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Sanchez T, Esteban O, Gomez Y, Pron A, Koob M, Dunet V, Girard N, Jakab A, Eixarch E, Auzias G, Bach Cuadra M. FetMRQC: A robust quality control system for multi-centric fetal brain MRI. Med Image Anal 2024; 97:103282. [PMID: 39053168 DOI: 10.1016/j.media.2024.103282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 06/28/2024] [Accepted: 07/15/2024] [Indexed: 07/27/2024]
Abstract
Fetal brain MRI is becoming an increasingly relevant complement to neurosonography for perinatal diagnosis, allowing fundamental insights into fetal brain development throughout gestation. However, uncontrolled fetal motion and heterogeneity in acquisition protocols lead to data of variable quality, potentially biasing the outcome of subsequent studies. We present FetMRQC, an open-source machine-learning framework for automated image quality assessment and quality control that is robust to domain shifts induced by the heterogeneity of clinical data. FetMRQC extracts an ensemble of quality metrics from unprocessed anatomical MRI and combines them to predict experts' ratings using random forests. We validate our framework on a pioneeringly large and diverse dataset of more than 1600 manually rated fetal brain T2-weighted images from four clinical centers and 13 different scanners. Our study shows that FetMRQC's predictions generalize well to unseen data while being interpretable. FetMRQC is a step towards more robust fetal brain neuroimaging, which has the potential to shed new insights on the developing human brain.
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Affiliation(s)
- Thomas Sanchez
- CIBM - Center for Biomedical Imaging, Switzerland; Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
| | - Oscar Esteban
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Yvan Gomez
- BCNatal Fetal Medicine Research Center (Hospital Clínic and Hospital Sant Joan de Déu), Universitat de Barcelona, Spain; Department Woman-Mother-Child, CHUV, Lausanne, Switzerland
| | - Alexandre Pron
- Aix-Marseille Université, CNRS, Institut de Neurosciences de La Timone, Marseilles, France
| | - Mériam Koob
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Vincent Dunet
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Nadine Girard
- Aix-Marseille Université, CNRS, Institut de Neurosciences de La Timone, Marseilles, France; Service de Neuroradiologie Diagnostique et Interventionnelle, Hôpital Timone, AP-HM, Marseilles, France
| | - Andras Jakab
- Center for MR Research, University Children's Hospital Zurich, University of Zurich, Zurich, Switzerland; Neuroscience Center Zurich, University of Zurich, Zurich, Switzerland; Research Priority Project Adaptive Brain Circuits in Development and Learning (AdaBD), University of Zürich, Zurich, Switzerland
| | - Elisenda Eixarch
- BCNatal Fetal Medicine Research Center (Hospital Clínic and Hospital Sant Joan de Déu), Universitat de Barcelona, Spain; IDIBAPS and CIBERER, Barcelona, Spain
| | - Guillaume Auzias
- Aix-Marseille Université, CNRS, Institut de Neurosciences de La Timone, Marseilles, France
| | - Meritxell Bach Cuadra
- CIBM - Center for Biomedical Imaging, Switzerland; Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
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Bachmann D, Saake A, Studer S, Buchmann A, Rauen K, Gruber E, Michels L, Nitsch RM, Hock C, Gietl A, Treyer V. Hypertension and cerebral blood flow in the development of Alzheimer's disease. Alzheimers Dement 2024. [PMID: 39254220 DOI: 10.1002/alz.14233] [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: 02/24/2024] [Revised: 08/07/2024] [Accepted: 08/09/2024] [Indexed: 09/11/2024]
Abstract
INTRODUCTION We investigated the interactive associations between amyloid and hypertension on the entorhinal cortex (EC) tau and atrophy and the role of cerebral blood flow (CBF) as a shared mechanism by which amyloid and hypertension contribute to EC tau and regional white matter hyperintensities (WMHs). METHODS We analyzed data from older adults without dementia participating in the Add-Tau study (NCT02958670, n = 138) or Alzheimer's Disease Neuroimaging Initiative (ADNI) (n = 523) who had available amyloid-positron emission tomography (PET), tau-PET, fluid-attenuated inversion recovery (FLAIR), and T1-weighted magnetic resonance imaging (MRI). A subsample in both cohorts had available arterial spin labeling (ASL) MRI (Add-Tau: n = 78; ADNI: n = 89). RESULTS The detrimental effects of hypertension on AD pathology and EC thickness were more pronounced in the Add-Tau cohort. Increased amyloid burden was associated with decreased occipital gray matter CBF in the ADNI cohort. In both cohorts, lower regional gray matter CBF was associated with higher EC tau and posterior WMH burden. DISCUSSION Reduced cerebral perfusion may be one common mechanism through which hypertension and amyloid are related to increased EC tau and WMH volume. HIGHLIGHTS Hypertension is associated with increased entorhinal cortex (EC) tau, particularly in the presence of amyloid. Decreased cortical cerebral blood flow (CBF) is associated with higher regional white matter hyperintensity volume. Increasing amyloid burden is associated with decreasing CBF in the occipital lobe. MTL CBF and amyloid are synergistically associated with EC tau.
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Affiliation(s)
- Dario Bachmann
- Institute for Regenerative Medicine, University of Zurich, Zurich, Switzerland
- Department of Health Sciences and Technology, ETH Zürich, Zurich, Switzerland
| | - Antje Saake
- Institute for Regenerative Medicine, University of Zurich, Zurich, Switzerland
| | - Sandro Studer
- Institute for Regenerative Medicine, University of Zurich, Zurich, Switzerland
| | - Andreas Buchmann
- Institute for Regenerative Medicine, University of Zurich, Zurich, Switzerland
| | - Katrin Rauen
- Institute for Regenerative Medicine, University of Zurich, Zurich, Switzerland
- Department of Geriatric Psychiatry, Psychiatric Hospital Zurich, Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich, Zurich, Switzerland
| | - Esmeralda Gruber
- Institute for Regenerative Medicine, University of Zurich, Zurich, Switzerland
| | - Lars Michels
- Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich, Zurich, Switzerland
| | - Roger M Nitsch
- Institute for Regenerative Medicine, University of Zurich, Zurich, Switzerland
- Neurimmune, Zurich, Switzerland
| | - Christoph Hock
- Institute for Regenerative Medicine, University of Zurich, Zurich, Switzerland
- Neurimmune, Zurich, Switzerland
| | - Anton Gietl
- Institute for Regenerative Medicine, University of Zurich, Zurich, Switzerland
- Department of Geriatric Psychiatry, Psychiatric Hospital Zurich, Zurich, Switzerland
| | - Valerie Treyer
- Institute for Regenerative Medicine, University of Zurich, Zurich, Switzerland
- Department of Nuclear Medicine, University Hospital of Zurich, University of Zurich, Zurich, Switzerland
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Sackl M, Tinauer C, Urschler M, Enzinger C, Stollberger R, Ropele S. Fully Automated Hippocampus Segmentation using T2-informed Deep Convolutional Neural Networks. Neuroimage 2024; 298:120767. [PMID: 39103064 DOI: 10.1016/j.neuroimage.2024.120767] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 07/26/2024] [Accepted: 07/31/2024] [Indexed: 08/07/2024] Open
Abstract
Hippocampal atrophy (tissue loss) has become a fundamental outcome parameter in clinical trials on Alzheimer's disease. To accurately estimate hippocampus volume and track its volume loss, a robust and reliable segmentation is essential. Manual hippocampus segmentation is considered the gold standard but is extensive, time-consuming, and prone to rater bias. Therefore, it is often replaced by automated programs like FreeSurfer, one of the most commonly used tools in clinical research. Recently, deep learning-based methods have also been successfully applied to hippocampus segmentation. The basis of all approaches are clinically used T1-weighted whole-brain MR images with approximately 1 mm isotropic resolution. However, such T1 images show low contrast-to-noise ratios (CNRs), particularly for many hippocampal substructures, limiting delineation reliability. To overcome these limitations, high-resolution T2-weighted scans are suggested for better visualization and delineation, as they show higher CNRs and usually allow for higher resolutions. Unfortunately, such time-consuming T2-weighted sequences are not feasible in a clinical routine. We propose an automated hippocampus segmentation pipeline leveraging deep learning with T2-weighted MR images for enhanced hippocampus segmentation of clinical T1-weighted images based on a series of 3D convolutional neural networks and a specifically acquired multi-contrast dataset. This dataset consists of corresponding pairs of T1- and high-resolution T2-weighted images, with the T2 images only used to create more accurate manual ground truth annotations and to train the segmentation network. The T2-based ground truth labels were also used to evaluate all experiments by comparing the masks visually and by various quantitative measures. We compared our approach with four established state-of-the-art hippocampus segmentation algorithms (FreeSurfer, ASHS, HippoDeep, HippMapp3r) and demonstrated a superior segmentation performance. Moreover, we found that the automated segmentation of T1-weighted images benefits from the T2-based ground truth data. In conclusion, this work showed the beneficial use of high-resolution, T2-based ground truth data for training an automated, deep learning-based hippocampus segmentation and provides the basis for a reliable estimation of hippocampal atrophy in clinical studies.
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Affiliation(s)
- Maximilian Sackl
- Department of Neurology, Medical University of Graz, Austria; BioTechMed-Graz, Austria
| | | | - Martin Urschler
- Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Austria; BioTechMed-Graz, Austria
| | | | - Rudolf Stollberger
- Institute of Biomedical Imaging, Graz University of Technology, Austria; BioTechMed-Graz, Austria
| | - Stefan Ropele
- Department of Neurology, Medical University of Graz, Austria; BioTechMed-Graz, Austria.
