative Disease and Memory Impairment (J.J.), the Center of Alzheimer's Disease, Beijing Institute of Brain Disorders, Collaborative Innovation Center for Brain Disorders (J.J.), and the Department of Neurology, Beijing Anding Hospital (Y.W.), Capital Medical University, Key Laboratory of Neurodegenerative Diseases, Ministry of Education (J.J.), the Center for Cognitive Disorders, Beijing Geriatric Hospital (J.L.), and the Department of Neurology, Beijing Jishuitan Hospital (X.Y.), Beijing, the Department of Neurology, Daqing Oilfield General Hospital, Daqing (S.Y.), the Department of Neurology, the 960th Hospital of the People's Liberation Army, Jinan (J.Z.), the Department of Neurology, Baotou Central Hospital, Baotou (F. Liang), the Department of Psychiatry, Zhejiang Provincial People's Hospital, Hangzhou (Z.L.), and the Department of Neurology, Second Hospital of Hebei Medical University, Shijiazhuang (Shan Wang) - all in China
| | - Ying Li
- From the Innovation Center for Neurological Disorders, Department of Neurology, Xuanwu Hospital (J.J., Y.N., M.C., Shuheng Wang, H.Y., F. Li, J.D., Yan Li, B.Z., W.Q., Q.W., Ying Li), Beijing Key Laboratory of Geriatric Cognitive Disorders, Clinical Center for Neurodegenerative Disease and Memory Impairment (J.J.), the Center of Alzheimer's Disease, Beijing Institute of Brain Disorders, Collaborative Innovation Center for Brain Disorders (J.J.), and the Department of Neurology, Beijing Anding Hospital (Y.W.), Capital Medical University, Key Laboratory of Neurodegenerative Diseases, Ministry of Education (J.J.), the Center for Cognitive Disorders, Beijing Geriatric Hospital (J.L.), and the Department of Neurology, Beijing Jishuitan Hospital (X.Y.), Beijing, the Department of Neurology, Daqing Oilfield General Hospital, Daqing (S.Y.), the Department of Neurology, the 960th Hospital of the People's Liberation Army, Jinan (J.Z.), the Department of Neurology, Baotou Central Hospital, Baotou (F. Liang), the Department of Psychiatry, Zhejiang Provincial People's Hospital, Hangzhou (Z.L.), and the Department of Neurology, Second Hospital of Hebei Medical University, Shijiazhuang (Shan Wang) - all in China
| | - Jintao Zhang
- From the Innovation Center for Neurological Disorders, Department of Neurology, Xuanwu Hospital (J.J., Y.N., M.C., Shuheng Wang, H.Y., F. Li, J.D., Yan Li, B.Z., W.Q., Q.W., Ying Li), Beijing Key Laboratory of Geriatric Cognitive Disorders, Clinical Center for Neurodegenerative Disease and Memory Impairment (J.J.), the Center of Alzheimer's Disease, Beijing Institute of Brain Disorders, Collaborative Innovation Center for Brain Disorders (J.J.), and the Department of Neurology, Beijing Anding Hospital (Y.W.), Capital Medical University, Key Laboratory of Neurodegenerative Diseases, Ministry of Education (J.J.), the Center for Cognitive Disorders, Beijing Geriatric Hospital (J.L.), and the Department of Neurology, Beijing Jishuitan Hospital (X.Y.), Beijing, the Department of Neurology, Daqing Oilfield General Hospital, Daqing (S.Y.), the Department of Neurology, the 960th Hospital of the People's Liberation Army, Jinan (J.Z.), the Department of Neurology, Baotou Central Hospital, Baotou (F. Liang), the Department of Psychiatry, Zhejiang Provincial People's Hospital, Hangzhou (Z.L.), and the Department of Neurology, Second Hospital of Hebei Medical University, Shijiazhuang (Shan Wang) - all in China
| | - Furu Liang
- From the Innovation Center for Neurological Disorders, Department of Neurology, Xuanwu Hospital (J.J., Y.N., M.C., Shuheng Wang, H.Y., F. Li, J.D., Yan Li, B.Z., W.Q., Q.W., Ying Li), Beijing Key Laboratory of Geriatric Cognitive Disorders, Clinical Center for Neurodegenerative Disease and Memory Impairment (J.J.), the Center of Alzheimer's Disease, Beijing Institute of Brain Disorders, Collaborative Innovation Center for Brain Disorders (J.J.), and the Department of Neurology, Beijing Anding Hospital (Y.W.), Capital Medical University, Key Laboratory of Neurodegenerative Diseases, Ministry of Education (J.J.), the Center for Cognitive Disorders, Beijing Geriatric Hospital (J.L.), and the Department of Neurology, Beijing Jishuitan Hospital (X.Y.), Beijing, the Department of Neurology, Daqing Oilfield General Hospital, Daqing (S.Y.), the Department of Neurology, the 960th Hospital of the People's Liberation Army, Jinan (J.Z.), the Department of Neurology, Baotou Central Hospital, Baotou (F. Liang), the Department of Psychiatry, Zhejiang Provincial People's Hospital, Hangzhou (Z.L.), and the Department of Neurology, Second Hospital of Hebei Medical University, Shijiazhuang (Shan Wang) - all in China
| | - Zhengluan Liao
- From the Innovation Center for Neurological Disorders, Department of Neurology, Xuanwu Hospital (J.J., Y.N., M.C., Shuheng Wang, H.Y., F. Li, J.D., Yan Li, B.Z., W.Q., Q.W., Ying Li), Beijing Key Laboratory of Geriatric Cognitive Disorders, Clinical Center for Neurodegenerative Disease and Memory Impairment (J.J.), the Center of Alzheimer's Disease, Beijing Institute of Brain Disorders, Collaborative Innovation Center for Brain Disorders (J.J.), and the Department of Neurology, Beijing Anding Hospital (Y.W.), Capital Medical University, Key Laboratory of Neurodegenerative Diseases, Ministry of Education (J.J.), the Center for Cognitive Disorders, Beijing Geriatric Hospital (J.L.), and the Department of Neurology, Beijing Jishuitan Hospital (X.Y.), Beijing, the Department of Neurology, Daqing Oilfield General Hospital, Daqing (S.Y.), the Department of Neurology, the 960th Hospital of the People's Liberation Army, Jinan (J.Z.), the Department of Neurology, Baotou Central Hospital, Baotou (F. Liang), the Department of Psychiatry, Zhejiang Provincial People's Hospital, Hangzhou (Z.L.), and the Department of Neurology, Second Hospital of Hebei Medical University, Shijiazhuang (Shan Wang) - all in China
| | - Shan Wang
- From the Innovation Center for Neurological Disorders, Department of Neurology, Xuanwu Hospital (J.J., Y.N., M.C., Shuheng Wang, H.Y., F. Li, J.D., Yan Li, B.Z., W.Q., Q.W., Ying Li), Beijing Key Laboratory of Geriatric Cognitive Disorders, Clinical Center for Neurodegenerative Disease and Memory Impairment (J.J.), the Center of Alzheimer's Disease, Beijing Institute of Brain Disorders, Collaborative Innovation Center for Brain Disorders (J.J.), and the Department of Neurology, Beijing Anding Hospital (Y.W.), Capital Medical University, Key Laboratory of Neurodegenerative Diseases, Ministry of Education (J.J.), the Center for Cognitive Disorders, Beijing Geriatric Hospital (J.L.), and the Department of Neurology, Beijing Jishuitan Hospital (X.Y.), Beijing, the Department of Neurology, Daqing Oilfield General Hospital, Daqing (S.Y.), the Department of Neurology, the 960th Hospital of the People's Liberation Army, Jinan (J.Z.), the Department of Neurology, Baotou Central Hospital, Baotou (F. Liang), the Department of Psychiatry, Zhejiang Provincial People's Hospital, Hangzhou (Z.L.), and the Department of Neurology, Second Hospital of Hebei Medical University, Shijiazhuang (Shan Wang) - all in China
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Juul-Madsen K, Parbo P, Ismail R, Ovesen PL, Schmidt V, Madsen LS, Thyrsted J, Gierl S, Breum M, Larsen A, Andersen MN, Romero-Ramos M, Holm CK, Andersen GR, Zhao H, Schuck P, Nygaard JV, Sutherland DS, Eskildsen SF, Willnow TE, Brooks DJ, Vorup-Jensen T. Amyloid-β aggregates activate peripheral monocytes in mild cognitive impairment. Nat Commun 2024; 15:1224. [PMID: 38336934 PMCID: PMC10858199 DOI: 10.1038/s41467-024-45627-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 01/31/2024] [Indexed: 02/12/2024] Open
Abstract
The peripheral immune system is important in neurodegenerative diseases, both in protecting and inflaming the brain, but the underlying mechanisms remain elusive. Alzheimer's Disease is commonly preceded by a prodromal period. Here, we report the presence of large Aβ aggregates in plasma from patients with mild cognitive impairment (n = 38). The aggregates are associated with low level Alzheimer's Disease-like brain pathology as observed by 11C-PiB PET and 18F-FTP PET and lowered CD18-rich monocytes. We characterize complement receptor 4 as a strong binder of amyloids and show Aβ aggregates are preferentially phagocytosed and stimulate lysosomal activity through this receptor in stem cell-derived microglia. KIM127 integrin activation in monocytes promotes size selective phagocytosis of Aβ. Hydrodynamic calculations suggest Aβ aggregates associate with vessel walls of the cortical capillaries. In turn, we hypothesize aggregates may provide an adhesion substrate for recruiting CD18-rich monocytes into the cortex. Our results support a role for complement receptor 4 in regulating amyloid homeostasis.
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Affiliation(s)
- Kristian Juul-Madsen
- Department of Biomedicine, Aarhus University, The Skou Building, Høegh-Guldbergs Gade 10, DK-8000, Aarhus C, Denmark
- Max-Delbrueck-Center for Molecular Medicine, Robert-Rössle-Str. 10, 13125, Berlin, Germany
| | - Peter Parbo
- Department of Nuclear Medicine, Odense University Hospital, J. B. Winsløws Vej 4, DK-5000, Odense C, Denmark
| | - Rola Ismail
- Department of Nuclear medicine and PET, Vejle Hospital, Beriderbakken 4, DK-7100, Vejle, Denmark
| | - Peter L Ovesen
- Max-Delbrueck-Center for Molecular Medicine, Robert-Rössle-Str. 10, 13125, Berlin, Germany
| | - Vanessa Schmidt
- Max-Delbrueck-Center for Molecular Medicine, Robert-Rössle-Str. 10, 13125, Berlin, Germany
| | - Lasse S Madsen
- Department of Nuclear Medicine & PET Centre, Aarhus University Hospital, Palle Juul-Jensens Boulevard 99, DK-8200, Aarhus N, Denmark
- Department of Clinical Medicine, Aarhus University, Palle Juul-Jensens Boulevard 11, DK-8200, Aarhus N, Denmark
- Center of Functionally Integrative Neuroscience, Aarhus University and Aarhus University Hospital, Building 1710, Universitetsbyen 3, DK-8200, Aarhus C, Denmark
| | - Jacob Thyrsted
- Department of Biomedicine, Aarhus University, The Skou Building, Høegh-Guldbergs Gade 10, DK-8000, Aarhus C, Denmark
| | - Sarah Gierl
- Department of Biomedicine, Aarhus University, The Skou Building, Høegh-Guldbergs Gade 10, DK-8000, Aarhus C, Denmark
| | - Mihaela Breum
- Department of Biomedicine, Aarhus University, The Skou Building, Høegh-Guldbergs Gade 10, DK-8000, Aarhus C, Denmark
| | - Agnete Larsen
- Department of Biomedicine, Aarhus University, The Skou Building, Høegh-Guldbergs Gade 10, DK-8000, Aarhus C, Denmark
| | - Morten N Andersen
- Department of Biomedicine, Aarhus University, The Skou Building, Høegh-Guldbergs Gade 10, DK-8000, Aarhus C, Denmark
- Department of Clinical Medicine, Aarhus University, Palle Juul-Jensens Boulevard 11, DK-8200, Aarhus N, Denmark
- Department of Hematology, Aarhus University Hospital, Palle Juul-Jensens Boulevard 99, DK-8200, Aarhus N, Denmark
| | - Marina Romero-Ramos
- Department of Biomedicine, Aarhus University, The Skou Building, Høegh-Guldbergs Gade 10, DK-8000, Aarhus C, Denmark
- NEURODIN AU IDEAS Center, Department of Biomedicine, Aarhus University, The Skou Building, Høegh-Guldbergs Gade 10, DK-8200, Aarhus C, Denmark
| | - Christian K Holm
- Department of Biomedicine, Aarhus University, The Skou Building, Høegh-Guldbergs Gade 10, DK-8000, Aarhus C, Denmark
| | - Gregers R Andersen
- Department of Molecular Biology and Genetics, Aarhus University, Universitetsbyen 81, DK-8000, Aarhus C, Denmark
| | - Huaying Zhao
- Laboratory of Dynamics and Macromolecular Assembly, National Institute of Biomedical Imaging and Bioengineering, Building 31, 9000 Rockville Pike, Bethesda, MD, 20892, USA
| | - Peter Schuck
- Laboratory of Dynamics and Macromolecular Assembly, National Institute of Biomedical Imaging and Bioengineering, Building 31, 9000 Rockville Pike, Bethesda, MD, 20892, USA
| | - Jens V Nygaard
- Department of Biological and Chemical Engineering, Aarhus University, Gustav Wieds vej 10 D, DK-8200, Aarhus C, Denmark
| | - Duncan S Sutherland
- Interdisiciplinary Nanoscience Center, Aarhus University, The iNANO House, Gustav Wieds Vej 14, DK-8200, Aarhus C, Denmark
- Center for Cellular Signal Patterns, Aarhus University, The iNANO House, Gustav Wieds Vej 14, DK-8200, Aarhus C, Denmark
| | - Simon F Eskildsen
- Department of Clinical Medicine, Aarhus University, Palle Juul-Jensens Boulevard 11, DK-8200, Aarhus N, Denmark
- Center of Functionally Integrative Neuroscience, Aarhus University and Aarhus University Hospital, Building 1710, Universitetsbyen 3, DK-8200, Aarhus C, Denmark
| | - Thomas E Willnow
- Department of Biomedicine, Aarhus University, The Skou Building, Høegh-Guldbergs Gade 10, DK-8000, Aarhus C, Denmark
- Max-Delbrueck-Center for Molecular Medicine, Robert-Rössle-Str. 10, 13125, Berlin, Germany
| | - David J Brooks
- Department of Nuclear Medicine & PET Centre, Aarhus University Hospital, Palle Juul-Jensens Boulevard 99, DK-8200, Aarhus N, Denmark
- Department of Brain Sciences, Imperial College London, Burlington Danes, The Hammersmith Hospital, Du Cane Road, London, W12 0NN, UK
- Institute of Translational and Clinical Research, University of Newcastle, Framlington Place, Newcastle upon Tyne, NE2 4HH, UK
| | - Thomas Vorup-Jensen
- Department of Biomedicine, Aarhus University, The Skou Building, Høegh-Guldbergs Gade 10, DK-8000, Aarhus C, Denmark.