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Beckett LA, Saito N, Donohue MC, Harvey DJ. Contributions of the ADNI Biostatistics Core. Alzheimers Dement 2024. [PMID: 39140601 DOI: 10.1002/alz.14159] [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: 04/26/2024] [Revised: 06/18/2024] [Accepted: 06/20/2024] [Indexed: 08/15/2024]
Abstract
The goal of the Biostatistics Core of the Alzheimer's Disease Neuroimaging Initiative (ADNI) has been to ensure that sound study designs and statistical methods are used to meet the overall goals of ADNI. We have supported the creation of a well-validated and well-curated longitudinal database of clinical and biomarker information on ADNI participants and helped to make this accessible and usable for researchers. We have developed a statistical methodology for characterizing the trajectories of clinical and biomarker change for ADNI participants across the spectrum from cognitively normal to dementia, including multivariate patterns and evidence for heterogeneity in cognitive aging. We have applied these methods and adapted them to improve clinical trial design. ADNI-4 will offer us a chance to help extend these efforts to a more diverse cohort with an even richer panel of biomarker data to support better knowledge of and treatment for Alzheimer's disease and related dementias. HIGHLIGHTS: The Alzheimer's Disease Neuroimaging Initiative (ADNI) Biostatistics Core provides study design and analytic support to ADNI investigators. Core members develop and apply novel statistical methodology to work with ADNI data and support clinical trial design. The Core contributes to the standardization, validation, and harmonization of biomarker data. The Core serves as a resource to the wider research community to address questions related to the data and study as a whole.
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Affiliation(s)
- Laurel A Beckett
- Department of Public Health Sciences, University of California, Davis, California, USA
| | - Naomi Saito
- Department of Public Health Sciences, University of California, Davis, California, USA
| | - Michael C Donohue
- Department of Neurology, University of Southern California, Los Angeles, California, USA
| | - Danielle J Harvey
- Department of Public Health Sciences, University of California, Davis, California, USA
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Yassin W, Green J, Keshavan M, Del Re EC, Addington J, Bearden CE, Cadenhead KS, Cannon TD, Cornblatt BA, Mathalon DH, Perkins DO, Walker EF, Woods SW, Stone WS. Cognitive subtypes in youth at clinical high risk for psychosis. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.08.07.24311240. [PMID: 39211862 PMCID: PMC11361220 DOI: 10.1101/2024.08.07.24311240] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/04/2024]
Abstract
Introduction Schizophrenia is a mental health condition that severely impacts well-being. Cognitive impairment is among its core features, often presenting well before the onset of overt psychosis, underscoring a critical need to study it in the psychosis proneness (clinical high risk; CHR) stage, to maximize the benefits of interventions and to improve clinical outcomes. However, given the heterogeneity of cognitive impairment in this population, a one-size-fits-all approach to therapeutic interventions would likely be insufficient. Thus, identifying cognitive subtypes in this population is crucial for tailored and successful therapeutic interventions. Here we identify, validate, and characterize cognitive subtypes in large CHR samples and delineate their baseline and longitudinal cognitive and functional trajectories. Methods Using machine learning, we performed cluster analysis on cognitive measures in a large sample of CHR youth (n = 764), and demographically comparable controls (HC; n = 280) from the North American Prodrome Longitudinal Study (NAPLS) 2, and independently validated our findings with an equally large sample (NAPLS 3; n = 628 CHR, 84 HC). By utilizing several statistical approaches, we compared the clusters on cognition and functioning at baseline, and over 24 months of followup. We further delineate the conversion status within those clusters. Results Two main cognitive clusters were identified, "impaired" and "intact" across all cognitive domains in CHR compared to HC. Baseline differences between the cognitively intact cluster and HC were found in the verbal abilities and attention and working memory domains. Longitudinally, those in the cognitively impaired cluster group demonstrated an overall floor effect and did not deteriorate further over time. However, a "catch up" trajectory was observed in the attention and working memory domain. This group had higher instances of conversion overall, with these converters having significantly more non-affective psychotic disorder diagnosis versus bipolar disorder, than those with intact cognition. In the cognitively intact group, we observed differences in trajectory based on conversion status, where those who start with intact cognition and later convert demonstrate a sharp decline in attention and functioning. Functioning was significantly better in the cognitively intact than in the impaired group at baseline. Most of the cognitive trajectories demonstrate a positive relationship with functional ones. Conclusion Our findings provide evidence for intact and impaired cognitive subtypes in youth at CHR, independent of conversion status. They further indicate that attention and working memory are important to distinguish between the CHR with intact cognition and controls. The cognitively intact CHR group becomes less attentive after conversion, while the cognitively impaired one demonstrates a catch up trajectory on both attention and working memory. Overall, early evaluation, covering several cognitive domains, is crucial for identifying trajectories of improvement and deterioration for the purpose of tailoring intervention for improving outcomes in individuals at CHR for psychosis.
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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; 40:692-698. [PMID: 38888066 DOI: 10.1002/kjm2.12870] [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: 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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Hu F, Lucas A, Chen AA, Coleman K, Horng H, Ng RWS, Tustison NJ, Davis KA, Shou H, Li M, Shinohara RT. DeepComBat: A statistically motivated, hyperparameter-robust, deep learning approach to harmonization of neuroimaging data. Hum Brain Mapp 2024; 45:e26708. [PMID: 39056477 PMCID: PMC11273293 DOI: 10.1002/hbm.26708] [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: 02/26/2024] [Revised: 04/19/2024] [Accepted: 04/25/2024] [Indexed: 07/28/2024] Open
Abstract
Neuroimaging data acquired using multiple scanners or protocols are increasingly available. However, such data exhibit technical artifacts across batches which introduce confounding and decrease reproducibility. This is especially true when multi-batch data are analyzed using complex downstream models which are more likely to pick up on and implicitly incorporate batch-related information. Previously proposed image harmonization methods have sought to remove these batch effects; however, batch effects remain detectable in the data after applying these methods. We present DeepComBat, a deep learning harmonization method based on a conditional variational autoencoder and the ComBat method. DeepComBat combines the strengths of statistical and deep learning methods in order to account for the multivariate relationships between features while simultaneously relaxing strong assumptions made by previous deep learning harmonization methods. As a result, DeepComBat can perform multivariate harmonization while preserving data structure and avoiding the introduction of synthetic artifacts. We apply this method to cortical thickness measurements from a cognitive-aging cohort and show DeepComBat qualitatively and quantitatively outperforms existing methods in removing batch effects while preserving biological heterogeneity. Additionally, DeepComBat provides a new perspective for statistically motivated deep learning harmonization methods.
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Affiliation(s)
- Fengling Hu
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and InformaticsPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Alfredo Lucas
- Center for Neuroengineering and Therapeutics, Department of EngineeringUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Andrew A. Chen
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and InformaticsPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Kyle Coleman
- Statistical Center for Single‐Cell and Spatial GenomicsPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Hannah Horng
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and InformaticsPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Raymond W. S. Ng
- Perelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Nicholas J. Tustison
- Department of Radiology and Medical ImagingUniversity of VirginiaCharlottesvilleVirginiaUSA
| | - Kathryn A. Davis
- Center for Neuroengineering and Therapeutics, Department of EngineeringUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of NeurologyPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Haochang Shou
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and InformaticsPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Center for Biomedical Image Computing and Analytics (CBICA)Perelman School of MedicinePhiladelphiaPennsylvaniaUSA
| | - Mingyao Li
- Statistical Center for Single‐Cell and Spatial GenomicsPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Russell T. Shinohara
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and InformaticsPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Center for Biomedical Image Computing and Analytics (CBICA)Perelman School of MedicinePhiladelphiaPennsylvaniaUSA
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Wang MB, Rahmani F, Benzinger TLS, Raji CA. Edge Density Imaging Identifies White Matter Biomarkers of Late-Life Obesity and Cognition. Aging Dis 2024; 15:1899-1912. [PMID: 37196133 PMCID: PMC11272213 DOI: 10.14336/ad.2022.1210] [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: 08/13/2022] [Accepted: 12/10/2022] [Indexed: 05/19/2023] Open
Abstract
Alzheimer disease (AD) and obesity are related to disruptions in the white matter (WM) connectome. We examined the link between the WM connectome and obesity and AD through edge-density imaging/index (EDI), a tractography-based method that characterizes the anatomical embedding of tractography connections. A total of 60 participants, 30 known to convert from normal cognition or mild-cognitive impairment to AD within a minimum of 24 months of follow up, were selected from the Alzheimer disease Neuroimaging Initiative (ADNI). Diffusion-weighted MR images from the baseline scans were used to extract fractional anisotropy (FA) and EDI maps that were subsequently averaged using deterministic WM tractography based on the Desikan-Killiany atlas. Multiple linear and logistic regression analysis were used to identify the weighted sum of tract-specific FA or EDI indices that maximized correlation to body-mass-index (BMI) or conversion to AD. Participants from the Open Access Series of Imaging Studies (OASIS) were used as an independent validation for the BMI findings. The edge-density rich, periventricular, commissural and projection fibers were among the most important WM tracts linking BMI to FA as well as to EDI. WM fibers that contributed significantly to the regression model related to BMI overlapped with those that predicted conversion; specifically in the frontopontine, corticostriatal, and optic radiation pathways. These results were replicated by testing the tract-specific coefficients found using ADNI in the OASIS-4 dataset. WM mapping with EDI enables identification of an abnormal connectome implicated in both obesity and conversion to AD.
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Affiliation(s)
- Maxwell Bond Wang
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA.
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA.
- Medical Scientist Training Program, University of Pittsburgh/Carnegie Mellon University, Pittsburgh, PA, USA.
| | - Farzaneh Rahmani
- Mallinckrodt Institute of Radiology, Division of Neuroradiology, Washington University in St. Louis, St. Louis, MO, USA.
- Charles F. and Joanne Knight Alzheimer Disease Research Center (Knight ADRC), Washington University, St. Louis, Missouri, USA.
| | - Tammie L. S Benzinger
- Mallinckrodt Institute of Radiology, Division of Neuroradiology, Washington University in St. Louis, St. Louis, MO, USA.
- Charles F. and Joanne Knight Alzheimer Disease Research Center (Knight ADRC), Washington University, St. Louis, Missouri, USA.
| | - Cyrus A Raji
- Mallinckrodt Institute of Radiology, Division of Neuroradiology, Washington University in St. Louis, St. Louis, MO, USA.
- Charles F. and Joanne Knight Alzheimer Disease Research Center (Knight ADRC), Washington University, St. Louis, Missouri, USA.