- NEURODIN AU IDEAS Center, Department of Biomedicine, Aarhus University, The Skou Building, Høegh-Guldbergs Gade 10, DK-8200, Aarhus C, Denmark.
- Interdisiciplinary Nanoscience Center, Aarhus University, The iNANO House, Gustav Wieds Vej 14, DK-8200, Aarhus C, Denmark.
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Zimmer L. Recent applications of positron emission tomographic (PET) imaging in psychiatric drug discovery. Expert Opin Drug Discov 2024; 19:161-172. [PMID: 37948046 DOI: 10.1080/17460441.2023.2278635] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 10/30/2023] [Indexed: 11/12/2023]
Abstract
INTRODUCTION Psychiatry is one of the medical disciplines that suffers most from a lack of innovation in its therapeutic arsenal. Many failures in drug candidate trials can be explained by pharmacological properties that have been poorly assessed upstream, in terms of brain passage, brain target binding and clinical outcomes. Positron emission tomography can provide pharmacokinetic and pharmacodynamic data to help select candidate-molecules for further clinical trials. AREAS COVERED This review aims to explain and discuss the various methods using positron-emitting radiolabeled molecules to trace the cerebral distribution of the drug-candidate or indirectly measure binding to its therapeutic target. More than an exhaustive review of PET studies in psychopharmacology, this article highlights the contributions this technology can make in drug discovery applied to psychiatry. EXPERT OPINION PET neuroimaging is the only technological approach that can, in vivo in humans, measure cerebral delivery of a drug candidate, percentage and duration of target binding, and even the pharmacological effects. PET studies in a small number of subjects in the early stages of the development of a psychotropic drug can therefore provide the pharmacokinetic/pharmacodynamic data required for subsequent clinical evaluation. While PET technology is demanding in terms of radiochemical, radiopharmacological and nuclear medicine expertise, its integration into the development process of new drugs for psychiatry has great added value.
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Affiliation(s)
- Luc Zimmer
- Lyon Neuroscience Research Center, Université Claude Bernard, Lyon, France
- CERMEP, Hospices Civils de Lyon, Lyon, France
- Institut National des Sciences et Technologies Nucléaire, Saclay, France
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15
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Rudisch DM, Krasko MN, Barnett DGS, Mueller KD, Russell JA, Connor NP, Ciucci MR. Early ultrasonic vocalization deficits and related thyroarytenoid muscle pathology in the transgenic TgF344-AD rat model of Alzheimer's disease. Front Behav Neurosci 2024; 17:1294648. [PMID: 38322496 PMCID: PMC10844490 DOI: 10.3389/fnbeh.2023.1294648] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Accepted: 12/01/2023] [Indexed: 02/08/2024] Open
Abstract
Background Alzheimer's disease (AD) is a progressive neurologic disease and the most common cause of dementia. Classic pathology in AD is characterized by inflammation, abnormal presence of tau protein, and aggregation of β-amyloid that disrupt normal neuronal function and lead to cell death. Deficits in communication also occur during disease progression and significantly reduce health, well-being, and quality of life. Because clinical diagnosis occurs in the mid-stage of the disease, characterizing the prodrome and early stages in humans is currently challenging. To overcome these challenges, we use the validated TgF344-AD (F344-Tg(Prp-APP, Prp-PS1)19/Rrrc) transgenic rat model that manifests cognitive, behavioral, and neuropathological dysfunction akin to AD in humans. Objectives The overarching goal of our work is to test the central hypothesis that pathology and related behavioral deficits such as communication dysfunction in part manifest in the peripheral nervous system and corresponding target tissues already in the early stages. The primary aims of this study are to test the hypotheses that: (1) changes in ultrasonic vocalizations (USV) occur in the prodromal stage at 6 months of age and worsen at 9 months of age, (2) inflammation as well as AD-related pathology can be found in the thyroarytenoid muscle (TA) at 12 months of age (experimental endpoint tissue harvest), and to (3) demonstrate that the TgF344-AD rat model is an appropriate model for preclinical investigations of early AD-related vocal deficits. Methods USVs were collected from male TgF344-AD (N = 19) and wildtype (WT) Fischer-344 rats (N = 19) at 6 months (N = 38; WT: n = 19; TgF344-AD: n = 19) and 9 months of age (N = 18; WT: n = 10; TgF344-AD: n = 8) and acoustically analyzed for duration, mean power, principal frequency, low frequency, high frequency, peak frequency, and call type. RT-qPCR was used to assay peripheral inflammation and AD-related pathology via gene expressions in the TA muscle of male TgF344-AD rats (n = 6) and WT rats (n = 6) at 12 months of age. Results This study revealed a significant reduction in mean power of ultrasonic calls from 6 to 9 months of age and increased peak frequency levels over time in TgF344-AD rats compared to WT controls. Additionally, significant downregulation of AD-related genes Uqcrc2, Bace2, Serpina3n, and Igf2, as well as downregulation of pro-inflammatory gene Myd88 was found in the TA muscle of TgF344-AD rats at 12 months of age. Discussion Our findings demonstrate early and progressive vocal deficits in the TgF344-AD rat model. We further provide evidence of dysregulation of AD-pathology-related genes as well as inflammatory genes in the TA muscles of TgF344-AD rats in the early stage of the disease, confirming this rat model for early-stage investigations of voice deficits and related pathology.
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Affiliation(s)
- Denis Michael Rudisch
- Department of Communication Sciences and Disorders, University of Wisconsin-Madison, Madison, WI, United States
- Department of Surgery, Division of Otolaryngology - Head and Neck Surgery, UW School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, United States
- UW Institute for Clinical and Translational Research, UW School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, United States
| | - Maryann N Krasko
- Department of Communication Sciences and Disorders, University of Wisconsin-Madison, Madison, WI, United States
- Department of Surgery, Division of Otolaryngology - Head and Neck Surgery, UW School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, United States
| | - David G S Barnett
- Department of Surgery, Division of Otolaryngology - Head and Neck Surgery, UW School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, United States
| | - Kimberly D Mueller
- Department of Communication Sciences and Disorders, University of Wisconsin-Madison, Madison, WI, United States
- Wisconsin Alzheimer's Disease Research Center, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, United States
| | - John A Russell
- Department of Surgery, Division of Otolaryngology - Head and Neck Surgery, UW School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, United States
| | - Nadine P Connor
- Department of Communication Sciences and Disorders, University of Wisconsin-Madison, Madison, WI, United States
- Department of Surgery, Division of Otolaryngology - Head and Neck Surgery, UW School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, United States
| | - Michelle R Ciucci
- Department of Communication Sciences and Disorders, University of Wisconsin-Madison, Madison, WI, United States
- Department of Surgery, Division of Otolaryngology - Head and Neck Surgery, UW School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, United States
- Neuroscience Training Program, University of Wisconsin-Madison, Madison, WI, United States
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Bermejo-Pareja F, del Ser T. Controversial Past, Splendid Present, Unpredictable Future: A Brief Review of Alzheimer Disease History. J Clin Med 2024; 13:536. [PMID: 38256670 PMCID: PMC10816332 DOI: 10.3390/jcm13020536] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 12/29/2023] [Accepted: 01/04/2024] [Indexed: 01/24/2024] Open
Abstract
Background: The concept of Alzheimer disease (AD)-since its histological discovery by Alzheimer to the present day-has undergone substantial modifications. Methods: We conducted a classical narrative review of this field with a bibliography selection (giving preference to Medline best match). Results: The following subjects are reviewed and discussed: Alzheimer's discovery, Kraepelin's creation of a new disease that was a rare condition until the 1970's, the growing interest and investment in AD as a major killer in a society with a large elderly population in the second half of the 20th century, the consolidation of the AD clinicopathological model, and the modern AD nosology based on the dominant amyloid hypothesis among many others. In the 21st century, the development of AD biomarkers has supported a novel biological definition of AD, although the proposed therapies have failed to cure this disease. The incidence of dementia/AD has shown a decrease in affluent countries (possibly due to control of risk factors), and mixed dementia has been established as the most frequent etiology in the oldest old. Conclusions: The current concept of AD lacks unanimity. Many hypotheses attempt to explain its complex physiopathology entwined with aging, and the dominant amyloid cascade has yielded poor therapeutic results. The reduction in the incidence of dementia/AD appears promising but it should be confirmed in the future. A reevaluation of the AD concept is also necessary.
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Affiliation(s)
- Félix Bermejo-Pareja
- CIBERNED, Institute of Health Carlos III, 28029 Madrid, Spain
- Institute of Research i+12, University Hospital “12 de Octubre”, 28041 Madrid, Spain
| | - Teodoro del Ser
- Alzheimer’s Centre Reina Sofia—CIEN Foundation, Institute of Health Carlos III, 28031 Madrid, Spain;
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Dai Y, Hsu YC, Fernandes BS, Zhang K, Li X, Enduru N, Liu A, Manuel AM, Jiang X, Zhao Z. Disentangling Accelerated Cognitive Decline from the Normal Aging Process and Unraveling Its Genetic Components: A Neuroimaging-Based Deep Learning Approach. J Alzheimers Dis 2024; 97:1807-1827. [PMID: 38306043 DOI: 10.3233/jad-231020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2024]
Abstract
Background The progressive cognitive decline, an integral component of Alzheimer's disease (AD), unfolds in tandem with the natural aging process. Neuroimaging features have demonstrated the capacity to distinguish cognitive decline changes stemming from typical brain aging and AD between different chronological points. Objective To disentangle the normal aging effect from the AD-related accelerated cognitive decline and unravel its genetic components using a neuroimaging-based deep learning approach. Methods We developed a deep-learning framework based on a dual-loss Siamese ResNet network to extract fine-grained information from the longitudinal structural magnetic resonance imaging (MRI) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. We then conducted genome-wide association studies (GWAS) and post-GWAS analyses to reveal the genetic basis of AD-related accelerated cognitive decline. Results We used our model to process data from 1,313 individuals, training it on 414 cognitively normal people and predicting cognitive assessment for all participants. In our analysis of accelerated cognitive decline GWAS, we identified two genome-wide significant loci: APOE locus (chromosome 19 p13.32) and rs144614292 (chromosome 11 p15.1). Variant rs144614292 (G > T) has not been reported in previous AD GWA studies. It is within the intronic region of NELL1, which is expressed in neurons and plays a role in controlling cell growth and differentiation. The cell-type-specific enrichment analysis and functional enrichment of GWAS signals highlighted the microglia and immune-response pathways. Conclusions Our deep learning model effectively extracted relevant neuroimaging features and predicted individual cognitive decline. We reported a novel variant (rs144614292) within the NELL1 gene.
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Affiliation(s)
- Yulin Dai
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Yu-Chun Hsu
- Center for Secure Artificial Intelligence for Healthcare, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Brisa S Fernandes
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Kai Zhang
- Center for Secure Artificial Intelligence for Healthcare, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Xiaoyang Li
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Department of Biostatistics and Data Science, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Nitesh Enduru
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Andi Liu
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Astrid M Manuel
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Xiaoqian Jiang
- Center for Secure Artificial Intelligence for Healthcare, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Zhongming Zhao
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
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18
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Paul SM, Potter WZ. Finding new and better treatments for psychiatric disorders. Neuropsychopharmacology 2024; 49:3-9. [PMID: 37582978 PMCID: PMC10700311 DOI: 10.1038/s41386-023-01690-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/28/2023] [Revised: 07/07/2023] [Accepted: 07/24/2023] [Indexed: 08/17/2023]
Abstract
In contrast to most fields of medicine, progress to discover and develop new and improved psychiatric drugs has been slow and disappointing. The vast majority of currently prescribed drugs to treat schizophrenia, mood and anxiety disorders are arguably no more effective than the first generation of psychiatric drugs introduced well over 50 years ago. With only a few exceptions current psychiatric drugs work via the same fundamental mechanisms of action as first-generation agents. Here we describe the reasons for this slow progress and outline a number of areas of research that involve a greater reliance on experimental therapeutics utilizing recent advances in neuroscience to better understand disease biology. We exemplify the potential impact of these areas of research focus with several recent examples of novel agents that have emerged and which support our optimism that newer, more effective and better tolerated agents, are on the horizon. Together with existing drugs these newer agents and novel mechanisms could offer markedly improved functional outcomes for the millions of people still disabled by psychiatric disorders.
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Affiliation(s)
- Steven M Paul
- Karuna Therapeutics, Washington University School of Medicine, St. Louis, MO, USA.
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19
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Zhang QZ, Yilihamu N, Li YB, Li XH, Qin YD. Simple Synthesis of [ 18F] AV-45 and its Clinical Application in the Diagnosis of Alzheimer's Disease. Curr Med Chem 2024; 31:1278-1288. [PMID: 37526186 DOI: 10.2174/0929867331666230731123226] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 06/15/2023] [Accepted: 06/19/2023] [Indexed: 08/02/2023]
Abstract
OBJECTIVE [18F] AV-45 can be produced in a simple, stable, and repeatable manner on the Tracerlab FXF-N platform using a self-editing synthetic procedure and solid-phase extraction purification method. This technique is applied to positron emission tomography (PET) imaging of Alzheimer's disease (AD) to observe its distribution and characteristics in various brain regions and its diagnostic efficiency for the disease. METHODS The precursor was subjected to nucleophilic radiofluorination at 120°C in anhydrous dimethyl sulfoxide, followed by acid hydrolysis of the protecting groups. The neutralized reaction mixture was purified by solid phase extraction to obtain a relatively pure [18F] AV-45 product with a high specific activity. A total of 10 participants who were diagnosed with Alzheimer's disease (AD group) and 10 healthy controls (HC group) were included retrospectively. All of them underwent [18F] AV-45 brain PET/CT imaging. The distribution of [18F] AV-45 in the AD group was analyzed visually and semi-quantitatively. RESULTS Six consecutive radiochemical syntheses were performed in this experiment. The average production time of [18F] AV-45 was 52 minutes, the radiochemical yield was 14.2 % ± 2.7% (n = 6), and the radiochemical purity was greater than 95%. When used with PET/CT imaging, the results of the visual analysis indicated increased [18F] AV-45 radioactivity uptake in the frontal, temporal, and parietal lobes in AD patients. Semiquantitative analysis showed the highest diagnostic efficacy in the posterior cingulate gyrus compared with other brain regions (P < 0.001). CONCLUSION Intravenous [18F] AV-45 was successfully prepared on the Tracerlab FXF-N platform by solid-phase extraction of crude product and automated radiochemical synthesis. PET/CT imaging can be used to diagnose and evaluate AD patients and provide a more robust basis for clinicians to diagnose AD.