- Department of Neurology, Washington University in Saint Louis, St. Louis, Missouri, USA
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9
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Lin J, Li Z, Zeng Y, Liu X, Li L, Jahanshad N, Ge X, Zhang D, Lu M, Liu M. Harmonizing three-dimensional MRI using pseudo-warping field guided GAN. Neuroimage 2024; 295:120635. [PMID: 38729542 DOI: 10.1016/j.neuroimage.2024.120635] [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: 02/26/2024] [Revised: 04/15/2024] [Accepted: 05/02/2024] [Indexed: 05/12/2024] Open
Abstract
In pursuit of cultivating automated models for magnetic resonance imaging (MRI) to aid in diagnostics, an escalating demand for extensive, multisite, and heterogeneous brain imaging datasets has emerged. This potentially introduces biased outcomes when directly applied for subsequent analysis. Researchers have endeavored to address this issue by pursuing the harmonization of MRIs. However, most existing image-based harmonization methods for MRI are tailored for 2D slices, which may introduce inter-slice variations when they are combined into a 3D volume. In this study, we aim to resolve inconsistencies between slices by introducing a pseudo-warping field. This field is created randomly and utilized to transform a slice into an artificially warped subsequent slice. The objective of this pseudo-warping field is to ensure that generators can consistently harmonize adjacent slices to another domain, without being affected by the varying content present in different slices. Furthermore, we construct unsupervised spatial and recycle loss to enhance the spatial accuracy and slice-wise consistency across the 3D images. The results demonstrate that our model effectively mitigates inter-slice variations and successfully preserves the anatomical details of the images during the harmonization process. Compared to generative harmonization models that employ 3D operators, our model exhibits greater computational efficiency and flexibility.
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Affiliation(s)
- Jiaying Lin
- Department of Biomedical Engineering, Sun Yat-sen University, Shenzhen 518107, China.
| | - Zhuoshuo Li
- Department of Biomedical Engineering, Sun Yat-sen University, Shenzhen 518107, China.
| | - Youbing Zeng
- Department of Biomedical Engineering, Sun Yat-sen University, Shenzhen 518107, China.
| | - Xiaobo Liu
- Department of Biomedical Engineering, Sun Yat-sen University, Shenzhen 518107, China.
| | - Liang Li
- Genuine Digital Technology Co., Ltd., Xi'an, China.
| | - Neda Jahanshad
- Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
| | - Xinting Ge
- School of Information Science and Engineering, Shandong Normal University, Shandong 250358, China.
| | - Dan Zhang
- School of Cyber Science and Engineering, Ningbo University of Technology, Zhejiang 315211, China.
| | - Minhua Lu
- Department of Biomedical Engineering, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Shenzhen University, Shenzhen 518060, China.
| | - Mengting Liu
- Department of Biomedical Engineering, Sun Yat-sen University, Shenzhen 518107, China.
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DesRuisseaux LA, Guevara JE, Duff K. Examining the Stability and Predictive Utility of Across- and Within-Domain Intra-Individual Variability in Mild Cognitive Impairment. Arch Clin Neuropsychol 2024:acae054. [PMID: 39003237 DOI: 10.1093/arclin/acae054] [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: 10/30/2023] [Revised: 06/13/2024] [Accepted: 07/01/2024] [Indexed: 07/15/2024] Open
Abstract
OBJECTIVE Dispersion is a form of intra-individual variability across neuropsychological tests that has been shown to predict cognitive decline. However, few studies have investigated the stability and predictive utility of both across- and within-domain dispersion. The current study aims to fill these gaps in the literature by examining multiple indices of dispersion in a longitudinal clinical sample of individuals diagnosed with mild cognitive impairment (MCI) at baseline. METHOD Two hundred thirty-eight MCI patients from a cognitive disorders clinic underwent testing at baseline and after approximately 1.5 years. Linear regression was used to examine whether baseline across- and within-domain dispersion predicted cognitive decline in individuals whose diagnostic classification progressed to dementia (i.e., MCI-Decline) and those who retained an MCI diagnosis at follow-up (i.e., MCI-Stable). Cognitive decline was operationalized dichotomously using group status and continuously using standardized regression-based (SRB) z-scores. RESULTS Dispersion variables at baseline and follow-up were positively correlated in both groups, with the exception of within-domain executive functioning and language dispersion in the MCI-Decline group. None of the dispersion variables predicted diagnostic conversion to MCI. Using SRB z-scores, greater across-domain dispersion predicted greater overall cognitive decline at follow-up, but this was not the case for within-domain variables with the exception of visuospatial skills. CONCLUSIONS Results suggest that across- and within-domain dispersion are relatively stable across time, and that across-domain dispersion is predictive of subtle cognitive decline in patients with MCI. However, these results also highlight that findings may differ based on the tests included in dispersion calculations.
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Affiliation(s)
| | - Jasmin E Guevara
- Department of Psychology, University of Utah, Salt Lake City, UT 84112, USA
| | - Kevin Duff
- Department of Neurology, Layton Aging and Alzheimer's Disease Center, Oregon Health & Science University, Portland, OR 97239, USA
- Department of Neurology, Center for Alzheimer's Care, Imaging, and Research, University of Utah, Salt Lake City, UT 84111, USA
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Xue C, Kowshik SS, Lteif D, Puducheri S, Jasodanand VH, Zhou OT, Walia AS, Guney OB, Zhang JD, Pham ST, Kaliaev A, Andreu-Arasa VC, Dwyer BC, Farris CW, Hao H, Kedar S, Mian AZ, Murman DL, O'Shea SA, Paul AB, Rohatgi S, Saint-Hilaire MH, Sartor EA, Setty BN, Small JE, Swaminathan A, Taraschenko O, Yuan J, Zhou Y, Zhu S, Karjadi C, Alvin Ang TF, Bargal SA, Plummer BA, Poston KL, Ahangaran M, Au R, Kolachalama VB. AI-based differential diagnosis of dementia etiologies on multimodal data. Nat Med 2024:10.1038/s41591-024-03118-z. [PMID: 38965435 DOI: 10.1038/s41591-024-03118-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Accepted: 06/06/2024] [Indexed: 07/06/2024]
Abstract
Differential diagnosis of dementia remains a challenge in neurology due to symptom overlap across etiologies, yet it is crucial for formulating early, personalized management strategies. Here, we present an artificial intelligence (AI) model that harnesses a broad array of data, including demographics, individual and family medical history, medication use, neuropsychological assessments, functional evaluations and multimodal neuroimaging, to identify the etiologies contributing to dementia in individuals. The study, drawing on 51,269 participants across 9 independent, geographically diverse datasets, facilitated the identification of 10 distinct dementia etiologies. It aligns diagnoses with similar management strategies, ensuring robust predictions even with incomplete data. Our model achieved a microaveraged area under the receiver operating characteristic curve (AUROC) of 0.94 in classifying individuals with normal cognition, mild cognitive impairment and dementia. Also, the microaveraged AUROC was 0.96 in differentiating the dementia etiologies. Our model demonstrated proficiency in addressing mixed dementia cases, with a mean AUROC of 0.78 for two co-occurring pathologies. In a randomly selected subset of 100 cases, the AUROC of neurologist assessments augmented by our AI model exceeded neurologist-only evaluations by 26.25%. Furthermore, our model predictions aligned with biomarker evidence and its associations with different proteinopathies were substantiated through postmortem findings. Our framework has the potential to be integrated as a screening tool for dementia in clinical settings and drug trials. Further prospective studies are needed to confirm its ability to improve patient care.
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Affiliation(s)
- Chonghua Xue
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Department of Electrical & Computer Engineering, Boston University, Boston, MA, USA
| | - Sahana S Kowshik
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Faculty of Computing & Data Sciences, Boston University, Boston, MA, USA
| | - Diala Lteif
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Department of Computer Science, Boston University, Boston, MA, USA
| | - Shreyas Puducheri
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Varuna H Jasodanand
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Olivia T Zhou
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Anika S Walia
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Osman B Guney
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Department of Electrical & Computer Engineering, Boston University, Boston, MA, USA
| | - J Diana Zhang
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- School of Chemistry, University of New South Wales, Sydney, Australia
| | - Serena T Pham
- Department of Radiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Artem Kaliaev
- Department of Radiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - V Carlota Andreu-Arasa
- Department of Radiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Brigid C Dwyer
- Department of Neurology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Chad W Farris
- Department of Radiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Honglin Hao
- Department of Neurology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Sachin Kedar
- Departments of Neurology & Ophthalmology, Emory University School of Medicine, Atlanta, GA, USA
| | - Asim Z Mian
- Department of Radiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Daniel L Murman
- Department of Neurological Sciences, University of Nebraska Medical Center, Omaha, NE, USA
| | - Sarah A O'Shea
- Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
| | - Aaron B Paul
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Saurabh Rohatgi
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | | | - Emmett A Sartor
- Department of Neurology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Bindu N Setty
- Department of Radiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Juan E Small
- Department of Radiology, Lahey Hospital & Medical Center, Burlington, MA, USA
| | | | - Olga Taraschenko
- Department of Neurological Sciences, University of Nebraska Medical Center, Omaha, NE, USA
| | - Jing Yuan
- Department of Neurology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Yan Zhou
- Department of Neurology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Shuhan Zhu
- Department of Neurology, Brigham & Women's Hospital, Boston, MA, USA
| | - Cody Karjadi
- The Framingham Heart Study, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Ting Fang Alvin Ang
- The Framingham Heart Study, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Department of Anatomy and Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Sarah A Bargal
- Department of Computer Science, Georgetown University, Washington, DC, USA
| | - Bryan A Plummer
- Department of Computer Science, Boston University, Boston, MA, USA
| | | | - Meysam Ahangaran
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Rhoda Au
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Department of Neurology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- The Framingham Heart Study, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Department of Anatomy and Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Boston University Alzheimer's Disease Research Center, Boston, MA, USA
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA
| | - Vijaya B Kolachalama
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.
- Faculty of Computing & Data Sciences, Boston University, Boston, MA, USA.
- Department of Computer Science, Boston University, Boston, MA, USA.
- Boston University Alzheimer's Disease Research Center, Boston, MA, USA.
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12
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Seyedmirzaei H, Salmannezhad A, Ashayeri H, Shushtari A, Farazinia B, Heidari MM, Momayezi A, Shaki Baher S. Growth-Associated Protein 43 and Tensor-Based Morphometry Indices in Mild Cognitive Impairment. Neuroinformatics 2024; 22:239-250. [PMID: 38630411 DOI: 10.1007/s12021-024-09663-9] [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] [Accepted: 04/08/2024] [Indexed: 08/17/2024]
Abstract
Growth-associated protein 43 (GAP-43) is found in the axonal terminal of neurons in the limbic system, which is affected in people with Alzheimer's disease (AD). We assumed GAP-43 may contribute to AD progression and serve as a biomarker. So, in a two-year follow-up study, we assessed GAP-43 changes and whether they are correlated with tensor-based morphometry (TBM) findings in patients with mild cognitive impairment (MCI). We included MCI and cognitively normal (CN) people with available baseline and follow-up cerebrospinal fluid (CSF) GAP-43 and TBM findings from the ADNI database. We assessed the difference between the two groups and correlations in each group at each time point. CSF GAP-43 and TBM measures were similar in the two study groups in all time points, except for the accelerated anatomical region of interest (ROI) of CN subjects that were significantly greater than those of MCI. The only significant correlations with GAP-43 observed were those inverse correlations with accelerated and non-accelerated anatomical ROI in MCI subjects at baseline. Plus, all TBM metrics decreased significantly in all study groups during the follow-up in contrast to CSF GAP-43 levels. Our study revealed significant associations between CSF GAP-43 levels and TBM indices among people of the AD spectrum.
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Affiliation(s)
- Homa Seyedmirzaei
- Sports Medicine Research Center, Neuroscience Institute, Tehran University of Medical Sciences, Tehran, Iran
| | | | - Hamidreza Ashayeri
- Student Research Committee, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Ali Shushtari
- Faculty of Medicine , Mazandaran University of Medical Sciences, Sari, Iran.
| | - Bita Farazinia
- Faculty of Economics and Management, Shiraz Branch, Islamic Azad University, Shiraz, Iran
| | - Mohammad Mahdi Heidari
- Student Research Committee, Faculty of Medicine, Kashan University of Medical Sciences, Kashan, Iran
| | - Amirali Momayezi
- School of Chemical engineering, Iran University of Science and Technology, Tehran, Iran
| | - Sara Shaki Baher
- Faculty of Medicine, Tehran Branch, Islamic Azad University, Tehran, Iran
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13
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Kumar S, Earnest T, Yang B, Kothapalli D, Aschenbrenner AJ, Hassenstab J, Xiong C, Ances B, Morris J, Benzinger TLS, Gordon BA, Payne P, Sotiras A. Analyzing heterogeneity in Alzheimer Disease using multimodal normative modeling on imaging-based ATN biomarkers. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.08.15.553412. [PMID: 37662280 PMCID: PMC10473626 DOI: 10.1101/2023.08.15.553412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
INTRODUCTION Previous studies have applied normative modeling on a single neuroimaging modality to investigate Alzheimer Disease (AD) heterogeneity. We employed a deep learning-based multimodal normative framework to analyze individual-level variation across ATN (amyloid-tau-neurodegeneration) imaging biomarkers. METHODS We selected cross-sectional discovery (n = 665) and replication cohorts (n = 430) with available T1-weighted MRI, amyloid and tau PET. Normative modeling estimated individual-level abnormal deviations in amyloid-positive individuals compared to amyloid-negative controls. Regional abnormality patterns were mapped at different clinical group levels to assess intra-group heterogeneity. An individual-level disease severity index (DSI) was calculated using both the spatial extent and magnitude of abnormal deviations across ATN. RESULTS Greater intra-group heterogeneity in ATN abnormality patterns was observed in more severe clinical stages of AD. Higher DSI was associated with worse cognitive function and increased risk of disease progression. DISCUSSION Subject-specific abnormality maps across ATN reveal the heterogeneous impact of AD on the brain.
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Affiliation(s)
- Sayantan Kumar
- Department of Computer Science and Engineering, Washington University in St Louis; 1 Brookings Drive, Saint Louis, MO 63130
- Institute for Informatics, Data Science & Biostatistics, Washington University School of Medicine in St Louis; 660 S. Euclid Ave, Campus Box 8132, Saint Louis, MO 63110
| | - Tom Earnest
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St Louis; 4525 Scott Ave, Saint Louis, MO 63110
| | - Braden Yang
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St Louis; 4525 Scott Ave, Saint Louis, MO 63110
| | - Deydeep Kothapalli
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St Louis; 4525 Scott Ave, Saint Louis, MO 63110
| | - Andrew J Aschenbrenner
- Department of Neurology, Washington University School of Medicine, 660 S Euclid Ave, Campus Box 8111, St louis, MO 63110
| | - Jason Hassenstab
- Department of Neurology, Washington University School of Medicine, 660 S Euclid Ave, Campus Box 8111, St louis, MO 63110
| | - Chengie Xiong
- Institute for Informatics, Data Science & Biostatistics, Washington University School of Medicine in St Louis; 660 S. Euclid Ave, Campus Box 8132, Saint Louis, MO 63110
| | - Beau Ances
- Department of Neurology, Washington University School of Medicine, 660 S Euclid Ave, Campus Box 8111, St louis, MO 63110
| | - John Morris
- Department of Neurology, Washington University School of Medicine, 660 S Euclid Ave, Campus Box 8111, St louis, MO 63110
| | - Tammie L S Benzinger
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St Louis; 4525 Scott Ave, Saint Louis, MO 63110
| | - Brian A Gordon
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St Louis; 4525 Scott Ave, Saint Louis, MO 63110
| | - Philip Payne
- Department of Computer Science and Engineering, Washington University in St Louis; 1 Brookings Drive, Saint Louis, MO 63130
- Institute for Informatics, Data Science & Biostatistics, Washington University School of Medicine in St Louis; 660 S. Euclid Ave, Campus Box 8132, Saint Louis, MO 63110
| | - Aristeidis Sotiras
- Institute for Informatics, Data Science & Biostatistics, Washington University School of Medicine in St Louis; 660 S. Euclid Ave, Campus Box 8132, Saint Louis, MO 63110
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St Louis; 4525 Scott Ave, Saint Louis, MO 63110
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14
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Zhang B, Zhang C, Wang Y, Cheng L, Wang Y, Qiao Y, Peng D. Associations of liver function with plasma biomarkers for Alzheimer's Disease. Neurol Sci 2024; 45:2625-2631. [PMID: 38177970 DOI: 10.1007/s10072-023-07284-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 12/14/2023] [Indexed: 01/06/2024]
Abstract
BACKGROUND Blood-based biomarkers for Alzheimer's disease (AD) are promising to be used in clinical settings. The liver is an important degradation organ of the body. Whether liver function affects the levels of AD biomarkers needs to be studied. OBJECTIVE To investigate the associations between liver function and the plasma levels of AD biomarkers. METHODS We conducted an ADNI cohort-based cross-sectional study. Thirteen liver function markers commonly used in clinical settings were analyzed: total protein (TP), albumin (AL), globulin (GL), AL/GL ratio (A/G), total bilirubin (TB), direct bilirubin (DB), indirect bilirubin (IB), alanine aminotransferase (ALT), aspartate aminotransferase (AST), AST/ALT ratio, alkaline phosphatase (ALP), lactate dehydrogenase (LDH), and γ-glutamyltransferase (GGT). Liquid chromatography-tandem mass spectrometry was used to detect the plasma Aβ42 and Aβ40 concentrations. Single Molecule array technique was used to measure the plasma p-tau181 and NfL concentrations. We used linear regression models to analyze the associations between liver function markers and the levels of AD plasma biomarkers. RESULTS ALP was positively associated with the levels of plasma Aβ42 (β = 0.16, P = 0.018) and Aβ40 (β = 0.21, P = 0.004). LDH was positively associated with the levels of plasma p-tau181 (β = 0.09, P = 0.022). While NfL was correlated with multiple liver function markers, including AL, A/G, ALT, AST/ALT, and LDH. CONCLUSION Liver function was associated with the plasma levels of AD biomarkers. It needs to consider the potential influence of liver function on the reference ranges and the interpretation of results for AD biomarkers before clinical use.
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Affiliation(s)
- Bin Zhang
- Department of Neurology, China-Japan Friendship Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Cheng Zhang
- International Acupuncture and Moxibustion Innovation Institute, School of Acupuncture-Moxibustion and Tuina, Beijing University of Chinese Medicine, Beijing, China
| | - YuYe Wang
- Department of Neurology, China-Japan Friendship Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - LeiAn Cheng
- Department of Neurology, China-Japan Friendship Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Yu Wang
- Department of Neurology, China-Japan Friendship Hospital, Beijing, China
| | - YaNan Qiao
- Department of Neurology, China-Japan Friendship Hospital, Beijing, China.
| | - Dantao Peng
- Department of Neurology, China-Japan Friendship Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
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15
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Shanks HRC, Chen K, Reiman EM, Blennow K, Cummings JL, Massa SM, Longo FM, Börjesson-Hanson A, Windisch M, Schmitz TW. p75 neurotrophin receptor modulation in mild to moderate Alzheimer disease: a randomized, placebo-controlled phase 2a trial. Nat Med 2024; 30:1761-1770. [PMID: 38760589 PMCID: PMC11186782 DOI: 10.1038/s41591-024-02977-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: 11/01/2023] [Accepted: 04/04/2024] [Indexed: 05/19/2024]
Abstract
p75 neurotrophin receptor (p75NTR) signaling pathways substantially overlap with degenerative networks active in Alzheimer disease (AD). Modulation of p75NTR with the first-in-class small molecule LM11A-31 mitigates amyloid-induced and pathological tau-induced synaptic loss in preclinical models. Here we conducted a 26-week randomized, placebo-controlled, double-blinded phase 2a safety and exploratory endpoint trial of LM11A-31 in 242 participants with mild to moderate AD with three arms: placebo, 200 mg LM11A-31 and 400 mg LM11A-31, administered twice daily by oral capsules. This trial met its primary endpoint of safety and tolerability. Within the prespecified secondary and exploratory outcome domains (structural magnetic resonance imaging, fluorodeoxyglucose positron-emission tomography and cerebrospinal fluid biomarkers), significant drug-placebo differences were found, consistent with the hypothesis that LM11A-31 slows progression of pathophysiological features of AD; no significant effect of active treatment was observed on cognitive tests. Together, these results suggest that targeting p75NTR with LM11A-31 warrants further investigation in larger-scale clinical trials of longer duration. EU Clinical Trials registration: 2015-005263-16 ; ClinicalTrials.gov registration: NCT03069014 .