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Affiliation(s)
- Qi-Zhou Zhang
- Department of Nuclear Medicine, The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830011, Xinjiang, China
| | - Nazi Yilihamu
- Department of Nuclear Medicine, The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830011, Xinjiang, China
| | - Yu-Bin Li
- Department of Nuclear Medicine, The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830011, Xinjiang, China
| | - Xiao-Hong Li
- Department of Nuclear Medicine, The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830011, Xinjiang, China
| | - Yong-De Qin
- Department of Nuclear Medicine, The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830011, Xinjiang, China
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Alber J, Bouwman F, den Haan J, Rissman RA, De Groef L, Koronyo‐Hamaoui M, Lengyel I, Thal DR. Retina pathology as a target for biomarkers for Alzheimer's disease: Current status, ophthalmopathological background, challenges, and future directions. Alzheimers Dement 2024; 20:728-740. [PMID: 37917365 PMCID: PMC10917008 DOI: 10.1002/alz.13529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 09/30/2023] [Accepted: 10/05/2023] [Indexed: 11/04/2023]
Abstract
There is emerging evidence that amyloid beta protein (Aβ) and tau-related lesions in the retina are associated with Alzheimer's disease (AD). Aβ and hyperphosphorylated (p)-tau deposits have been described in the retina and were associated with small amyloid spots visualized by in vivo imaging techniques as well as degeneration of the retina. These changes correlate with brain amyloid deposition as determined by histological quantification, positron emission tomography (PET) or clinical diagnosis of AD. However, the literature is not coherent on these histopathological and in vivo imaging findings. One important reason for this is the variability in the methods and the interpretation of findings across different studies. In this perspective, we indicate the critical methodological deviations among different groups and suggest a roadmap moving forward on how to harmonize (i) histopathologic examination of retinal tissue; (ii) in vivo imaging among different methods, devices, and interpretation algorithms; and (iii) inclusion/exclusion criteria for studies aiming at retinal biomarker validation.
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Affiliation(s)
- Jessica Alber
- George and Anne Ryan Institute for Neuroscience, Department of Biomedical and Pharmaceutical SciencesUniversity of Rhode IslandKingstonRhode IslandUSA
- Butler Hospital Memory & Aging ProgramProvidenceRhode IslandUSA
| | - Femke Bouwman
- Amsterdam UMC, location VUmcAlzheimer Center, Department of NeurologyAmsterdamThe Netherlands
| | - Jurre den Haan
- Amsterdam UMC, location VUmcAlzheimer Center, Department of NeurologyAmsterdamThe Netherlands
| | - Robert A. Rissman
- Alzheimer's Therapeutic Research InstituteKeck School of Medicine of the University of Southern CaliforniaSan DiegoCaliforniaUSA
| | - Lies De Groef
- Cellular Communication and Neurodegeneration Research Group, Animal Physiology and Neurobiology Division, Department of BiologyLeuven Brain InstituteKU LeuvenLeuvenBelgium
| | - Maya Koronyo‐Hamaoui
- Departments of Neurosurgery, Neurology, and Biomedical SciencesMaxine Dunitz Neurosurgical Research Institute, Cedars‐Sinai Medical CenterLos AngelesCaliforniaUSA
| | - Imre Lengyel
- The Wellcome‐Wolfson Institute for Experimental Medicine, School of Medicine, Dentistry and Biomedical ScienceQueen's University BelfastBelfastUK
| | - Dietmar Rudolf Thal
- Laboratory of NeuropathologyDepartment of Imaging and Pathology, and Leuven Brain Institute, KU LeuvenLeuvenBelgium
- Department of PathologyUZ LeuvenLeuvenBelgium
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Veitch DP, Weiner MW, Miller M, Aisen PS, Ashford MA, Beckett LA, Green RC, Harvey D, Jack CR, Jagust W, Landau SM, Morris JC, Nho KT, Nosheny R, Okonkwo O, Perrin RJ, Petersen RC, Rivera Mindt M, Saykin A, Shaw LM, Toga AW, Tosun D. The Alzheimer's Disease Neuroimaging Initiative in the era of Alzheimer's disease treatment: A review of ADNI studies from 2021 to 2022. Alzheimers Dement 2024; 20:652-694. [PMID: 37698424 PMCID: PMC10841343 DOI: 10.1002/alz.13449] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 07/27/2023] [Accepted: 08/01/2023] [Indexed: 09/13/2023]
Abstract
The Alzheimer's Disease Neuroimaging Initiative (ADNI) aims to improve Alzheimer's disease (AD) clinical trials. Since 2006, ADNI has shared clinical, neuroimaging, and cognitive data, and biofluid samples. We used conventional search methods to identify 1459 publications from 2021 to 2022 using ADNI data/samples and reviewed 291 impactful studies. This review details how ADNI studies improved disease progression understanding and clinical trial efficiency. Advances in subject selection, detection of treatment effects, harmonization, and modeling improved clinical trials and plasma biomarkers like phosphorylated tau showed promise for clinical use. Biomarkers of amyloid beta, tau, neurodegeneration, inflammation, and others were prognostic with individualized prediction algorithms available online. Studies supported the amyloid cascade, emphasized the importance of neuroinflammation, and detailed widespread heterogeneity in disease, linked to genetic and vascular risk, co-pathologies, sex, and resilience. Biological subtypes were consistently observed. Generalizability of ADNI results is limited by lack of cohort diversity, an issue ADNI-4 aims to address by enrolling a diverse cohort.
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Affiliation(s)
- Dallas P. Veitch
- Department of Veterans Affairs Medical CenterNorthern California Institute for Research and Education (NCIRE)San FranciscoCaliforniaUSA
- Department of Veterans Affairs Medical CenterCenter for Imaging of Neurodegenerative DiseasesSan FranciscoCaliforniaUSA
| | - Michael W. Weiner
- Department of Veterans Affairs Medical CenterCenter for Imaging of Neurodegenerative DiseasesSan FranciscoCaliforniaUSA
- Department of Radiology and Biomedical ImagingUniversity of CaliforniaSan FranciscoCaliforniaUSA
- Department of MedicineUniversity of CaliforniaSan FranciscoCaliforniaUSA
- Department of Psychiatry and Behavioral SciencesUniversity of CaliforniaSan FranciscoCaliforniaUSA
- Department of NeurologyUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - Melanie Miller
- Department of Veterans Affairs Medical CenterNorthern California Institute for Research and Education (NCIRE)San FranciscoCaliforniaUSA
- Department of Veterans Affairs Medical CenterCenter for Imaging of Neurodegenerative DiseasesSan FranciscoCaliforniaUSA
| | - Paul S. Aisen
- Alzheimer's Therapeutic Research InstituteUniversity of Southern CaliforniaSan DiegoCaliforniaUSA
| | - Miriam A. Ashford
- Department of Veterans Affairs Medical CenterNorthern California Institute for Research and Education (NCIRE)San FranciscoCaliforniaUSA
| | - Laurel A. Beckett
- Division of BiostatisticsDepartment of Public Health SciencesUniversity of CaliforniaDavisCaliforniaUSA
| | - Robert C. Green
- Division of GeneticsDepartment of MedicineBrigham and Women's HospitalBroad Institute Ariadne Labs and Harvard Medical SchoolBostonMassachusettsUSA
| | - Danielle Harvey
- Division of BiostatisticsDepartment of Public Health SciencesUniversity of CaliforniaDavisCaliforniaUSA
| | | | - William Jagust
- Helen Wills Neuroscience InstituteUniversity of California BerkeleyBerkeleyCaliforniaUSA
| | - Susan M. Landau
- Helen Wills Neuroscience InstituteUniversity of California BerkeleyBerkeleyCaliforniaUSA
| | - John C. Morris
- Knight Alzheimer's Disease Research CenterWashington University School of MedicineSaint LouisMissouriUSA
- Department of NeurologyWashington University School of MedicineSaint LouisMissouriUSA
- Department of Pathology and ImmunologyWashington University School of MedicineSaint LouisMissouriUSA
| | - Kwangsik T. Nho
- Department of Radiology and Imaging Sciences and the Indiana Alzheimer's Disease Research CenterIndiana University School of MedicineIndianapolisIndianaUSA
- Center for Computational Biology and BioinformaticsIndiana University School of MedicineIndianapolisIndianaUSA
| | - Rachel Nosheny
- Department of Veterans Affairs Medical CenterCenter for Imaging of Neurodegenerative DiseasesSan FranciscoCaliforniaUSA
- Department of Psychiatry and Behavioral SciencesUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - Ozioma Okonkwo
- Wisconsin Alzheimer's Disease Research Center and Department of MedicineUniversity of Wisconsin School of Medicine and Public HealthMadisonWisconsinUSA
| | - Richard J. Perrin
- Knight Alzheimer's Disease Research CenterWashington University School of MedicineSaint LouisMissouriUSA
- Department of NeurologyWashington University School of MedicineSaint LouisMissouriUSA
- Department of Pathology and ImmunologyWashington University School of MedicineSaint LouisMissouriUSA
| | | | - Monica Rivera Mindt
- Department of PsychologyLatin American and Latino Studies InstituteAfrican and African American StudiesFordham UniversityNew YorkNew YorkUSA
- Department of NeurologyIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Andrew Saykin
- Department of Radiology and Imaging Sciences and the Indiana Alzheimer's Disease Research CenterIndiana University School of MedicineIndianapolisIndianaUSA
- Department of Medical and Molecular GeneticsIndiana University School of MedicineIndianapolisIndianaUSA
| | - Leslie M. Shaw
- Department of Pathology and Laboratory Medicine and the PENN Alzheimer's Disease Research CenterCenter for Neurodegenerative ResearchPerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Arthur W. Toga
- Laboratory of Neuro ImagingInstitute of Neuroimaging and InformaticsKeck School of Medicine of University of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Duygu Tosun
- Department of Veterans Affairs Medical CenterCenter for Imaging of Neurodegenerative DiseasesSan FranciscoCaliforniaUSA
- Department of Radiology and Biomedical ImagingUniversity of CaliforniaSan FranciscoCaliforniaUSA
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22
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Erickson CM, Karlawish J, Grill JD, Harkins K, Landau SM, Rivera-Mindt MG, Okonkwo O, Petersen RC, Aisen PS, Weiner MW, Largent EA. A Pragmatic, Investigator-Driven Process for Disclosure of Amyloid PET Scan Results to ADNI-4 Research Participants. J Prev Alzheimers Dis 2024; 11:294-302. [PMID: 38374735 PMCID: PMC10883638 DOI: 10.14283/jpad.2024.33] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/21/2024]
Abstract
BACKGROUND Prior studies of Alzheimer's disease (AD) biomarker disclosure have answered important questions about individuals' safety after learning and comprehending their amyloid PET results; however, these studies have typically employed highly structured disclosure protocols and focused on the psychological impact of disclosure (e.g., anxiety, depression, and suicidality) in homogeneous populations. More work is needed to develop flexible disclosure protocols and study outcomes in ethnoculturally representative samples. METHODS The Alzheimer's Disease Neuroimaging Initiative (ADNI) is formally incorporating amyloid PET disclosure into the newest protocol (ADNI-4). Participants across the cognitive spectrum who wish to know their amyloid PET results may learn them. The pragmatic disclosure process spans four timepoints: (1) a pre-disclosure visit, (2) the PET scan and its read, (3) a disclosure visit, and (4) a post-disclosure check-in. This process applies to all participants, with slight modifications to account for their cognitive status. In designing this process, special emphasis was placed on utilizing investigator discretion. Participant measures include perceived risk of dementia, purpose in life, and disclosure satisfaction. Investigator assessment of the disclosure visit (e.g., challenges encountered, topics discussed, etc.) is also included. RESULTS Data collection is ongoing. Results will allow for more robust characterization of the impact of learning amyloid PET results on individuals and describe the perspectives of investigators. CONCLUSION The pragmatic design of the disclosure process in ADNI-4 coupled with the novel participant and investigator data will inform future disclosure practices. This is especially important as disclosure of biomarker results expands in research and care.
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Affiliation(s)
- C M Erickson
- Emily Largent JD, PhD, RN, 423 Guardian Drive Philadelphia, PA 19104, USA,
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23
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Lang L, Wang Y. Markov model combined with MR diffusion tensor imaging for predicting the onset of Alzheimer's disease. Open Life Sci 2023; 18:20220714. [PMID: 37954101 PMCID: PMC10638840 DOI: 10.1515/biol-2022-0714] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 07/26/2023] [Accepted: 08/08/2023] [Indexed: 11/14/2023] Open
Abstract
Alzheimer's disease (AD) affects cognition, behavior, and memory of brain. It causes 60-80% of dementia cases. Cross-sectional imaging investigations of AD show that magnetic resonance (MR) with diffusion tensor image (DTI)-detected lesion locations in AD patients are heterogeneous and distributed across the imaging area. This study suggested that Markov model (MM) combined with MR-DTI (MM + MR-DTI) was offered as a method for predicting the onset of AD. In 120 subjects (normal controls [NCs], amnestic mild cognitive impairment [aMCI] patients, and AD patients) from a discovery dataset and 122 subjects (NCs, aMCI, and AD) from a replicated dataset, we used them to evaluate the white matter (WM) integrity and abnormalities. We did this by using automated fiber quantification, which allowed us to identify 20 central WM tracts. Point-wise alterations in WM tracts were shown using discovery and replication datasets. The statistical analysis revealed a substantial correlation between microstructural WM alterations and output in the patient groups and cognitive performance, suggesting that this may be a potential biomarker for AD. The MR-based classifier demonstrated the following performance levels for the basis classifiers, with DTI achieving the lowest performance. The following outcomes were seen in MM + MR-DTI using multimodal techniques when combining two modalities. Finally, a combination of every imaging method produced results with an accuracy of 98%, a specificity of 97%, and a sensitivity of 99%. In summary, DTI performs better when paired with structural MR, despite its relatively weak performance when used alone. These findings support the idea that WM modifications play a significant role in AD.