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Grants
- R35 AG071476 NIA NIH HHS
- P30 AG072980 NIA NIH HHS
- SG-23-1038904 QC Alzheimer's Association
- 2022-00732 Vetenskapsrådet (Swedish Research Council)
- P20 GM109025 NIGMS NIH HHS
- R01 AG053798 NIA NIH HHS
- R35AG71476 U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging)
- ZEN-21-848495 Alzheimer's Association
- R01 AG051596 NIA NIH HHS
- P20GM109025 U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS)
- 453677 Gouvernement du Canada | Canadian Institutes of Health Research (Instituts de Recherche en Santé du Canada)
- P20 AG068053 NIA NIH HHS
- 2017-00915 Vetenskapsrådet (Swedish Research Council)
- U01 AG024904 NIA NIH HHS
- R01AG053798 U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging)
- R25 AG083721-01 U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging)
- R25 AG083721 NIA NIH HHS
- Jonathan and Joshua Memorial Foundation Government of Ontario
- U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging)
- State of Arizona
- Alzheimer’s Association
- the Swedish state under the agreement between the Swedish government and the County Councils, the ALF-agreement (#ALFGBG-715986 and #ALFGBG-965240), the Swedish Alzheimer Foundation (#AF-930351, #AF-939721 and #AF-968270), Hjärnfonden, Sweden (#FO2017-0243 and #ALZ2022-0006), La Fondation Recherche Alzheimer (FRA), Paris, France, the Kirsten and Freddy Johansen Foundation, Copenhagen, Denmark, and Familjen Rönströms Stiftelse, Stockholm, Sweden.
- U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS)
- Alzheimer’s Drug Discovery Foundation (ADDF)
- Ted and Maria Quirk Endowment; Joy Chambers-Grundy Endowment.
- San Francisco VA Health Care System
- National Institutes of Aging (NIA AD Pilot Trial 1R01AG051596) PharmatrophiX (Menlo Park, California)
- Alzheimer’s Society of Canada (176677)
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Affiliation(s)
- Hayley R C Shanks
- Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada.
- Robarts Research Institute, Western University, London, Ontario, Canada.
- Western Institute for Neuroscience, Western University, London, Ontario, Canada.
| | - Kewei Chen
- Banner Alzheimer's Institute, Phoenix, AZ, USA
- College of Medicine-Phoenix, University of Arizona, Phoenix, AZ, USA
- College of Health Solutions, Arizona State University, Downtown, Phoenix, AZ, USA
| | - Eric M Reiman
- Banner Alzheimer's Institute, Phoenix, AZ, USA
- College of Medicine-Phoenix, University of Arizona, Phoenix, AZ, USA
- Translational Genomics Research Institute, Phoenix, AZ, USA
- Arizona Alzheimer's Consortium, Phoenix, AZ, USA
- ASU-Banner Neurodegenerative Disease Research Center, Arizona State University, Tempe, AZ, USA
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Jeffrey L Cummings
- Chambers-Grundy Center for Transformative Neuroscience, Department of Brain Health, School of Integrated Health Sciences, University of Nevada Las Vegas (UNLV), Las Vegas, NV, USA
| | - Stephen M Massa
- San Francisco Veterans Affairs Health Care System, San Francisco, CA, USA
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | | | - Anne Börjesson-Hanson
- Clinical Trials, Department of Aging, Karolinska University Hospital, Stockholm, Sweden
| | | | - Taylor W Schmitz
- Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada.
- Robarts Research Institute, Western University, London, Ontario, Canada.
- Western Institute for Neuroscience, Western University, London, Ontario, Canada.
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16
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Shang C, Sakurai K, Nihashi T, Arahata Y, Takeda A, Ishii K, Ishii K, Matsuda H, Ito K, Kato T, Toyama H, Nakamura A. Comparison of consistency in centiloid scale among different analytical methods in amyloid PET: the CapAIBL, VIZCalc, and Amyquant methods. Ann Nucl Med 2024; 38:460-467. [PMID: 38512444 PMCID: PMC11108942 DOI: 10.1007/s12149-024-01919-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Accepted: 02/28/2024] [Indexed: 03/23/2024]
Abstract
OBJECTIVE The Centiloid (CL) scale is a standardized measure for quantifying amyloid deposition in amyloid positron emission tomography (PET) imaging. We aimed to assess the agreement among 3 CL calculation methods: CapAIBL, VIZCalc, and Amyquant. METHODS This study included 192 participants (mean age: 71.5 years, range: 50-87 years), comprising 55 with Alzheimer's disease, 65 with mild cognitive impairment, 13 with non-Alzheimer's dementia, and 59 cognitively normal participants. All the participants were assessed using the three CL calculation methods. Spearman's rank correlation, linear regression, Friedman tests, Wilcoxon signed-rank tests, and Bland-Altman analysis were employed to assess data correlations, linear associations, method differences, and systematic bias, respectively. RESULTS Strong correlations (rho = 0.99, p < .001) were observed among the CL values calculated using the three methods. Scatter plots and regression lines visually confirmed these strong correlations and met the validation criteria. Despite the robust correlations, a significant difference in CL value between CapAIBL and Amyquant was observed (36.1 ± 39.7 vs. 34.9 ± 39.4; p < .001). In contrast, no significant differences were found between CapAIBL and VIZCalc or between VIZCalc and Amyquant. The Bland-Altman analysis showed no observable systematic bias between the methods. CONCLUSIONS The study demonstrated strong agreement among the three methods for calculating CL values. Despite minor variations in the absolute values of the Centiloid scores obtained using these methods, the overall agreement suggests that they are interchangeable.
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Affiliation(s)
- Cong Shang
- Department of Radiology, National Center for Geriatrics and Gerontology, 7-430 Morioka-Cho, Obu, Aichi, 474-8511, Japan
- Department of Radiology, Fujita Health University School of Medicine, Toyoake, Japan
| | - Keita Sakurai
- Department of Radiology, National Center for Geriatrics and Gerontology, 7-430 Morioka-Cho, Obu, Aichi, 474-8511, Japan
| | - Takashi Nihashi
- Department of Radiology, National Center for Geriatrics and Gerontology, 7-430 Morioka-Cho, Obu, Aichi, 474-8511, Japan
| | - Yutaka Arahata
- Department of Neurology, National Center for Geriatrics and Gerontology, Obu, Japan
| | - Akinori Takeda
- Department of Neurology, National Center for Geriatrics and Gerontology, Obu, Japan
| | - Kazunari Ishii
- Department of Radiology, Faculty of Medicine, Kindai University, Osakasayama, Japan
| | - Kenji Ishii
- Team for Neuroimaging Research, Tokyo Metropolitan Institute of Gerontology, Tokyo, Japan
| | - Hiroshi Matsuda
- Department of Biofunctional Imaging, Fukushima Medical University, Fukushima, Japan
- Drug Discovery and Cyclotron Research Center, Southern Tohoku Research Institute for Neuroscience, Koriyama, Japan
| | - Kengo Ito
- Department of Radiology, National Center for Geriatrics and Gerontology, 7-430 Morioka-Cho, Obu, Aichi, 474-8511, Japan
- Department of Clinical and Experimental Neuroimaging, National Center for Geriatrics and Gerontology, Obu, Japan
| | - Takashi Kato
- Department of Radiology, National Center for Geriatrics and Gerontology, 7-430 Morioka-Cho, Obu, Aichi, 474-8511, Japan.
- Department of Clinical and Experimental Neuroimaging, National Center for Geriatrics and Gerontology, Obu, Japan.
| | - Hiroshi Toyama
- Department of Radiology, Fujita Health University School of Medicine, Toyoake, Japan
| | - Akinori Nakamura
- Department of Clinical and Experimental Neuroimaging, National Center for Geriatrics and Gerontology, Obu, Japan
- Department of Biomarker Research, National Center for Geriatrics and Gerontology, Obu, Japan
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17
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Zou A, Ji J, Lei M, Liu J, Song Y. Exploring Brain Effective Connectivity Networks Through Spatiotemporal Graph Convolutional Models. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024; 35:7871-7883. [PMID: 36399590 DOI: 10.1109/tnnls.2022.3221617] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Learning brain effective connectivity networks (ECN) from functional magnetic resonance imaging (fMRI) data has gained much attention in recent years. With the successful applications of deep learning in numerous fields, several brain ECN learning methods based on deep learning have been reported in the literature. However, current methods ignore the deep temporal features of fMRI data and fail to fully employ the spatial topological relationship between brain regions. In this article, we propose a novel method for learning brain ECN based on spatiotemporal graph convolutional models (STGCM), named STGCMEC, in which we first adopt the temporal convolutional network to extract the deep temporal features of fMRI data and utilize the graph convolutional network to update the spatial features of each brain region by aggregating information from neighborhoods, which makes the features of brain regions more discriminative. Then, based on such features of brain regions, we design a joint loss function to guide STGCMEC to learn the brain ECN, which includes a task prediction loss and a graph regularization loss. The experimental results on a simulated dataset and a real Alzheimer's disease neuroimaging initiative (ADNI) dataset show that the proposed STGCMEC is able to better learn brain ECN compared with some state-of-the-art methods.