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Affiliation(s)
- Lili Lang
- Basic Medical College, Changzhi Medical College, Changzhi, Shanxi, 046000, China
| | - Ying Wang
- Endoscopic Chamber, Muling Town Forest District Hospital, Mudanjiang, Heilongjiang, 157513, China
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24
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Krainc D, Martin WJ, Casey B, Jensen FE, Tishkoff S, Potter WZ, Hyman SE. Shifting the trajectory of therapeutic development for neurological and psychiatric disorders. Sci Transl Med 2023; 15:eadg4775. [PMID: 38190501 DOI: 10.1126/scitranslmed.adg4775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Accepted: 10/13/2023] [Indexed: 01/10/2024]
Abstract
Clinical trials for central nervous system disorders often enroll patients with unrecognized heterogeneous diseases, leading to costly trials that have high failure rates. Here, we discuss the potential of emerging technologies and datasets to elucidate disease mechanisms and identify biomarkers to improve patient stratification and monitoring of disease progression in clinical trials for neuropsychiatric disorders. Greater efforts must be centered on rigorously standardizing data collection and sharing of methods, datasets, and analytical tools across sectors. To address health care disparities in clinical trials, diversity of genetic ancestries and environmental exposures of research participants and associated biological samples must be prioritized.
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Affiliation(s)
- Dimitri Krainc
- Davee Department of Neurology, Simpson Querrey Center for Neurogenetics, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | | | - Bradford Casey
- Michael J. Fox Foundation for Parkinson's Research, New York, NY, USA
| | - Frances E Jensen
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sarah Tishkoff
- Departments of Genetics and Biology, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Steven E Hyman
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
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25
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Wang H, Ma LZ, Sheng ZH, Liu JY, Yuan WY, Guo F, Zhang W, Tan L. Association between cerebrospinal fluid clusterin and biomarkers of Alzheimer's disease pathology in mild cognitive impairment: a longitudinal cohort study. Front Aging Neurosci 2023; 15:1256389. [PMID: 37941999 PMCID: PMC10629112 DOI: 10.3389/fnagi.2023.1256389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 10/10/2023] [Indexed: 11/10/2023] Open
Abstract
Background Clusterin, a glycoprotein implicated in Alzheimer's disease (AD), remains unclear. The objective of this study was to analyze the effect of cerebrospinal fluid (CSF) clusterin in relation to AD biomarkers using a longitudinal cohort of non-demented individuals. Methods We gathered a sample comprising 86 individuals under cognition normal (CN) and 134 patients diagnosed with MCI via the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. To investigate the correlation of CSF clusterin with cognitive function and markers of key physiological changes, we employed multiple linear regression and mixed-effect models. We undertook a causal mediation analysis to inspect the mediating influence of CSF clusterin on cognitive abilities. Results Pathological characteristics associated with baseline Aβ42, Tau, brain volume, exhibited a correlation with initial CSF clusterin in the general population, Specifically, these correlations were especially prominent in the MCI population; CSF Aβ42 (PCN = 0.001; PMCI = 0.007), T-tau (PCN < 0.001; PMCI < 0.001), and Mid temporal (PCN = 0.033; PMCI = 0.005). Baseline CSF clusterin level was predictive of measurable cognitive shifts in the MCI population, as indicated by MMSE (β = 0.202, p = 0.029), MEM (β = 0.186, p = 0.036), RAVLT immediate recall (β = 0.182, p = 0.038), and EF scores (β = 0.221, p = 0.013). In MCI population, the alterations in brain regions (17.87% of the total effect) mediated the effect of clusterin on cognition. It was found that variables such as age, gender, and presence of APOE ε4 carrier status, influenced some of these connections. Conclusion Our investigation underscored a correlation between CSF clusterin concentrations and pivotal AD indicators, while also highlighting clusterin's potential role as a protective factor for cognitive abilities in MCI patients.
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Affiliation(s)
- Hao Wang
- Department of Neurology, Affiliated Hospital of Weifang Medical University, School of Clinical Medicine, Weifang Medical University, Weifang, China
| | - Ling-Zhi Ma
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Ze-Hu Sheng
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Jia-Yao Liu
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Wei-Yu Yuan
- Department of Neurology, Affiliated Hospital of Weifang Medical University, School of Clinical Medicine, Weifang Medical University, Weifang, China
| | - Fan Guo
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Wei Zhang
- Department of Neurology, Affiliated Hospital of Weifang Medical University, School of Clinical Medicine, Weifang Medical University, Weifang, China
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
- Department of Neurology, Qingdao Municipal Hospital, University of Health and Rehabilitation Sciences, Qingdao, China
| | - Lan Tan
- Department of Neurology, Affiliated Hospital of Weifang Medical University, School of Clinical Medicine, Weifang Medical University, Weifang, China
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
- Department of Neurology, Qingdao Municipal Hospital, University of Health and Rehabilitation Sciences, Qingdao, China
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Ghouri R, Öksüz N, Taşdelen B, Özge A. Factors affecting progression of non-Alzheimer dementia: a retrospective analysis with long-term follow-up. Front Neurol 2023; 14:1240093. [PMID: 37920834 PMCID: PMC10619744 DOI: 10.3389/fneur.2023.1240093] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Accepted: 08/23/2023] [Indexed: 11/04/2023] Open
Abstract
Background Non-Alzheimer's dementias, including vascular dementia (VaD), frontotemporal dementia (FTD), Lewy body dementia (LBD), and Parkinson's disease dementia (PDD), possess unique characteristics and prognostic factors that remain poorly understood. This study aims to investigate the temporal course of these subtypes and identify the impact of functional, neuropsychiatric, and comorbid medical conditions on prognosis. Additionally, the relationship between hippocampal atrophy, white matter intensities, and disease progression will be examined, along with the identification of key covariates influencing slow or fast progression in non-Alzheimer's dementias. Methods A total of 196 patients with non-Alzheimer's dementias who underwent at least three comprehensive evaluations were included, with proportions of VaD, FTD, LBD, and PDD being 50, 19.39, 19.90, and 10.71%, respectively. Patient demographics, comorbidities, neuropsychiatric and neuroimaging parameters, and global evaluation were analyzed using appropriate statistical methods. The study followed patients for a mean duration of 62.57 ± 33.45 months (ranging from 11 to 198 months). Results The results from three different visits for each non-AD dementia case demonstrated significant differences in various measures across visits, including functional capacity (BDLAS), cognition (MMSE), and other neuropsychological tests. Notably, certain genotypes and hippocampal atrophy grades were more prevalent in specific subtypes. The results indicate that Fazekas grading and hippocampal atrophy were significant predictors of disease progression, while epilepsy, extrapyramidal symptoms, thyroid dysfunction, coronary artery disease, diabetes mellitus, hypertension, stroke, hyperlipidemia, sleep disorders, smoking, and family history of dementia were not significant predictors. BDLAS and EDLAS scores at the first and second visits showed significant associations with disease progression, while scores at the third visit did not. Group-based trajectory analysis revealed that non-AD cases separated into two reliable subgroups with slow/fast prognosis, showing high reliability (Entropy = 0.790, 51.8 vs. 48.2%). Conclusion This study provides valuable insights into the temporal course and prognostic factors of non-Alzheimer's dementias. The findings underscore the importance of considering functional, neuropsychological, and comorbid medical conditions in understanding disease progression. The significant associations between hippocampal atrophy, white matter intensities, and prognosis highlight potential avenues for further research and therapeutic interventions.
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Affiliation(s)
- Reza Ghouri
- Department of Neurology, School of Medicine, Mersin University, Mersin, Türkiye
| | - Nevra Öksüz
- Department of Neurology, School of Medicine, Mersin University, Mersin, Türkiye
| | - Bahar Taşdelen
- Department of Biostatistics, School of Medicine, Mersin University, Mersin, Türkiye
| | - Aynur Özge
- Department of Neurology, School of Medicine, Mersin University, Mersin, Türkiye
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Stephenson D, Belfiore-Oshan R, Karten Y, Keavney J, Kwok DK, Martinez T, Montminy J, Müller MLTM, Romero K, Sivakumaran S. Transforming Drug Development for Neurological Disorders: Proceedings from a Multidisease Area Workshop. Neurotherapeutics 2023; 20:1682-1691. [PMID: 37823970 PMCID: PMC10684834 DOI: 10.1007/s13311-023-01440-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/06/2023] [Indexed: 10/13/2023] Open
Abstract
Neurological disorders represent some of the most challenging therapeutic areas for successful drug approvals. The escalating global burden of death and disability for such diseases represents a significant worldwide public health challenge, and the rate of failure of new therapies for chronic progressive disorders of the nervous system is higher relative to other non-neurological conditions. However, progress is emerging rapidly in advancing the drug development landscape in both rare and common neurodegenerative diseases. In October 2022, the Critical Path Institute (C-Path) and the US Food and Drug Administration (FDA) organized a Neuroscience Annual Workshop convening representatives from the drug development industry, academia, the patient community, government agencies, and regulatory agencies regarding the future development of tools and therapies for neurological disorders. This workshop focused on five chronic progressive diseases: Alzheimer's disease, Parkinson's disease, Huntington's disease, Duchenne muscular dystrophy, and inherited ataxias. This special conference report reviews the key points discussed during the three-day dynamic workshop, including shared learnings, and recommendations that promise to catalyze future advancement of novel therapies and drug development tools.
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Di Paolo M, Corsi F, Cerri C, Bisti S, Piano I, Gargini C. A Window to the Brain: The Retina to Monitor the Progression and Efficacy of Saffron Repron ® Pre-Treatment in an LPS Model of Neuroinflammation and Memory Impairment. Pharmaceuticals (Basel) 2023; 16:1307. [PMID: 37765115 PMCID: PMC10536337 DOI: 10.3390/ph16091307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 08/23/2023] [Accepted: 09/11/2023] [Indexed: 09/29/2023] Open
Abstract
A mechanism shared by most neurodegenerative diseases, like Alzheimer's disease (AD) and Parkinson's disease (PD), is neuroinflammation. It has been shown to have a link between cognitive impairment and retinal function under neuroinflammatory conditions, confirming the essential role of the retina as a window to the brain. Here, we characterize a mouse model of LPS-induced neuroinflammation describing the parallel deterioration of both memory and visual function. Then, we demonstrate, using the Novel Object Recognition test (NOR) and electroretinogram (ERG) recordings, that preventive, chronic treatment with saffron Repron® is able to reduce the neuroinflammation process and prevent the impairment of both cognitive and visual function. The improvement in behavioral and visual function is confirmed by the pattern of expression of neuroinflammation-related genes and related proteins where pre-treatment with Repron® saffron presents a positive modulation compared with that obtained in animals treated with LPS alone. These results hold for retinal tissue and partially in the brain, where it appears that the onset of damage was delayed. This trend underlines the critical role of the retina as a most sensitive portion of the central nervous system to LPS-induced damage and could be used as a "sensor" for the early detection of neurodegenerative diseases such as Alzheimer's.
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Affiliation(s)
- Mattia Di Paolo
- Department of Ophthalmology and Visual Science, University of Louisville, Louisville, KY 40202, USA;
- Istituto Nazionale di Biostrutture e Biosistemi (INBB), via Medaglie d’Oro 305, 00136 Roma, Italy; (F.C.); (S.B.); (C.G.)
| | - Francesca Corsi
- Istituto Nazionale di Biostrutture e Biosistemi (INBB), via Medaglie d’Oro 305, 00136 Roma, Italy; (F.C.); (S.B.); (C.G.)
- Department of Pharmacy, University of Pisa, Via Bonanno 6, 56126 Pisa, Italy;
| | - Chiara Cerri
- Department of Pharmacy, University of Pisa, Via Bonanno 6, 56126 Pisa, Italy;
| | - Silvia Bisti
- Istituto Nazionale di Biostrutture e Biosistemi (INBB), via Medaglie d’Oro 305, 00136 Roma, Italy; (F.C.); (S.B.); (C.G.)
| | - Ilaria Piano
- Istituto Nazionale di Biostrutture e Biosistemi (INBB), via Medaglie d’Oro 305, 00136 Roma, Italy; (F.C.); (S.B.); (C.G.)
- Department of Pharmacy, University of Pisa, Via Bonanno 6, 56126 Pisa, Italy;
| | - Claudia Gargini
- Istituto Nazionale di Biostrutture e Biosistemi (INBB), via Medaglie d’Oro 305, 00136 Roma, Italy; (F.C.); (S.B.); (C.G.)
- Department of Pharmacy, University of Pisa, Via Bonanno 6, 56126 Pisa, Italy;
- Interdepartmental Research Center “Nutraceuticals and Food for Health”, University of Pisa, 56126 Pisa, Italy
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Han F, Lee J, Chen X, Ziontz J, Ward T, Landau SM, Baker SL, Harrison TM, Jagust WJ. Global brain activity and its coupling with cerebrospinal fluid flow is related to tau pathology. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.12.557492. [PMID: 37745434 PMCID: PMC10515801 DOI: 10.1101/2023.09.12.557492] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
Amyloid-β (Aβ) and tau deposition constitute Alzheimer's disease (AD) neuropathology. Cortical tau deposits first in the entorhinal cortex and hippocampus and then propagates to neocortex in an Aβ-dependent manner. Tau also tends to accumulate earlier in higher-order association cortex than in lower-order primary sensory-motor cortex. While previous research has examined the production and spread of tau, little attention has been paid to its clearance. Low-frequency (<0.1 Hz) global brain activity during the resting state is coupled with cerebrospinal fluid (CSF) flow and potentially reflects glymphatic clearance. Here we report that tau deposition in subjects with evaluated Aβ, accompanied by cortical thinning and cognitive decline, is strongly associated with decreased coupling between CSF flow and global brain activity. Substantial modulation of global brain activity is also manifested as propagating waves of brain activation between higher- and lower-order regions, resembling tau spreading. Together, the findings suggest an important role of resting-state global brain activity in AD tau pathology.