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18
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Chandra S, Prakash PKS, Samanta S, Chilukuri S. ClinicalGAN: powering patient monitoring in clinical trials with patient digital twins. Sci Rep 2024; 14:12236. [PMID: 38806536 PMCID: PMC11133486 DOI: 10.1038/s41598-024-62567-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Accepted: 05/19/2024] [Indexed: 05/30/2024] Open
Abstract
Conducting clinical trials is becoming increasingly challenging lately due to spiraling costs, increased time to market, and high failure rates. Patient recruitment and retention is one of the key challenges that impact 90% of the trials directly. While a lot of attention has been given to optimizing patient recruitment, limited progress has been made towards developing comprehensive clinical trial monitoring systems to determine patients at risk and potentially improve patient retention through the right intervention at the right time. Earlier research in patient retention primarily focused on using deterministic frameworks to model the inherently stochastic patient journey process. Existing generative approaches to model temporal data such as TimeGAN or CRBM , face challenges and fail to address key requirements such as personalized generation, variable patient journey, and multi-variate time-series needed to model patient digital twin. In response to these challenges, current research proposes ClinicalGAN to enable patient level generation, effectively creating a patient's digital twin. ClinicalGAN provides capabilities for: (a) patient-level personalized generation by utilizing patient meta-data for conditional generation; (b) dynamic termination prediction to enable pro-active patient monitoring for improved patient retention; (c) multi-variate time-series training to incorporate relationship and dependencies among different tests measures captured during patient journey. The proposed solution is validated on two Alzheimer's clinical trial datasets and the results are benchmarked across multiple dimensions of generation quality. Empirical results demonstrate that the proposed ClinicalGAN outperforms the SOTA approach by 3-4 × on average across all the generation quality metrics. Furthermore, the proposed architecture is shown to outperform predictive methods at the task of drop-off prediction significantly (5-10% MAPE scores).
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Affiliation(s)
- Shantanu Chandra
- ZS, 2nd Floor, MFAR Manyta Tech park, Phase IV, Manayata Tech Park, Nagavara, Bengaluru, Karnataka, India.
| | - P K S Prakash
- ZS, 2nd Floor, MFAR Manyta Tech park, Phase IV, Manayata Tech Park, Nagavara, Bengaluru, Karnataka, India
| | - Subhrajit Samanta
- ZS, 2nd Floor, MFAR Manyta Tech park, Phase IV, Manayata Tech Park, Nagavara, Bengaluru, Karnataka, India
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Grigas O, Damaševičius R, Maskeliūnas R. Positive Effect of Super-Resolved Structural Magnetic Resonance Imaging for Mild Cognitive Impairment Detection. Brain Sci 2024; 14:381. [PMID: 38672031 PMCID: PMC11048389 DOI: 10.3390/brainsci14040381] [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: 03/20/2024] [Revised: 04/09/2024] [Accepted: 04/10/2024] [Indexed: 04/28/2024] Open
Abstract
This paper presents a novel approach to improving the detection of mild cognitive impairment (MCI) through the use of super-resolved structural magnetic resonance imaging (MRI) and optimized deep learning models. The study introduces enhancements to the perceptual quality of super-resolved 2D structural MRI images using advanced loss functions, modifications to the upscaler part of the generator, and experiments with various discriminators within a generative adversarial training setting. It empirically demonstrates the effectiveness of super-resolution in the MCI detection task, showcasing performance improvements across different state-of-the-art classification models. The paper also addresses the challenge of accurately capturing perceptual image quality, particularly when images contain checkerboard artifacts, and proposes a methodology that incorporates hyperparameter optimization through a Pareto optimal Markov blanket (POMB). This approach systematically explores the hyperparameter space, focusing on reducing overfitting and enhancing model generalizability. The research findings contribute to the field by demonstrating that super-resolution can significantly improve the quality of MRI images for MCI detection, highlighting the importance of choosing an adequate discriminator and the potential of super-resolution as a preprocessing step to boost classification model performance.
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Affiliation(s)
- Ovidijus Grigas
- Faculty of Informatics, Kaunas University of Technology, 50254 Kaunas, Lithuania; (O.G.); (R.M.)
| | - Robertas Damaševičius
- Faculty of Informatics, Kaunas University of Technology, 50254 Kaunas, Lithuania; (O.G.); (R.M.)
- Faculty of Applied Mathematics, Silesian University of Technology, 44-100 Gliwice, Poland
| | - Rytis Maskeliūnas
- Faculty of Informatics, Kaunas University of Technology, 50254 Kaunas, Lithuania; (O.G.); (R.M.)
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20
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Griswold AJ, Rajabli F, Gu T, Arvizu J, Golightly CG, Whitehead PL, Hamilton-Nelson KL, Adams LD, Sanchez JJ, Mena PR, Starks TD, Illanes-Manrique M, Silva C, Bush WS, Cuccaro ML, Vance JM, Cornejo-Olivas MR, Feliciano-Astacio BE, Byrd GS, Beecham GW, Haines JL, Pericak-Vance MA. Generalizability of Tau and Amyloid Plasma Biomarkers in Alzheimer's Disease Cohorts of Diverse Genetic Ancestries. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.10.24305617. [PMID: 38645114 PMCID: PMC11030471 DOI: 10.1101/2024.04.10.24305617] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
Introduction Plasma phosphorylated threonine-181 of Tau and amyloid beta are biomarkers for differential diagnosis and preclinical detection of Alzheimer disease (AD). Given differences in AD risk across diverse populations, generalizability of existing biomarker data is not assured. Methods In 2,086 individuals of diverse genetic ancestries (African American, Caribbean Hispanic, and Peruvians) we measured plasma pTau-181 and Aβ42/Aβ40. Differences in biomarkers between cohorts and clinical diagnosis groups and the potential discriminative performance of the two biomarkers were assessed. Results pTau-181 and Aβ42/Aβ40 were consistent across cohorts. Higher levels of pTau181 were associated with AD while Aβ42/Aβ40 had minimal differences. Correspondingly, pTau-181 had greater predictive value than Aβ42/Aβ40, however, the area under the curve differed between cohorts. Discussion pTau-181 as a plasma biomarker for clinical AD is generalizable across genetic ancestries, but predictive value may differ. Combining genomic and biomarker data from diverse individuals will increase understanding of genetic risk and refine clinical diagnoses.
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Affiliation(s)
- Anthony J Griswold
- John P. Hussman Institute for Human Genomics, University of Miami, Miami, FL, 33136, USA
- Dr. John T Macdonald Foundation Department of Human Genetics, University of Miami, Miami, FL, 33136, USA
| | - Farid Rajabli
- John P. Hussman Institute for Human Genomics, University of Miami, Miami, FL, 33136, USA
- Dr. John T Macdonald Foundation Department of Human Genetics, University of Miami, Miami, FL, 33136, USA
| | - Tianjie Gu
- John P. Hussman Institute for Human Genomics, University of Miami, Miami, FL, 33136, USA
| | - Jamie Arvizu
- John P. Hussman Institute for Human Genomics, University of Miami, Miami, FL, 33136, USA
| | - Charles G Golightly
- John P. Hussman Institute for Human Genomics, University of Miami, Miami, FL, 33136, USA
| | - Patrice L Whitehead
- John P. Hussman Institute for Human Genomics, University of Miami, Miami, FL, 33136, USA
| | - Kara L Hamilton-Nelson
- John P. Hussman Institute for Human Genomics, University of Miami, Miami, FL, 33136, USA
| | - Larry D Adams
- John P. Hussman Institute for Human Genomics, University of Miami, Miami, FL, 33136, USA
| | - Jose Javier Sanchez
- John P. Hussman Institute for Human Genomics, University of Miami, Miami, FL, 33136, USA
| | - Pedro R Mena
- John P. Hussman Institute for Human Genomics, University of Miami, Miami, FL, 33136, USA
| | - Takiyah D Starks
- Maya Angelou Center for Health Equity, Wake Forest University, Winston-Salem, NC, 27102, USA
| | | | - Concepcion Silva
- Department of Internal Medicine, Universidad Central Del Caribe, Bayamón, Puerto Rico, 00960, USA
| | - William S Bush
- Department of Population & Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, 44106, USA
- Cleveland Institute for Computational Biology, Cleveland, OH, 44106, USA
| | - Michael L Cuccaro
- John P. Hussman Institute for Human Genomics, University of Miami, Miami, FL, 33136, USA
- Dr. John T Macdonald Foundation Department of Human Genetics, University of Miami, Miami, FL, 33136, USA
| | - Jeffery M Vance
- John P. Hussman Institute for Human Genomics, University of Miami, Miami, FL, 33136, USA
- Dr. John T Macdonald Foundation Department of Human Genetics, University of Miami, Miami, FL, 33136, USA
| | - Mario R Cornejo-Olivas
- Neurogenetics Research Center, Instituto Nacional de Ciencias Neurologicas, Lima, 15003, Peru
| | | | - Goldie S Byrd
- Maya Angelou Center for Health Equity, Wake Forest University, Winston-Salem, NC, 27102, USA
| | - Gary W Beecham
- John P. Hussman Institute for Human Genomics, University of Miami, Miami, FL, 33136, USA
- Dr. John T Macdonald Foundation Department of Human Genetics, University of Miami, Miami, FL, 33136, USA
| | - Jonathan L Haines
- Department of Population & Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, 44106, USA
- Cleveland Institute for Computational Biology, Cleveland, OH, 44106, USA
| | - Margaret A Pericak-Vance
- John P. Hussman Institute for Human Genomics, University of Miami, Miami, FL, 33136, USA
- Dr. John T Macdonald Foundation Department of Human Genetics, University of Miami, Miami, FL, 33136, USA
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21
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Ortega‐Cruz D, Bress KS, Gazula H, Rabano A, Iglesias JE, Strange BA. Three-dimensional histology reveals dissociable human hippocampal long-axis gradients of Alzheimer's pathology. Alzheimers Dement 2024; 20:2606-2619. [PMID: 38369763 PMCID: PMC11032559 DOI: 10.1002/alz.13695] [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: 08/04/2023] [Revised: 12/04/2023] [Accepted: 12/18/2023] [Indexed: 02/20/2024]
Abstract
INTRODUCTION Three-dimensional (3D) histology analyses are essential to overcome sampling variability and understand pathological differences beyond the dissection axis. We present Path2MR, the first pipeline allowing 3D reconstruction of sparse human histology without a magnetic resonance imaging (MRI) reference. We implemented Path2MR with post-mortem hippocampal sections to explore pathology gradients in Alzheimer's disease. METHODS Blockface photographs of brain hemisphere slices are used for 3D reconstruction, from which an MRI-like image is generated using machine learning. Histology sections are aligned to the reconstructed hemisphere and subsequently to an atlas in standard space. RESULTS Path2MR successfully registered histological sections to their anatomic position along the hippocampal longitudinal axis. Combined with histopathology quantification, we found an expected peak of tau pathology at the anterior end of the hippocampus, whereas amyloid-beta (Aβ) displayed a quadratic anterior-posterior distribution. CONCLUSION Path2MR, which enables 3D histology using any brain bank data set, revealed significant differences along the hippocampus between tau and Aβ. HIGHLIGHTS Path2MR enables three-dimensional (3D) brain reconstruction from blockface dissection photographs. This pipeline does not require dense specimen sampling or a subject-specific magnetic resonance (MR) image. Anatomically consistent mapping of hippocampal sections was obtained with Path2MR. Our analyses revealed an anterior-posterior gradient of hippocampal tau pathology. In contrast, the peak of amyloid-beta (Aβ) deposition was closer to the hippocampal body.