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Affiliation(s)
- Feng Han
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA
| | - JiaQie Lee
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA
| | - Xi Chen
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA
- Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Jacob Ziontz
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA
| | - Tyler Ward
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA
| | - Susan M Landau
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA
| | | | - Theresa M Harrison
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA
| | - William J Jagust
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA
- Lawrence Berkeley National Laboratory, Berkeley, CA, USA
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Dai Y, Yu-Chun H, Fernandes BS, Zhang K, Xiaoyang L, Enduru N, Liu A, Manuel AM, Jiang X, Zhao Z. Disentangling accelerated cognitive decline from the normal aging process and unraveling its genetic components: A neuroimaging-based deep learning approach. RESEARCH SQUARE 2023:rs.3.rs-3328861. [PMID: 37720047 PMCID: PMC10503860 DOI: 10.21203/rs.3.rs-3328861/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/19/2023]
Abstract
Background The progressive cognitive decline that is an integral component of AD unfolds in tandem with the natural aging process. Neuroimaging features have demonstrated the capacity to distinguish cognitive decline changes stemming from typical brain aging and Alzheimer's disease between different chronological points. Methods We developed a deep-learning framework based on dual-loss Siamese ResNet network to extract fine-grained information from the longitudinal structural magnetic resonance imaging (MRI) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. We then conducted genome-wide association studies (GWAS) and post-GWAS analyses to reveal the genetic basis of AD-related accelerated cognitive decline. Results We used our model to process data from 1,313 individuals, training it on 414 cognitively normal people and predicting cognitive assessment for all participants. In our analysis of accelerated cognitive decline GWAS, we identified two genome-wide significant loci: APOE locus (chromosome 19 p13.32) and rs144614292 (chromosome 11 p15.1). Variant rs144614292 (G>T) has not been reported in previous AD GWA studies. It is within the intronic region of NELL1, which is expressed in neuron and plays a role in controlling cell growth and differentiation. In addition, MUC7 and PROL1/OPRPNon chromosome 4 were significant at the gene level. The cell-type-specific enrichment analysis and functional enrichment of GWAS signals highlighted the microglia and immune-response pathways. Furthermore, we found that the cognitive decline slope GWAS was positively correlated with previous AD GWAS. Conclusion Our deep learning model was demonstrated effective on extracting relevant neuroimaging features and predicting individual cognitive decline. We reported a novel variant (rs144614292) within the NELL1 gene. Our approach has the potential to disentangle accelerated cognitive decline from the normal aging process and to determine its related genetic factors, leveraging opportunities for early intervention.
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Affiliation(s)
- Yulin Dai
- The University of Texas Health Science Center at Houston
| | - Hsu Yu-Chun
- The University of Texas Health Science Center at Houston
| | | | - Kai Zhang
- The University of Texas Health Science Center at Houston
| | - Li Xiaoyang
- The University of Texas Health Science Center at Houston
| | - Nitesh Enduru
- The University of Texas Health Science Center at Houston
| | - Andi Liu
- The University of Texas Health Science Center at Houston
| | | | - Xiaoqian Jiang
- The University of Texas Health Science Center at Houston
| | - Zhongming Zhao
- The University of Texas Health Science Center at Houston
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Igarashi A, Azuma MK, Zhang Q, Ye W, Sardesai A, Folse H, Chavan A, Tomita K, Tahami Monfared AA. Predicting the Societal Value of Lecanemab in Early Alzheimer's Disease in Japan: A Patient-Level Simulation. Neurol Ther 2023; 12:1133-1157. [PMID: 37188886 PMCID: PMC10310671 DOI: 10.1007/s40120-023-00492-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/25/2023] [Accepted: 04/28/2023] [Indexed: 05/17/2023] Open
Abstract
INTRODUCTION Alzheimer's disease (AD), a neurodegenerative disorder that progresses from mild cognitive impairment (MCI) to dementia, is responsible for significant burden on caregivers and healthcare systems. In this study, data from the large phase III CLARITY AD trial were used to estimate the societal value of lecanemab plus standard of care (SoC) versus SoC alone against a range of willingness-to-pay (WTP) thresholds from a healthcare and societal perspective in Japan. METHODS A disease simulation model was used to evaluate the impact of lecanemab on disease progression in early AD based on data from the phase III CLARITY AD trial and published literature. The model used a series of predictive risk equations based on clinical and biomarker data from the Alzheimer's Disease Neuroimaging Initiative and Assessment of Health Economics in Alzheimer's Disease II study. The model predicted key patient outcomes, including life years (LYs), quality-adjusted life years (QALYs), and total healthcare and informal costs of patients and caregivers. RESULTS Over a lifetime horizon, patients treated with lecanemab plus SoC gained an additional 0.73 LYs compared with SoC alone (8.50 years vs. 7.77 years). Lecanemab, with an average treatment duration of 3.68 years, was found to be associated with a 0.91 increase in patient QALYs and a total increase of 0.96 when accounting for caregiver utility. The estimated value of lecanemab varied according to the WTP thresholds (JPY 5-15 million per QALY gained) and the perspective employed. From the narrow healthcare payer's perspective, it ranged from JPY 1,331,305 to JPY 3,939,399. From the broader healthcare payer's perspective, it ranged from JPY 1,636,827 to JPY 4,249,702, while from the societal perspective, it ranged from JPY 1,938,740 to JPY 4,675,818. CONCLUSION The use of lecanemab plus SoC would improve health and humanistic outcomes with reduced economic burden for patients and caregivers with early AD in Japan.
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Affiliation(s)
- Ataru Igarashi
- Department of Health Economics and Outcomes Research, Graduate School of Pharmaceutical Sciences, The University of Tokyo, Tokyo, Japan
- Department of Public Health, Yokohama City University School of Medicine, Kanagawa, Japan
| | - Mie Kasai Azuma
- Medical Headquarter, Clinical Planning and Development, Eisai Co., Ltd., Tokyo, Japan
| | - Quanwu Zhang
- Global Alzheimer's Disease and Brain Health, Eisai Inc., 200 Metro Blvd., Nutley, NJ, 07110, USA
| | - Weicheng Ye
- Evidence Synthesis, Modeling and Communication, Evidera Inc., Bethesda, MD, 20814, USA
| | - Aditya Sardesai
- Evidence Synthesis, Modeling and Communication, Evidera Inc., Bethesda, MD, 20814, USA
| | - Henri Folse
- Evidence Synthesis, Modeling and Communication, Evidera Inc., Bethesda, MD, 20814, USA
| | - Ameya Chavan
- Evidence Synthesis, Modeling and Communication, Evidera Inc., Bethesda, MD, 20814, USA
| | | | - Amir Abbas Tahami Monfared
- Global Alzheimer's Disease and Brain Health, Eisai Inc., 200 Metro Blvd., Nutley, NJ, 07110, USA.
- Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, QC, Canada.
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Iyaswamy A, Lu K, Guan XJ, Kan Y, Su C, Liu J, Jaganathan R, Vasudevan K, Paul J, Thakur A, Li M. Impact and Advances in the Role of Bacterial Extracellular Vesicles in Neurodegenerative Disease and Its Therapeutics. Biomedicines 2023; 11:2056. [PMID: 37509695 PMCID: PMC10377521 DOI: 10.3390/biomedicines11072056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 07/16/2023] [Accepted: 07/19/2023] [Indexed: 07/30/2023] Open
Abstract
Bacterial Extracellular Vesicles (BEVs) possess the capability of intracellular interactions with other cells, and, hence, can be utilized as an efficient cargo for worldwide delivery of therapeutic substances such as monoclonal antibodies, proteins, plasmids, siRNA, and small molecules for the treatment of neurodegenerative diseases (NDs). BEVs additionally possess a remarkable capacity for delivering these therapeutics across the blood-brain barrier to treat Alzheimer's disease (AD). This review summarizes the role and advancement of BEVs for NDs, AD, and their treatment. Additionally, it investigates the critical BEV networks in the microbiome-gut-brain axis, their defensive and offensive roles in NDs, and their interaction with NDs. Furthermore, the part of BEVs in the neuroimmune system and their interference with ND, as well as the risk factors made by BEVs in the autophagy-lysosomal pathway and their potential outcomes on ND, are all discussed. To conclude, this review aims to gain a better understanding of the credentials of BEVs in NDs and possibly discover new therapeutic strategies.
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Affiliation(s)
- Ashok Iyaswamy
- Mr. & Mrs. Ko Chi-Ming Centre for Parkinson's Disease Research, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong SAR, China
- Department of Biochemistry, Karpagam Academy of Higher Education, Coimbatore 641021, India
| | - Kejia Lu
- Mr. & Mrs. Ko Chi-Ming Centre for Parkinson's Disease Research, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong SAR, China
| | - Xin-Jie Guan
- Mr. & Mrs. Ko Chi-Ming Centre for Parkinson's Disease Research, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong SAR, China
| | - Yuxuan Kan
- Mr. & Mrs. Ko Chi-Ming Centre for Parkinson's Disease Research, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong SAR, China
| | - Chengfu Su
- Mr. & Mrs. Ko Chi-Ming Centre for Parkinson's Disease Research, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong SAR, China
| | - Jia Liu
- Mr. & Mrs. Ko Chi-Ming Centre for Parkinson's Disease Research, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong SAR, China
| | - Ravindran Jaganathan
- Preclinical Department, Faculty of Medicine, Royal College of Medicine Perak, Universiti Kuala Lumpur, Ipoh 30450, Malaysia
| | | | - Jeyakumari Paul
- Department of Physiology, Dr. ALM PG Institute of Basic Medical Sciences, University of Madras, Chennai 600005, India
| | - Abhimanyu Thakur
- Pritzker School of Molecular Engineering, Ben May Department for Cancer Research, University of Chicago, Chicago, IL 60637, USA
| | - Min Li
- Mr. & Mrs. Ko Chi-Ming Centre for Parkinson's Disease Research, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong SAR, China
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Dipietro L, Gonzalez-Mego P, Ramos-Estebanez C, Zukowski LH, Mikkilineni R, Rushmore RJ, Wagner T. The evolution of Big Data in neuroscience and neurology. JOURNAL OF BIG DATA 2023; 10:116. [PMID: 37441339 PMCID: PMC10333390 DOI: 10.1186/s40537-023-00751-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Accepted: 05/08/2023] [Indexed: 07/15/2023]
Abstract
Neurological diseases are on the rise worldwide, leading to increased healthcare costs and diminished quality of life in patients. In recent years, Big Data has started to transform the fields of Neuroscience and Neurology. Scientists and clinicians are collaborating in global alliances, combining diverse datasets on a massive scale, and solving complex computational problems that demand the utilization of increasingly powerful computational resources. This Big Data revolution is opening new avenues for developing innovative treatments for neurological diseases. Our paper surveys Big Data's impact on neurological patient care, as exemplified through work done in a comprehensive selection of areas, including Connectomics, Alzheimer's Disease, Stroke, Depression, Parkinson's Disease, Pain, and Addiction (e.g., Opioid Use Disorder). We present an overview of research and the methodologies utilizing Big Data in each area, as well as their current limitations and technical challenges. Despite the potential benefits, the full potential of Big Data in these fields currently remains unrealized. We close with recommendations for future research aimed at optimizing the use of Big Data in Neuroscience and Neurology for improved patient outcomes. Supplementary Information The online version contains supplementary material available at 10.1186/s40537-023-00751-2.
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Affiliation(s)
| | - Paola Gonzalez-Mego
- Spaulding Rehabilitation/Neuromodulation Lab, Harvard Medical School, Cambridge, MA USA
| | | | | | | | | | - Timothy Wagner
- Highland Instruments, Cambridge, MA USA
- Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA USA
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Attaluri S, Jaimes Gonzalez J, Kirmani M, Vogel AD, Upadhya R, Kodali M, Madhu LN, Rao S, Shuai B, Babu RS, Huard C, Shetty AK. Intranasally administered extracellular vesicles from human induced pluripotent stem cell-derived neural stem cells quickly incorporate into neurons and microglia in 5xFAD mice. Front Aging Neurosci 2023; 15:1200445. [PMID: 37424631 PMCID: PMC10323752 DOI: 10.3389/fnagi.2023.1200445] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 06/07/2023] [Indexed: 07/11/2023] Open
Abstract
Introduction Extracellular vesicles (EVs) released by human-induced pluripotent stem cell (hiPSC)-derived neural stem cells (NSCs) have robust antiinflammatory and neurogenic properties due to therapeutic miRNAs and proteins in their cargo. Hence, hiPSC-NSC-EVs are potentially an excellent biologic for treating neurodegenerative disorders, including Alzheimer's disease (AD). Methods This study investigated whether intranasally (IN) administered hiPSC-NSC-EVs would quickly target various neural cell types in the forebrain, midbrain, and hindbrain regions of 3-month-old 5xFAD mice, a model of β-amyloidosis and familial AD. We administered a single dose of 25 × 109 hiPSC-NSC-EVs labeled with PKH26, and different cohorts of naïve and 5xFAD mice receiving EVs were euthanized at 45 min or 6 h post-administration. Results At 45 min post-administration, EVs were found in virtually all subregions of the forebrain, midbrain, and hindbrain of naïve and 5xFAD mice, with predominant targeting and internalization into neurons, interneurons, and microglia, including plaque-associated microglia in 5xFAD mice. EVs also came in contact with the plasma membranes of astrocytic processes and the soma of oligodendrocytes in white matter regions. Evaluation of CD63/CD81 expression with the neuronal marker confirmed that PKH26 + particles found within neurons were IN administered hiPSC-NSC-EVs. At 6 h post-administration, EVs persisted in all cell types in both groups, with the distribution mostly matching what was observed at 45 min post-administration. Area fraction (AF) analysis revealed that, in both naïve and 5xFAD mice, higher fractions of EVs incorporate into forebrain regions at both time points. However, at 45 min post-IN administration, AFs of EVs within cell layers in forebrain regions and within microglia in midbrain and hindbrain regions were lower in 5xFAD mice than naïve mice, implying that amyloidosis reduces EV penetrance. Discussion Collectively, the results provide novel evidence that IN administration of therapeutic hiPSC-NSC-EVs is an efficient avenue for directing such EVs into neurons and glia in all brain regions in the early stage of amyloidosis. As pathological changes in AD are observed in multiple brain areas, the ability to deliver therapeutic EVs into various neural cells in virtually every brain region in the early stage of amyloidosis is attractive for promoting neuroprotective and antiinflammatory effects.
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Nicoletti A, Baschi R, Cicero CE, Iacono S, Re VL, Luca A, Schirò G, Monastero R. Sex and gender differences in Alzheimer's disease, Parkinson's disease, and Amyotrophic Lateral Sclerosis: a narrative review. Mech Ageing Dev 2023; 212:111821. [PMID: 37127082 DOI: 10.1016/j.mad.2023.111821] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 04/24/2023] [Accepted: 04/27/2023] [Indexed: 05/03/2023]
Abstract
Neurodegenerative diseases (NDs), including Alzheimer's disease (AD), Parkinson's disease (PD), and amyotrophic lateral sclerosis (ALS), exhibit high phenotypic variability and they are very common in the general population. These diseases are associated with poor prognosis and a significant burden on patients and their caregivers. Although increasing evidence suggests that biological sex is an important factor for the development and phenotypical expression of some NDs, the role of sex and gender in the diagnosis and prognosis of NDs has been poorly explored. Current knowledge relating to sex- and gender-related differences in the epidemiology, clinical features, biomarkers, and treatment of AD, PD, and ALS will be summarized in this narrative review. The cumulative evidence hitherto collected suggests that sex and gender are factors to be considered in explaining the heterogeneity of these NDs. Clarifying the role of sex and gender in AD, PD, and ALS is a key topic in precision medicine, which will facilitate sex-specific prevention and treatment strategies to be implemented in the near future.
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Affiliation(s)
- Alessandra Nicoletti
- Department of Medical, Surgical Sciences and Advanced Technologies "G.F. Ingrassia", University of Catania, Via Santa Sofia 78, 95123, Catania, Italy.
| | - Roberta Baschi
- Department of Biomedicine, Neurosciences and Advanced Diagnostics (BiND), University of Palermo, Via La Loggia 1, 90129 Palermo, Italy
| | - Calogero Edoardo Cicero
- Department of Medical, Surgical Sciences and Advanced Technologies "G.F. Ingrassia", University of Catania, Via Santa Sofia 78, 95123, Catania, Italy
| | - Salvatore Iacono
- Department of Biomedicine, Neurosciences and Advanced Diagnostics (BiND), University of Palermo, Via La Loggia 1, 90129 Palermo, Italy
| | - Vincenzina Lo Re
- Neurology Service, Mediterranean Institute for Transplantation and Advanced Specialized Therapies (IRCCS-ISMETT), Via Ernesto Tricomi 5, 90127 Palermo, Italy; Women's Brain Project, Guntershausen, Switzerland
| | - Antonina Luca
- Department of Medical, Surgical Sciences and Advanced Technologies "G.F. Ingrassia", University of Catania, Via Santa Sofia 78, 95123, Catania, Italy
| | - Giuseppe Schirò
- Department of Biomedicine, Neurosciences and Advanced Diagnostics (BiND), University of Palermo, Via La Loggia 1, 90129 Palermo, Italy
| | - Roberto Monastero
- Department of Biomedicine, Neurosciences and Advanced Diagnostics (BiND), University of Palermo, Via La Loggia 1, 90129 Palermo, Italy.
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Park SW, Yeo NY, Lee J, Lee SH, Byun J, Park DY, Yum S, Kim JK, Byeon G, Kim Y, Jang JW. Machine learning application for classification of Alzheimer's disease stages using 18F-flortaucipir positron emission tomography. Biomed Eng Online 2023; 22:40. [PMID: 37120537 PMCID: PMC10149022 DOI: 10.1186/s12938-023-01107-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Accepted: 04/25/2023] [Indexed: 05/01/2023] Open
Abstract
BACKGROUND The progression of Alzheimer's dementia (AD) can be classified into three stages: cognitive unimpairment (CU), mild cognitive impairment (MCI), and AD. The purpose of this study was to implement a machine learning (ML) framework for AD stage classification using the standard uptake value ratio (SUVR) extracted from 18F-flortaucipir positron emission tomography (PET) images. We demonstrate the utility of tau SUVR for AD stage classification. We used clinical variables (age, sex, education, mini-mental state examination scores) and SUVR extracted from PET images scanned at baseline. Four types of ML frameworks, such as logistic regression, support vector machine (SVM), extreme gradient boosting, and multilayer perceptron (MLP), were used and explained by Shapley Additive Explanations (SHAP) to classify the AD stage. RESULTS Of a total of 199 participants, 74, 69, and 56 patients were in the CU, MCI, and AD groups, respectively; their mean age was 71.5 years, and 106 (53.3%) were men. In the classification between CU and AD, the effect of clinical and tau SUVR was high in all classification tasks and all models had a mean area under the receiver operating characteristic curve (AUC) > 0.96. In the classification between MCI and AD, the independent effect of tau SUVR in SVM had an AUC of 0.88 (p < 0.05), which was the highest compared to other models. In the classification between MCI and CU, the AUC of each classification model was higher with tau SUVR variables than with clinical variables independently, which yielded an AUC of 0.75(p < 0.05) in MLP, which was the highest. As an explanation by SHAP for the classification between MCI and CU, and AD and CU, the amygdala and entorhinal cortex greatly affected the classification results. In the classification between MCI and AD, the para-hippocampal and temporal cortex affected model performance. Especially entorhinal cortex and amygdala showed a higher effect on model performance than all clinical variables in the classification between MCI and CU. CONCLUSIONS The independent effect of tau deposition indicates that it is an effective biomarker in classifying CU and MCI into clinical stages using MLP. It is also very effective in classifying AD stages using SVM with clinical information that can be easily obtained at clinical screening.
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Affiliation(s)
- Sang Won Park
- Department of Neurology, Kangwon National University Hospital, 156, Baengnyeong-ro, Chuncheon, Gangwon, 24289, Republic of Korea
- Department of Medical Informatics, Kangwon National University, Chuncheon, Korea
- School of Medicine, Kangwon National University, Chuncheon, Korea
| | - Na Young Yeo
- Department of Neurology, Kangwon National University Hospital, 156, Baengnyeong-ro, Chuncheon, Gangwon, 24289, Republic of Korea
- Department of Medical Bigdata Convergence, Kangwon National University, Chuncheon, Korea
| | - Jinsu Lee
- Department of Data Science Research Center, Seoul National University Hospital, Seoul, Korea
| | - Suk-Hee Lee
- Department of Statistics, Kangwon National University, Chuncheon, Korea
| | - Junghyun Byun
- Department of Healthcare, Radiation Health Institute, Hydro & Nuclear Co., Ltd., Seongnam, Korea
| | - Dong Young Park
- Department of Neurology, Kangwon National University Hospital, 156, Baengnyeong-ro, Chuncheon, Gangwon, 24289, Republic of Korea
- Department of Medical Bigdata Convergence, Kangwon National University, Chuncheon, Korea
| | - Sujin Yum
- Department of Neurology, Kangwon National University Hospital, 156, Baengnyeong-ro, Chuncheon, Gangwon, 24289, Republic of Korea
- Department of Medical Bigdata Convergence, Kangwon National University, Chuncheon, Korea
| | - Jung-Kyeom Kim
- Department of Neurology, Kangwon National University Hospital, 156, Baengnyeong-ro, Chuncheon, Gangwon, 24289, Republic of Korea
| | - Gihwan Byeon
- School of Medicine, Kangwon National University, Chuncheon, Korea
- Department of Psychiatry, Kangwon National University Hospital, Chuncheon, Korea
| | - Yeshin Kim
- Department of Neurology, Kangwon National University Hospital, 156, Baengnyeong-ro, Chuncheon, Gangwon, 24289, Republic of Korea
- School of Medicine, Kangwon National University, Chuncheon, Korea
| | - Jae-Won Jang
- Department of Neurology, Kangwon National University Hospital, 156, Baengnyeong-ro, Chuncheon, Gangwon, 24289, Republic of Korea.
- Department of Medical Informatics, Kangwon National University, Chuncheon, Korea.
- School of Medicine, Kangwon National University, Chuncheon, Korea.
- Department of Medical Bigdata Convergence, Kangwon National University, Chuncheon, Korea.
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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] [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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Tahami Monfared AA, Ye W, Sardesai A, Folse H, Chavan A, Aruffo E, Zhang Q. A Path to Improved Alzheimer's Care: Simulating Long-Term Health Outcomes of Lecanemab in Early Alzheimer's Disease from the CLARITY AD Trial. Neurol Ther 2023; 12:863-881. [PMID: 37009976 DOI: 10.1007/s40120-023-00473-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 03/21/2023] [Indexed: 04/04/2023] Open
Abstract
INTRODUCTION Alzheimer's disease (AD), a progressive neurodegenerative disease, is the main cause of dementia and one of the leading causes of death for elderly people in the USA. Lecanemab is a humanized IgG1 monoclonal antibody targeting amyloid protofibrils for the treatment of early AD [i.e., mild cognitive impairment (MCI) or mild AD dementia]. In a recent 18-month phase III trial, using a double-blind, placebo-controlled design, lecanemab treatment led to reduced brain amyloid burden and significant improvements in cognitive and functional abilities in individuals with early AD. METHODS An evidence-based patient-level disease simulation model was updated to estimate the long-term health outcomes of lecanemab plus standard of care (SoC) compared to SoC alone in patients with early AD and evidence of brain amyloid burden, using recent phase III trial data and published literature. The disease progression is described by changes in the underlying biomarkers of AD, including measures of amyloid and tau, and their connection to the clinical presentation of the disease assessed through various patient-level scales of cognition and function. RESULTS Lecanemab treatment was estimated to slow the progression of AD to moderate and severe stages and reduce the time spent in these more advanced states. In individuals with early AD, lecanemab plus SoC was associated with a gain of 0.71 quality-adjusted life-years (QALYs), a 2.95-year delay in mean time to progression to AD dementia, a reduction of 0.11 years in institutional care, and an additional 1.07 years in community care as shown in the base-case study. Improved health outcomes were demonstrated with lecanemab treatment when initiated earlier based on age, disease severity, or tau pathology, resulting in estimated gains in QALYs ranging from 0.77 to 1.09 years, compared to 0.4 years in the mild AD dementia subset, as shown by the model. CONCLUSION The study findings demonstrate the potential clinical value of lecanemab for individuals with early AD by slowing down disease progression and prolonging time in earlier stages of disease, which significantly benefits not only patients and caregivers but also society overall. TRIAL REGISTRATION ClinicalTrials.gov identifier, NCT03887455.
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Affiliation(s)
- Amir Abbas Tahami Monfared
- Eisai Inc., 200 Metro Blvd., Nutley, NJ, 07110, USA.
- Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, QC, Canada.
| | - Weicheng Ye
- Evidence Synthesis, Modeling and Communication, Evidera Inc., Bethesda, MD, 20814, USA
| | - Aditya Sardesai
- Evidence Synthesis, Modeling and Communication, Evidera Inc., Bethesda, MD, 20814, USA
| | - Henri Folse
- Evidence Synthesis, Modeling and Communication, Evidera Inc., Bethesda, MD, 20814, USA
| | - Ameya Chavan
- Evidence Synthesis, Modeling and Communication, Evidera Inc., Bethesda, MD, 20814, USA
| | - Elena Aruffo
- Evidence Synthesis, Modeling and Communication, Evidera Inc., Bethesda, MD, 20814, USA
| | - Quanwu Zhang
- Eisai Inc., 200 Metro Blvd., Nutley, NJ, 07110, USA
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Mindt MR, Okonkwo O, Weiner MW, Veitch DP, Aisen P, Ashford M, Coker G, Donohue MC, Langa KM, Miller G, Petersen R, Raman R, Nosheny R. Improving generalizability and study design of Alzheimer's disease cohort studies in the United States by including under-represented populations. Alzheimers Dement 2023; 19:1549-1557. [PMID: 36372959 PMCID: PMC10101866 DOI: 10.1002/alz.12823] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 09/07/2022] [Accepted: 09/09/2022] [Indexed: 11/16/2022]
Abstract
The poor generalizability of clinical research data due to the enrollment of highly educated, non-Latinx White participants hampers the development of therapies for Alzheimer's disease (AD). Black and Latinx older adults have a greater risk for dementia, yet it is unclear how health-care disparities and sociocultural factors influence potential AD therapies and prognosis. Low enrollment of under-represented populations may be attributable to several factors including greater exclusion due to higher rates of comorbidities, lower access to AD clinics, and the legacy of unethical treatment in medical research. This perspective outlines solutions tested in the Brain Health Registry (BHR) and the Alzheimer's Disease Neuroimaging Initiative (ADNI), including culturally-informed digital research methods, community-engaged research strategies, leadership from under-represented communities, and the reduction of exclusion criteria based on comorbidities. Our successes demonstrate that it is possible to increase the inclusion and engagement of under-represented populations into US-based clinical studies, thereby increasing the generalizability of their results.
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Affiliation(s)
- Monica Rivera Mindt
- Department of Psychology, Latin American and Latino Studies Institute, & African and African-American Studies, Fordham University, New York, NY, USA
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ozioma Okonkwo
- Wisconsin Alzheimer’s Disease Research Center and Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Michael W. Weiner
- Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA
- Department of Medicine, University of California, San Francisco, CA, USA
- Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, CA, USA
- Department of Neurology, University of California, San Francisco, CA, USA
| | - Dallas P. Veitch
- Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA
- Northern California Institute for Research and Education (NCIRE), Department of Veterans Affairs Medical Center, San Francisco, CA, USA
| | - Paul Aisen
- Alzheimer’s Therapeutic Research Institute, University of Southern California, San Diego, CA, USA
| | - Miriam Ashford
- Northern California Institute for Research and Education (NCIRE), Department of Veterans Affairs Medical Center, San Francisco, CA, USA
| | - Godfrey Coker
- Alzheimer’s Therapeutic Research Institute, University of Southern California, San Diego, CA, USA
| | - Michael C. Donohue
- Alzheimer’s Therapeutic Research Institute, University of Southern California, San Diego, CA, USA
| | - Kenneth M. Langa
- Department of Internal Medicine, Institute for Social Research, and Veterans Affairs Center for Clinical Management Research, University of Michigan, Ann Arbor, MI, USA
| | - Garrett Miller
- Alzheimer’s Therapeutic Research Institute, University of Southern California, San Diego, CA, USA
- Division of Neurobiology, University of Southern California, San Diego, CA, USA
| | | | - Rema Raman
- Alzheimer’s Therapeutic Research Institute, University of Southern California, San Diego, CA, USA
| | - Rachel Nosheny
- Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA
- Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, CA, USA
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Tahami Monfared AA, Ye W, Sardesai A, Folse H, Chavan A, Kang K, Zhang Q. Estimated Societal Value of Lecanemab in Patients with Early Alzheimer's Disease Using Simulation Modeling. Neurol Ther 2023; 12:795-814. [PMID: 36929345 DOI: 10.1007/s40120-023-00460-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 03/03/2023] [Indexed: 03/18/2023] Open
Abstract
INTRODUCTION Alzheimer's disease (AD) is a progressive neurodegenerative disorder associated with memory, cognitive, and behavioral deficits, and brings significant economic burden on caregivers and healthcare systems. This study aims to estimate the long-term societal value of lecanemab plus standard of care (SoC) versus SoC alone, corresponding to a range of willingness-to-pay (WTP) thresholds based on the phase III CLARITY AD trial readouts from both the US payer and societal perspectives. METHODS An evidence-based model was developed to simulate the effects of lecanemab on disease progression in early AD using interconnected predictive equations based on longitudinal clinical and biomarker data derived from the Alzheimer's Disease Neuroimaging Initiative (ADNI). The model was informed with the results of the phase III CLARITY AD trial and published literature. Key model outcomes included patient life-years (LYs), quality-adjusted life-years (QALYs), and total costs of both the direct and indirect costs of patients and caregivers over a lifetime horizon. RESULTS Patients treated with lecanemab plus SoC gained an additional 0.62 years of life versus SoC alone (6.23 years vs. 5.61 years). The mean time on lecanemab was 3.91 years, and the treatment was associated with an increase in patient QALYs of 0.61 and an increase in total QALYs of 0.64 when both patient and caregiver utilities were considered. The model estimated that the annual value of lecanemab for the US payer perspective was US$18,709-35,678 ($19,710-37,351 for societal perspective) at the WTP threshold of $100,000-200,000 per QALY gained, respectively. Scenario analyses of patient subgroups, time horizon, input sources, treatment stopping rules, and treatment dosing were conducted to explore the impact of alternative assumptions on the model results. CONCLUSION The economic study suggested that lecanemab plus SoC would improve health and humanistic (quality of life) outcomes and reduce economic burden for patients and caregivers in early AD.