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Affiliation(s)
- Diana Ortega‐Cruz
- Laboratory for Clinical Neuroscience, Center for Biomedical TechnologyUniversidad Politécnica de Madrid, IdISSCMadridSpain
- Alzheimer's Disease Research UnitCIEN Foundation, Queen Sofia Foundation Alzheimer CenterMadridSpain
| | - Kimberly S. Bress
- Alzheimer's Disease Research UnitCIEN Foundation, Queen Sofia Foundation Alzheimer CenterMadridSpain
- Present address:
Vanderbilt University School of MedicineNashvilleTennesseeUSA
| | - Harshvardhan Gazula
- Martinos Center for Biomedical ImagingMassachusetts General Hospital and Harvard Medical SchoolBostonMassachusettsUSA
| | - Alberto Rabano
- Alzheimer's Disease Research UnitCIEN Foundation, Queen Sofia Foundation Alzheimer CenterMadridSpain
| | - Juan Eugenio Iglesias
- Martinos Center for Biomedical ImagingMassachusetts General Hospital and Harvard Medical SchoolBostonMassachusettsUSA
- Computer Science and Artificial Intelligence LaboratoryMassachusetts Institute of TechnologyBostonMassachusettsUSA
- Centre for Medical Image ComputingUniversity College LondonLondonUK
| | - Bryan A. Strange
- Laboratory for Clinical Neuroscience, Center for Biomedical TechnologyUniversidad Politécnica de Madrid, IdISSCMadridSpain
- Alzheimer's Disease Research UnitCIEN Foundation, Queen Sofia Foundation Alzheimer CenterMadridSpain
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22
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Momota Y, Bun S, Hirano J, Kamiya K, Ueda R, Iwabuchi Y, Takahata K, Yamamoto Y, Tezuka T, Kubota M, Seki M, Shikimoto R, Mimura Y, Kishimoto T, Tabuchi H, Jinzaki M, Ito D, Mimura M. Amyloid-β prediction machine learning model using source-based morphometry across neurocognitive disorders. Sci Rep 2024; 14:7633. [PMID: 38561395 PMCID: PMC10984960 DOI: 10.1038/s41598-024-58223-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 03/26/2024] [Indexed: 04/04/2024] Open
Abstract
Previous studies have developed and explored magnetic resonance imaging (MRI)-based machine learning models for predicting Alzheimer's disease (AD). However, limited research has focused on models incorporating diverse patient populations. This study aimed to build a clinically useful prediction model for amyloid-beta (Aβ) deposition using source-based morphometry, using a data-driven algorithm based on independent component analyses. Additionally, we assessed how the predictive accuracies varied with the feature combinations. Data from 118 participants clinically diagnosed with various conditions such as AD, mild cognitive impairment, frontotemporal lobar degeneration, corticobasal syndrome, progressive supranuclear palsy, and psychiatric disorders, as well as healthy controls were used for the development of the model. We used structural MR images, cognitive test results, and apolipoprotein E status for feature selection. Three-dimensional T1-weighted images were preprocessed into voxel-based gray matter images and then subjected to source-based morphometry. We used a support vector machine as a classifier. We applied SHapley Additive exPlanations, a game-theoretical approach, to ensure model accountability. The final model that was based on MR-images, cognitive test results, and apolipoprotein E status yielded 89.8% accuracy and a receiver operating characteristic curve of 0.888. The model based on MR-images alone showed 84.7% accuracy. Aβ-positivity was correctly detected in non-AD patients. One of the seven independent components derived from source-based morphometry was considered to represent an AD-related gray matter volume pattern and showed the strongest impact on the model output. Aβ-positivity across neurological and psychiatric disorders was predicted with moderate-to-high accuracy and was associated with a probable AD-related gray matter volume pattern. An MRI-based data-driven machine learning approach can be beneficial as a diagnostic aid.
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Affiliation(s)
- Yuki Momota
- Department of Neuropsychiatry, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan
- Department of Functional Brain Imaging Research, Institute for Quantum Medical Science, National Institutes for Quantum Science and Technology, 4-9-1 Anagawa, Inage-Ku, Chiba-Shi, Chiba, 263-8555, Japan
| | - Shogyoku Bun
- Department of Neuropsychiatry, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan.
| | - Jinichi Hirano
- Department of Neuropsychiatry, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan.
| | - Kei Kamiya
- Department of Neuropsychiatry, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan
| | - Ryo Ueda
- Office of Radiation Technology, Keio University Hospital, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan
| | - Yu Iwabuchi
- Department of Radiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan
| | - Keisuke Takahata
- Department of Neuropsychiatry, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan
- Department of Functional Brain Imaging Research, Institute for Quantum Medical Science, National Institutes for Quantum Science and Technology, 4-9-1 Anagawa, Inage-Ku, Chiba-Shi, Chiba, 263-8555, Japan
| | - Yasuharu Yamamoto
- Department of Functional Brain Imaging Research, Institute for Quantum Medical Science, National Institutes for Quantum Science and Technology, 4-9-1 Anagawa, Inage-Ku, Chiba-Shi, Chiba, 263-8555, Japan
| | - Toshiki Tezuka
- Department of Neurology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan
| | - Masahito Kubota
- Department of Neurology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan
| | - Morinobu Seki
- Department of Neurology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan
| | - Ryo Shikimoto
- Department of Neuropsychiatry, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan
| | - Yu Mimura
- Department of Neuropsychiatry, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan
| | - Taishiro Kishimoto
- Psychiatry Department, Donald and Barbara Zucker School of Medicine, Hempstead, NY, 11549, USA
- Hills Joint Research Laboratory for Future Preventive Medicine and Wellness, Keio University School of Medicine, Mori JP Tower F7, 1-3-1 Azabudai, Minato-ku, Tokyo, 106-0041, Japan
| | - Hajime Tabuchi
- Department of Neuropsychiatry, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan
| | - Masahiro Jinzaki
- Department of Radiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan
| | - Daisuke Ito
- Department of Physiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan
- Memory Center, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan
| | - Masaru Mimura
- Center for Preventive Medicine, Keio University, Mori JP Tower 7th Floor, 1-3-1 Azabudai, Minato-ku, Tokyo, 106-0041, Japan
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23
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Xue C, Kowshik SS, Lteif D, Puducheri S, Jasodanand VH, Zhou OT, Walia AS, Guney OB, Zhang JD, Pham ST, Kaliaev A, Andreu-Arasa VC, Dwyer BC, Farris CW, Hao H, Kedar S, Mian AZ, Murman DL, O’Shea SA, Paul AB, Rohatgi S, Saint-Hilaire MH, Sartor EA, Setty BN, Small JE, Swaminathan A, Taraschenko O, Yuan J, Zhou Y, Zhu S, Karjadi C, Ang TFA, Bargal SA, Plummer BA, Poston KL, Ahangaran M, Au R, Kolachalama VB. AI-based differential diagnosis of dementia etiologies on multimodal data. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.02.08.24302531. [PMID: 38585870 PMCID: PMC10996713 DOI: 10.1101/2024.02.08.24302531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Differential diagnosis of dementia remains a challenge in neurology due to symptom overlap across etiologies, yet it is crucial for formulating early, personalized management strategies. Here, we present an AI model that harnesses a broad array of data, including demographics, individual and family medical history, medication use, neuropsychological assessments, functional evaluations, and multimodal neuroimaging, to identify the etiologies contributing to dementia in individuals. The study, drawing on 51,269 participants across 9 independent, geographically diverse datasets, facilitated the identification of 10 distinct dementia etiologies. It aligns diagnoses with similar management strategies, ensuring robust predictions even with incomplete data. Our model achieved a micro-averaged area under the receiver operating characteristic curve (AUROC) of 0.94 in classifying individuals with normal cognition, mild cognitive impairment and dementia. Also, the micro-averaged AUROC was 0.96 in differentiating the dementia etiologies. Our model demonstrated proficiency in addressing mixed dementia cases, with a mean AUROC of 0.78 for two co-occurring pathologies. In a randomly selected subset of 100 cases, the AUROC of neurologist assessments augmented by our AI model exceeded neurologist-only evaluations by 26.25%. Furthermore, our model predictions aligned with biomarker evidence and its associations with different proteinopathies were substantiated through postmortem findings. Our framework has the potential to be integrated as a screening tool for dementia in various clinical settings and drug trials, with promising implications for person-level management.