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Affiliation(s)
- Amir Abbas Tahami Monfared
- Eisai Inc., 200 Metro Blvd, Nutley, NJ, 07110, USA. .,McGill University, Epidemiology, Biostatistics, and Occupational Health, Montreal, QC, Canada.
| | - Weicheng Ye
- Evidence Synthesis, Modeling and Communication, Evidera Inc, Bethesda, MD, 20814, USA
| | - Aditya Sardesai
- Evidence Synthesis, Modeling and Communication, Evidera Inc, Bethesda, MD, 20814, USA
| | - Henri Folse
- Evidence Synthesis, Modeling and Communication, Evidera Inc, Bethesda, MD, 20814, USA
| | - Ameya Chavan
- Evidence Synthesis, Modeling and Communication, Evidera Inc, Bethesda, MD, 20814, USA
| | - Kang Kang
- Evidence Synthesis, Modeling and Communication, Evidera Inc, Bethesda, MD, 20814, USA
| | - Quanwu Zhang
- Eisai Inc., 200 Metro Blvd, Nutley, NJ, 07110, USA
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Penke B, Szűcs M, Bogár F. New Pathways Identify Novel Drug Targets for the Prevention and Treatment of Alzheimer’s Disease. Int J Mol Sci 2023; 24:ijms24065383. [PMID: 36982456 PMCID: PMC10049476 DOI: 10.3390/ijms24065383] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 03/06/2023] [Accepted: 03/09/2023] [Indexed: 03/18/2023] Open
Abstract
Alzheimer’s disease (AD) is an incurable, progressive neurodegenerative disorder. AD is a complex and multifactorial disease that is responsible for 60–80% of dementia cases. Aging, genetic factors, and epigenetic changes are the main risk factors for AD. Two aggregation-prone proteins play a decisive role in AD pathogenesis: β-amyloid (Aβ) and hyperphosphorylated tau (pTau). Both of them form deposits and diffusible toxic aggregates in the brain. These proteins are the biomarkers of AD. Different hypotheses have tried to explain AD pathogenesis and served as platforms for AD drug research. Experiments demonstrated that both Aβ and pTau might start neurodegenerative processes and are necessary for cognitive decline. The two pathologies act in synergy. Inhibition of the formation of toxic Aβ and pTau aggregates has been an old drug target. Recently, successful Aβ clearance by monoclonal antibodies has raised new hopes for AD treatments if the disease is detected at early stages. More recently, novel targets, e.g., improvements in amyloid clearance from the brain, application of small heat shock proteins (Hsps), modulation of chronic neuroinflammation by different receptor ligands, modulation of microglial phagocytosis, and increase in myelination have been revealed in AD research.
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Affiliation(s)
- Botond Penke
- Department of Medical Chemistry, University of Szeged, Dóm Square 8, H-6720 Szeged, Hungary
- Correspondence:
| | - Mária Szűcs
- Department of Medical Chemistry, University of Szeged, Dóm Square 8, H-6720 Szeged, Hungary
| | - Ferenc Bogár
- ELKH-SZTE Biomimetic Systems Research Group, Eötvös Loránd Research Network (ELKH), Dóm Square 8, H-6720 Szeged, Hungary
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Yin C, Harms AC, Hankemeier T, Kindt A, de Lange ECM. Status of Metabolomic Measurement for Insights in Alzheimer's Disease Progression-What Is Missing? Int J Mol Sci 2023; 24:ijms24054960. [PMID: 36902391 PMCID: PMC10003384 DOI: 10.3390/ijms24054960] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 02/24/2023] [Accepted: 03/02/2023] [Indexed: 03/08/2023] Open
Abstract
Alzheimer's disease (AD) is an aging-related neurodegenerative disease, leading to the progressive loss of memory and other cognitive functions. As there is still no cure for AD, the growth in the number of susceptible individuals represents a major emerging threat to public health. Currently, the pathogenesis and etiology of AD remain poorly understood, while no efficient treatments are available to slow down the degenerative effects of AD. Metabolomics allows the study of biochemical alterations in pathological processes which may be involved in AD progression and to discover new therapeutic targets. In this review, we summarized and analyzed the results from studies on metabolomics analysis performed in biological samples of AD subjects and AD animal models. Then this information was analyzed by using MetaboAnalyst to find the disturbed pathways among different sample types in human and animal models at different disease stages. We discuss the underlying biochemical mechanisms involved, and the extent to which they could impact the specific hallmarks of AD. Then we identify gaps and challenges and provide recommendations for future metabolomics approaches to better understand AD pathogenesis.
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Affiliation(s)
- Chunyuan Yin
- Metabolomics and Analytics Centre, Leiden Academic Centre for Drug Research, Leiden University, 2333 CC Leiden, The Netherlands
- Division of Systems Pharmacology and Pharmacy, Leiden Academic Centre for Drug Research, Leiden University, 2333 CC Leiden, The Netherlands
| | - Amy C. Harms
- Metabolomics and Analytics Centre, Leiden Academic Centre for Drug Research, Leiden University, 2333 CC Leiden, The Netherlands
| | - Thomas Hankemeier
- Metabolomics and Analytics Centre, Leiden Academic Centre for Drug Research, Leiden University, 2333 CC Leiden, The Netherlands
| | - Alida Kindt
- Metabolomics and Analytics Centre, Leiden Academic Centre for Drug Research, Leiden University, 2333 CC Leiden, The Netherlands
| | - Elizabeth C. M. de Lange
- Division of Systems Pharmacology and Pharmacy, Leiden Academic Centre for Drug Research, Leiden University, 2333 CC Leiden, The Netherlands
- Correspondence:
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Seibyl JP, DuBois JM, Racine A, Collins J, Guo Q, Wooten D, Stage E, Cheng D, Gunn RN, Porat L, Whittington A, Kuo PH, Ichise M, Comley R, Martarello L, Salinas C. A Visual Interpretation Algorithm for Assessing Brain Tauopathy with 18F-MK-6240 PET. J Nucl Med 2023; 64:444-451. [PMID: 36175137 PMCID: PMC10071795 DOI: 10.2967/jnumed.122.264371] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 09/21/2022] [Accepted: 09/21/2022] [Indexed: 11/16/2022] Open
Abstract
In vivo characterization of pathologic deposition of tau protein in the human brain by PET imaging is a promising tool in drug development trials of Alzheimer disease (AD). 6-(fluoro-18F)-3-(1H-pyrrolo[2,3-c]pyridin-1-yl)isoquinolin-5-amine (18F-MK-6240) is a radiotracer with high selectivity and subnanomolar affinity for neurofibrillary tangles that shows favorable nonspecific brain penetration and excellent kinetic properties. The purpose of the present investigation was to develop a visual assessment method that provides both an overall assessment of brain tauopathy and regional characterization of abnormal tau deposition. Methods: 18F-MK-6240 scans from 102 participants (including cognitively normal volunteers and patients with AD or other neurodegenerative disorders) were reviewed by an expert nuclear medicine physician masked to each participant's diagnosis to identify common patterns of brain uptake. This initial visual read method was field-tested in a separate, nonoverlapping cohort of 102 participants, with 2 additional naïve readers trained on the method. Visual read outcomes were compared with semiquantitative assessments using volume-of-interest SUV ratio. Results: For the visual read, the readers assessed 8 gray-matter regions per hemisphere as negative (no abnormal uptake) or positive (1%-25% of the region involved, 25%-75% involvement, or >75% involvement) and then characterized the tau binding pattern as positive or negative for evidence of tau and, if positive, whether brain uptake was in an AD pattern. The readers demonstrated agreement 94% of the time for overall positivity or negativity. Concordance on the determination of regional binary outcomes (negative or positive) showed agreement of 74.3% and a Fleiss κ of 0.912. Using clinical diagnosis as the ground truth, the readers demonstrated a sensitivity of 73%-79% and specificity of 91%-93%, with a combined reader-concordance sensitivity of 80% and specificity of 93%. The average SUV ratio in cortical regions showed a robust correlation with visually derived ratings of regional involvement (r = 0.73, P < 0.0001). Conclusion: We developed a visual read algorithm for 18F-MK-6240 PET offering determination of both scan positivity and the regional degree of cortical involvement. These cross-sectional results show strong interreader concordance on both binary and regional assessments of tau deposition, as well as good sensitivity and excellent specificity supporting use as a tool for clinical trials.
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Affiliation(s)
- John P Seibyl
- Institute for Neurodegenerative Disorders, New Haven, Connecticut;
- Invicro, New Haven, Connecticut
| | | | | | | | - Qi Guo
- AbbVie, North Chicago, Illinois
| | | | | | | | | | | | | | | | - Masanori Ichise
- National Institute of Radiological Sciences, National Institutes for Quantum and Radiological Science and Technology, Chiba, Japan
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Zhang G, Nie X, Liu B, Yuan H, Li J, Sun W, Huang S. A multimodal fusion method for Alzheimer's disease based on DCT convolutional sparse representation. Front Neurosci 2023; 16:1100812. [PMID: 36685238 PMCID: PMC9853298 DOI: 10.3389/fnins.2022.1100812] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Accepted: 12/07/2022] [Indexed: 01/07/2023] Open
Abstract
Introduction The medical information contained in magnetic resonance imaging (MRI) and positron emission tomography (PET) has driven the development of intelligent diagnosis of Alzheimer's disease (AD) and multimodal medical imaging. To solve the problems of severe energy loss, low contrast of fused images and spatial inconsistency in the traditional multimodal medical image fusion methods based on sparse representation. A multimodal fusion algorithm for Alzheimer' s disease based on the discrete cosine transform (DCT) convolutional sparse representation is proposed. Methods The algorithm first performs a multi-scale DCT decomposition of the source medical images and uses the sub-images of different scales as training images, respectively. Different sparse coefficients are obtained by optimally solving the sub-dictionaries at different scales using alternating directional multiplication method (ADMM). Secondly, the coefficients of high-frequency and low-frequency subimages are inverse DCTed using an improved L1 parametric rule combined with improved spatial frequency novel sum-modified SF (NMSF) to obtain the final fused images. Results and discussion Through extensive experimental results, we show that our proposed method has good performance in contrast enhancement, texture and contour information retention.
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Affiliation(s)
- Guo Zhang
- School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, China,School of Medical Information and Engineering, Southwest Medical University, Luzhou, China
| | - Xixi Nie
- Chongqing Key Laboratory of Image Cognition, College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Bangtao Liu
- School of Medical Information and Engineering, Southwest Medical University, Luzhou, China
| | - Hong Yuan
- School of Medical Information and Engineering, Southwest Medical University, Luzhou, China
| | - Jin Li
- School of Medical Information and Engineering, Southwest Medical University, Luzhou, China
| | - Weiwei Sun
- School of Optoelectronic Engineering, Chongqing University of Posts and Telecommunications, Chongqing, China,*Correspondence: Weiwei Sun,
| | - Shixin Huang
- School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, China,Department of Scientific Research, The People’s Hospital of Yubei District of Chongqing City, Yubei, China,Shixin Huang,
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Wang ML, Zou QQ, Sun Z, Wei XE, Li PY, Wu X, Li YH. Associations of MRI-visible perivascular spaces with longitudinal cognitive decline across the Alzheimer's disease spectrum. Alzheimers Res Ther 2022; 14:185. [PMID: 36514127 PMCID: PMC9746143 DOI: 10.1186/s13195-022-01136-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Accepted: 12/05/2022] [Indexed: 12/15/2022]
Abstract
OBJECTIVE To investigate the characteristics and associations of MRI-visible perivascular spaces (PVS) with clinical progression and longitudinal cognitive decline across the Alzheimer's disease spectrum. METHODS We included 1429 participants (641 [44.86%] female) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. PVS number and grade in the centrum semiovale (CSO-PVS), basal ganglia (BG-PVS), and hippocampus (HP-PVS) were compared among the control (CN), mild cognitive impairment (MCI), and Alzheimer's disease (AD) groups. PVS were tested as predictors of diagnostic progression (i.e., CN to MCI/AD or MCI to AD) and longitudinal changes in the 13-item Alzheimer's Disease Assessment Scale-cognitive subscale (ADAS-Cog 13), Mini-Mental State Examination (MMSE), memory (ADNI-MEM), and executive function (ADNI-EF) using multiple linear regression, linear mixed-effects, and Cox proportional hazards modeling. RESULTS Compared with CN subjects, MCI and AD subjects had more CSO-PVS, both in number (p < 0.001) and grade (p < 0.001). However, there was no significant difference in BG-PVS and HP-PVS across the AD spectrum (p > 0.05). Individuals with moderate and frequent/severe CSO-PVS had a higher diagnostic conversion risk than individuals with no/mild CSO-PVS (log-rank p < 0.001 for all) in the combined CN and MCI group. Further Cox regression analyses revealed that moderate and frequent/severe CSO-PVS were associated with a higher risk of diagnostic conversion (HR = 2.007, 95% CI = 1.382-2.914, p < 0.001; HR = 2.676, 95% CI = 1.830-3.911, p < 0.001, respectively). A higher CSO-PVS number was associated with baseline cognitive performance and longitudinal cognitive decline in all cognitive tests (p < 0.05 for all). CONCLUSIONS CSO-PVS were more common in MCI and AD and were associated with cognitive decline across the AD spectrum.
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Affiliation(s)
- Ming-Liang Wang
- grid.16821.3c0000 0004 0368 8293Department of Radiology, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, No. 600, Yi Shan Road, Shanghai, 200233 China
| | - Qiao-Qiao Zou
- grid.16821.3c0000 0004 0368 8293Department of Radiology, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, No. 600, Yi Shan Road, Shanghai, 200233 China
| | - Zheng Sun
- grid.16821.3c0000 0004 0368 8293Department of Radiology, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, No. 600, Yi Shan Road, Shanghai, 200233 China
| | - Xiao-Er Wei
- grid.16821.3c0000 0004 0368 8293Department of Radiology, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, No. 600, Yi Shan Road, Shanghai, 200233 China
| | - Peng-Yang Li
- grid.224260.00000 0004 0458 8737Division of Cardiology, Pauley Heart Center, Virginia Commonwealth University, Richmond, VA USA
| | - Xue Wu
- grid.266102.10000 0001 2297 6811Institute for Global Health Sciences, University of California San Francisco, San Francisco, CA USA
| | - Yue-Hua Li
- grid.16821.3c0000 0004 0368 8293Department of Radiology, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, No. 600, Yi Shan Road, Shanghai, 200233 China
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Liu Z, Guan R, Bu F, Pan L. Treatment of Alzheimer's disease by combination of acupuncture and Chinese medicine based on pathophysiological mechanism: A review. Medicine (Baltimore) 2022; 101:e32218. [PMID: 36626477 PMCID: PMC9750551 DOI: 10.1097/md.0000000000032218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/11/2023] Open
Abstract
Alzheimer's disease (AD) is a neurodegenerative disorder characterized by neurodegeneration, nerve loss, neurofibrillary tangles, and Aβ plaques. In modern medical science, there has been a serious obstacle to the effective treatment of AD. At present, there is no clinically proven and effective western medicine treatment for AD. The reason is that the etiology of AD is not yet fully understood. In 2018, the international community put forward a purely biological definition of AD, but soon this view of biomarkers was widely questioned, because the so-called AD biomarkers are shared with other neurological diseases, the diagnostic accuracy is low, and they face various challenges in the process of clinical diagnosis and treatment. Nowadays, scholars increasingly regard AD as the result of multimechanism and multicenter interaction. Because there is no exact Western medicine treatment for AD, the times call for the comprehensive treatment of AD in traditional Chinese medicine (TCM). AD belongs to the category of "dull disease" in TCM. For thousands of years, TCM has accumulated a lot of relevant treatment experience in the process of diagnosis and treatment. TCM, acupuncture, and the combination of acupuncture and medicine all play an important role in the treatment of AD. Based on the research progress of modern medicine on the pathophysiology of AD, this paper discusses the treatment of this disease with the combination of acupuncture and medicine.
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Affiliation(s)
- Zhao Liu
- Heilongjiang University of Traditional Chinese Medicine, Harbin, Heilongjiang Province, China
- * Correspondence: Zhao Liu, Heilongjiang University of Traditional Chinese Medicine, 24 Heping Road, Harbin, Heilongjiang Province 150006, China (e-mail: )
| | - Ruiqian Guan
- Heilongjiang University of Traditional Chinese Medicine, Harbin, Heilongjiang Province, China
- Second Affiliated Hospital of Heilongjiang University of Traditional Chinese Medicine, Harbin, Heilongjiang Province, China
| | - Fan Bu
- Heilongjiang University of Traditional Chinese Medicine, Harbin, Heilongjiang Province, China
| | - Limin Pan
- Heilongjiang University of Traditional Chinese Medicine, Harbin, Heilongjiang Province, China
- Second Affiliated Hospital of Heilongjiang University of Traditional Chinese Medicine, Harbin, Heilongjiang Province, China
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Wang HF, Zhang W, Rolls ET, Li Y, Wang L, Ma YH, Kang J, Feng J, Yu JT, Cheng W. Hearing impairment is associated with cognitive decline, brain atrophy and tau pathology. EBioMedicine 2022; 86:104336. [PMID: 36356475 PMCID: PMC9649369 DOI: 10.1016/j.ebiom.2022.104336] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 10/01/2022] [Accepted: 10/17/2022] [Indexed: 11/09/2022] Open
Abstract
BACKGROUND Hearing impairment was recently identified as the most prominent risk factor for dementia. However, the mechanisms underlying the link between hearing impairment and dementia are still unclear. METHODS We investigated the association of hearing performance with cognitive function, brain structure and cerebrospinal fluid (CSF) proteins in cross-sectional, longitudinal, mediation and genetic association analyses across the UK Biobank (N = 165,550), the Chinese Alzheimer's Biomarker and Lifestyle (CABLE, N = 863) study, and the Alzheimer's Disease Neuroimaging Initiative (ADNI, N = 1770) database. FINDINGS Poor hearing performance was associated with worse cognitive function in the UK Biobank and in the CABLE study. Hearing impairment was significantly related to lower volume of temporal cortex, hippocampus, inferior parietal lobe, precuneus, etc., and to lower integrity of white matter (WM) tracts. Furthermore, a higher polygenic risk score (PRS) for hearing impairment was strongly associated with lower cognitive function, lower volume of gray matter, and lower integrity of WM tracts. Moreover, hearing impairment was correlated with a high level of CSF tau protein in the CABLE study and in the ADNI database. Finally, mediation analyses showed that brain atrophy and tau pathology partly mediated the association between hearing impairment and cognitive decline. INTERPRETATION Hearing impairment is associated with cognitive decline, brain atrophy and tau pathology, and hearing impairment may reflect the risk for cognitive decline and dementia as it is related to bran atrophy and tau accumulation in brain. However, it is necessary to assess the mechanism in future animal studies. FUNDING A full list of funding bodies that supported this study can be found in the Acknowledgements section.
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Affiliation(s)
- Hui-Fu Wang
- Department of Neurology, Huashan Hospital, Institute of Science and Technology for Brain-Inspired Intelligence, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China; Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Wei Zhang
- Department of Neurology, Huashan Hospital, Institute of Science and Technology for Brain-Inspired Intelligence, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Edmund T Rolls
- Department of Neurology, Huashan Hospital, Institute of Science and Technology for Brain-Inspired Intelligence, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China; Department of Computer Science, University of Warwick, Coventry, CV4 7AL, UK; Oxford Centre for Computational Neuroscience, Oxford, UK
| | | | - Yuzhu Li
- Department of Neurology, Huashan Hospital, Institute of Science and Technology for Brain-Inspired Intelligence, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Linbo Wang
- Department of Neurology, Huashan Hospital, Institute of Science and Technology for Brain-Inspired Intelligence, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Ya-Hui Ma
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Jujiao Kang
- Department of Neurology, Huashan Hospital, Institute of Science and Technology for Brain-Inspired Intelligence, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Jianfeng Feng
- Department of Neurology, Huashan Hospital, Institute of Science and Technology for Brain-Inspired Intelligence, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China; Zhangjiang Fudan International Innovation Center, Shanghai, China; Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Zhejiang, China
| | - Jin-Tai Yu
- Department of Neurology, Huashan Hospital, Institute of Science and Technology for Brain-Inspired Intelligence, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.
| | - Wei Cheng
- Department of Neurology, Huashan Hospital, Institute of Science and Technology for Brain-Inspired Intelligence, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China; Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Zhejiang, China.
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Park C, Jang JW, Joo G, Kim Y, Kim S, Byeon G, Park SW, Kasani PH, Yum S, Pyun JM, Park YH, Lim JS, Youn YC, Choi HS, Park C, Im H, Kim S. Predicting progression to dementia with “comprehensive visual rating scale” and machine learning algorithms. Front Neurol 2022; 13:906257. [PMID: 36071894 PMCID: PMC9443667 DOI: 10.3389/fneur.2022.906257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 07/14/2022] [Indexed: 11/17/2022] Open
Abstract
Background and Objective Identifying biomarkers for predicting progression to dementia in patients with mild cognitive impairment (MCI) is crucial. To this end, the comprehensive visual rating scale (CVRS), which is based on magnetic resonance imaging (MRI), was developed for the assessment of structural changes in the brains of patients with MCI. This study aimed to investigate the use of the CVRS score for predicting dementia in patients with MCI over a 2-year follow-up period using various machine learning (ML) algorithms. Methods We included 197 patients with MCI who were followed up more than once. The data used for this study were obtained from the Japanese-Alzheimer's Disease Neuroimaging Initiative study. We assessed all the patients using their CVRS scores, cortical thickness data, and clinical data to determine their progression to dementia during a follow-up period of over 2 years. ML algorithms, such as logistic regression, random forest (RF), XGBoost, and LightGBM, were applied to the combination of the dataset. Further, feature importance that contributed to the progression from MCI to dementia was analyzed to confirm the risk predictors among the various variables evaluated. Results Of the 197 patients, 108 (54.8%) showed progression from MCI to dementia. Tree-based classifiers, such as XGBoost, LightGBM, and RF, achieved relatively high performance. In addition, the prediction models showed better performance when clinical data and CVRS score (accuracy 0.701–0.711) were used than when clinical data and cortical thickness (accuracy 0.650–0.685) were used. The features related to CVRS helped predict progression to dementia using the tree-based models compared to logistic regression. Conclusions Tree-based ML algorithms can predict progression from MCI to dementia using baseline CVRS scores combined with clinical data.
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Affiliation(s)
- Chaeyoon Park
- Department of Convergence Security, Kangwon National University, Chuncheon, South Korea
| | - Jae-Won Jang
- Department of Convergence Security, Kangwon National University, Chuncheon, South Korea
- Department of Neurology, Kangwon National University Hospital, Kangwon National University College of Medicine, Chuncheon, South Korea
- Interdisciplinary Graduate Program in Medical Bigdata Convergence, Chuncheon, South Korea
| | - Gihun Joo
- Interdisciplinary Graduate Program in Medical Bigdata Convergence, Chuncheon, South Korea
| | - Yeshin Kim
- Department of Neurology, Kangwon National University Hospital, Kangwon National University College of Medicine, Chuncheon, South Korea
| | - Seongheon Kim
- Department of Neurology, Kangwon National University Hospital, Kangwon National University College of Medicine, Chuncheon, South Korea
| | - Gihwan Byeon
- Department of Psychiatry, Kangwon National University Hospital, Kangwon National University College of Medicine, Chuncheon, South Korea
| | - Sang Won Park
- Interdisciplinary Graduate Program in Medical Bigdata Convergence, Chuncheon, South Korea
| | | | - Sujin Yum
- Interdisciplinary Graduate Program in Medical Bigdata Convergence, Chuncheon, South Korea
| | - Jung-Min Pyun
- Department of Neurology, Soonchunhyang University Seoul Hospital, Soonchunhyang University College of Medicine, Seoul, South Korea
| | - Young Ho Park
- Department of Neurology, Seoul National University Bundang Hospital, Seongnam, South Korea
- Department of Neurology, Seoul National University College of Medicine, Seoul, South Korea
| | - Jae-Sung Lim
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
| | - Young Chul Youn
- Department of Neurology, Chung-Ang University Hospital, Chung-Ang University College of Medicine, Seoul, South Korea
| | - Hyun-Soo Choi
- Interdisciplinary Graduate Program in Medical Bigdata Convergence, Chuncheon, South Korea
- Department of Computer Science and Engineering, Kangwon National University, Chuncheon, South Korea
| | - Chihyun Park
- Interdisciplinary Graduate Program in Medical Bigdata Convergence, Chuncheon, South Korea
- Department of Computer Science and Engineering, Kangwon National University, Chuncheon, South Korea
| | - Hyeonseung Im
- Department of Convergence Security, Kangwon National University, Chuncheon, South Korea
- Interdisciplinary Graduate Program in Medical Bigdata Convergence, Chuncheon, South Korea
- Department of Computer Science and Engineering, Kangwon National University, Chuncheon, South Korea
- *Correspondence: Hyeonseung Im
| | - SangYun Kim
- Department of Neurology, Seoul National University Bundang Hospital, Seongnam, South Korea
- Department of Neurology, Seoul National University College of Medicine, Seoul, South Korea
- SangYun Kim
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Cervenka S, Frick A, Bodén R, Lubberink M. Application of positron emission tomography in psychiatry-methodological developments and future directions. Transl Psychiatry 2022; 12:248. [PMID: 35701411 PMCID: PMC9198063 DOI: 10.1038/s41398-022-01990-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 05/20/2022] [Accepted: 05/25/2022] [Indexed: 11/09/2022] Open
Abstract
Mental disorders represent an increasing source of disability and high costs for societies globally. Molecular imaging techniques such as positron emission tomography (PET) represent powerful tools with the potential to advance knowledge regarding disease mechanisms, allowing the development of new treatment approaches. Thus far, most PET research on pathophysiology in psychiatric disorders has focused on the monoaminergic neurotransmission systems, and although a series of discoveries have been made, the results have not led to any material changes in clinical practice. We outline areas of methodological development that can address some of the important obstacles to fruitful progress. First, we point towards new radioligands and targets that can lead to the identification of processes upstream, or parallel to disturbances in monoaminergic systems. Second, we describe the development of new methods of PET data quantification and PET systems that may facilitate research in psychiatric populations. Third, we review the application of multimodal imaging that can link molecular imaging data to other aspects of brain function, thus deepening our understanding of disease processes. Fourth, we highlight the need to develop imaging study protocols to include longitudinal and interventional paradigms, as well as frameworks to assess dimensional symptoms such that the field can move beyond cross-sectional studies within current diagnostic boundaries. Particular effort should be paid to include also the most severely ill patients. Finally, we discuss the importance of harmonizing data collection and promoting data sharing to reach the desired sample sizes needed to fully capture the phenotype of psychiatric conditions.
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Affiliation(s)
- Simon Cervenka
- Department of Medical Sciences, Psychiatry, Uppsala University, Uppsala, Sweden. .,Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet and Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden.
| | - Andreas Frick
- grid.8993.b0000 0004 1936 9457Department of Medical Sciences, Psychiatry, Uppsala University, Uppsala, Sweden
| | - Robert Bodén
- grid.8993.b0000 0004 1936 9457Department of Medical Sciences, Psychiatry, Uppsala University, Uppsala, Sweden
| | - Mark Lubberink
- grid.8993.b0000 0004 1936 9457Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
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Giliberto L. Editorial: Degenerative and cognitive diseases. Curr Opin Neurol 2022; 35:208-211. [PMID: 35232933 DOI: 10.1097/wco.0000000000001037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Affiliation(s)
- Luca Giliberto
- Litwin-Zucker Center for the Study of Alzheimer's Diseases and Memory Disorders, Feinstein Institutes for Medical Research and Institute for Neurology and Neurosurgery, Northwell Health System, Manhasset, New York, USA
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