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Affiliation(s)
- Chonghua Xue
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Department of Electrical & Computer Engineering, Boston University, MA, USA
| | - Sahana S. Kowshik
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Faculty of Computing & Data Sciences, Boston University, MA, USA
| | - Diala Lteif
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Department of Computer Science, Boston University, MA, USA
| | - Shreyas Puducheri
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Varuna H. Jasodanand
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Olivia T. Zhou
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Anika S. Walia
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Osman B. Guney
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Department of Electrical & Computer Engineering, Boston University, MA, USA
| | - J. Diana Zhang
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- School of Chemistry, University of New South Wales, Sydney, Australia
| | - Serena T. Pham
- Department of Radiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Artem Kaliaev
- Department of Radiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - V. Carlota Andreu-Arasa
- Department of Radiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Brigid C. Dwyer
- Department of Neurology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Chad W. Farris
- Department of Radiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Honglin Hao
- Department of Neurology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Sachin Kedar
- Departments of Neurology & Ophthalmology, Emory University School of Medicine, Atlanta, GA, USA
| | - Asim Z. Mian
- Department of Radiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Daniel L. Murman
- Department of Neurological Sciences, University of Nebraska Medical Center, Omaha, NE, USA
| | - Sarah A. O’Shea
- Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
| | - Aaron B. Paul
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Saurabh Rohatgi
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | | | - Emmett A. Sartor
- Department of Neurology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Bindu N. Setty
- Department of Radiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Juan E. Small
- Department of Radiology, Lahey Hospital & Medical Center, Burlington, MA, USA
| | | | - Olga Taraschenko
- Department of Neurological Sciences, University of Nebraska Medical Center, Omaha, NE, USA
| | - Jing Yuan
- Department of Neurology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Yan Zhou
- Department of Neurology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Shuhan Zhu
- Department of Neurology, Brigham & Women’s Hospital, Boston, MA, USA
| | - Cody Karjadi
- The Framingham Heart Study, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Ting Fang Alvin Ang
- The Framingham Heart Study, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Department of Anatomy and Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Sarah A. Bargal
- Department of Computer Science, Georgetown University, Washington DC, USA
| | | | | | - Meysam Ahangaran
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Rhoda Au
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Department of Neurology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- The Framingham Heart Study, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Department of Anatomy and Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Boston University Alzheimer’s Disease Research Center, Boston, MA, USA
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA
| | - Vijaya B. Kolachalama
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Faculty of Computing & Data Sciences, Boston University, MA, USA
- Department of Computer Science, Boston University, MA, USA
- Boston University Alzheimer’s Disease Research Center, Boston, MA, USA
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24
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Kim RT, Zhou L, Li Y, Krieger AC, Nordvig AS, Butler T, de Leon MJ, Chiang GC. Impaired sleep is associated with tau deposition on 18F-flortaucipir PET and accelerated cognitive decline, accounting for medications that affect sleep. J Neurol Sci 2024; 458:122927. [PMID: 38341949 PMCID: PMC10947806 DOI: 10.1016/j.jns.2024.122927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 01/06/2024] [Accepted: 02/06/2024] [Indexed: 02/13/2024]
Abstract
BACKGROUND Impaired sleep is commonly associated with Alzheimer's disease (AD), although the underlying mechanisms remain unclear. Furthermore, the moderating effects of sleep-affecting medications, which have been linked to AD pathology, are incompletely characterized. Using data from the Alzheimer's Disease Neuroimaging Initiative, we investigated whether a medical history of impaired sleep, informant-reported nighttime behaviors, and sleep-affecting medications are associated with beta-amyloid and tau deposition on PET and cognitive change, cross-sectionally and longitudinally. METHODS We included 964 subjects with 18F-florbetapir PET scans. Measures of sleep impairment and medication use were obtained from medical histories and the Neuropsychiatric Inventory Questionnaire. Multivariate models, adjusted for covariates, were used to assess associations among sleep-related features, beta-amyloid and tau, and cognition. Cortical tau deposition, categorized by Braak stage, was assessed using the standardized uptake value peak alignment (SUVP) method on 18F-flortaucipir PET. RESULTS Medical history of sleep impairment was associated with greater baseline tau in the meta-temporal, Braak 1, and Braak 4 regions (p = 0.04, p < 0.001, p = 0.025, respectively). Abnormal nighttime behaviors were also associated with greater baseline tau in the meta-temporal region (p = 0.024), and greater cognitive impairment, cross-sectionally (p = 0.007) and longitudinally (p < 0.001). Impaired sleep was not associated with baseline beta-amyloid (p > 0.05). Short-term use of selective serotonin reuptake inhibitors and benzodiazepines slightly weakened the sleep-tau relationship. CONCLUSIONS Sleep impairment was associated with tauopathy and cognitive decline, which could be linked to increased tau secretion from neuronal hyperactivity. Clinically, our results help identify high-risk individuals who could benefit from sleep-related interventions aimed to delay cognitive decline and AD.
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Affiliation(s)
- Ryan T Kim
- From the Department of Stem Cell and Regenerative Biology, Harvard University, Bauer-Sherman Fairchild Complex 7 Divinity Avenue, Cambridge, MA 02138, United States of America.
| | - Liangdong Zhou
- From the Department of Radiology, Brain Health Imaging Institute, Weill Cornell Medicine, NewYork-Presbyterian Hospital, 407 E 61(st) Street, New York, NY 10065, United States of America.
| | - Yi Li
- From the Department of Radiology, Brain Health Imaging Institute, Weill Cornell Medicine, NewYork-Presbyterian Hospital, 407 E 61(st) Street, New York, NY 10065, United States of America.
| | - Ana C Krieger
- From the Departments of Medicine and Neurology, Division of Sleep Neurology, Weill Cornell Medicine, NewYork-Presbyterian Hospital, 425 E 61st St., 5th Floor, New York, NY 10065, United States of America.
| | - Anna S Nordvig
- From the Department of Neurology, Alzheimer's Disease and Memory Disorders Program, Weill Cornell Medicine, NewYork-Presbyterian Hospital, 428 East 72(nd) Street Suite 500, New York, NY 10021, United States of America.
| | - Tracy Butler
- From the Department of Radiology, Brain Health Imaging Institute, Weill Cornell Medicine, NewYork-Presbyterian Hospital, 407 E 61(st) Street, New York, NY 10065, United States of America.
| | - Mony J de Leon
- From the Department of Radiology, Brain Health Imaging Institute, Weill Cornell Medicine, NewYork-Presbyterian Hospital, 407 E 61(st) Street, New York, NY 10065, United States of America.
| | - Gloria C Chiang
- From the Department of Radiology, Brain Health Imaging Institute, Weill Cornell Medicine, NewYork-Presbyterian Hospital, 407 E 61(st) Street, New York, NY 10065, United States of America; From the Department of Radiology, Division of Neuroradiology, Weill Cornell Medicine, NewYork-Presbyterian Hospital, 525 East 68th Street, Starr Pavilion, Box 141, New York, NY 10065, United States of America.
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Yang K, Liu L, Wen Y. The impact of Bayesian optimization on feature selection. Sci Rep 2024; 14:3948. [PMID: 38366092 PMCID: PMC10873405 DOI: 10.1038/s41598-024-54515-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: 10/15/2023] [Accepted: 02/13/2024] [Indexed: 02/18/2024] Open
Abstract
Feature selection is an indispensable step for the analysis of high-dimensional molecular data. Despite its importance, consensus is lacking on how to choose the most appropriate feature selection methods, especially when the performance of the feature selection methods itself depends on hyper-parameters. Bayesian optimization has demonstrated its advantages in automatically configuring the settings of hyper-parameters for various models. However, it remains unclear whether Bayesian optimization can benefit feature selection methods. In this research, we conducted extensive simulation studies to compare the performance of various feature selection methods, with a particular focus on the impact of Bayesian optimization on those where hyper-parameters tuning is needed. We further utilized the gene expression data obtained from the Alzheimer's Disease Neuroimaging Initiative to predict various brain imaging-related phenotypes, where various feature selection methods were employed to mine the data. We found through simulation studies that feature selection methods with hyper-parameters tuned using Bayesian optimization often yield better recall rates, and the analysis of transcriptomic data further revealed that Bayesian optimization-guided feature selection can improve the accuracy of disease risk prediction models. In conclusion, Bayesian optimization can facilitate feature selection methods when hyper-parameter tuning is needed and has the potential to substantially benefit downstream tasks.
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Affiliation(s)
- Kaixin Yang
- Department of Health Statistics, School of Public Health, Shanxi Medical University, No 56 Xinjian South Road, Yingze District, Taiyuan, Shanxi, China
| | - Long Liu
- Department of Health Statistics, School of Public Health, Shanxi Medical University, No 56 Xinjian South Road, Yingze District, Taiyuan, Shanxi, China.
| | - Yalu Wen
- Department of Statistics, University of Auckland, 38 Princes Street, Auckland Central, Auckland, 1010, New Zealand.
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26
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Tuena C, Pupillo C, Stramba-Badiale C, Stramba-Badiale M, Riva G. Predictive power of gait and gait-related cognitive measures in amnestic mild cognitive impairment: a machine learning analysis. Front Hum Neurosci 2024; 17:1328713. [PMID: 38348371 PMCID: PMC10859484 DOI: 10.3389/fnhum.2023.1328713] [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: 11/07/2023] [Accepted: 12/20/2023] [Indexed: 02/15/2024] Open
Abstract
Introduction Gait disorders and gait-related cognitive tests were recently linked to future Alzheimer's Disease (AD) dementia diagnosis in amnestic Mild Cognitive Impairment (aMCI). This study aimed to evaluate the predictive power of gait disorders and gait-related neuropsychological performances for future AD diagnosis in aMCI through machine learning (ML). Methods A sample of 253 aMCI (stable, converter) individuals were included. We explored the predictive accuracy of four predictors (gait profile plus MMSE, DSST, and TMT-B) previously identified as critical for the conversion from aMCI to AD within a 36-month follow-up. Supervised ML algorithms (Support Vector Machine [SVM], Logistic Regression, and k-Nearest Neighbors) were trained on 70% of the dataset, and feature importance was evaluated for the best algorithm. Results The SVM algorithm achieved the best performance. The optimized training set performance achieved an accuracy of 0.67 (sensitivity = 0.72; specificity = 0.60), improving to 0.70 on the test set (sensitivity = 0.79; specificity = 0.52). Feature importance revealed MMSE as the most important predictor in both training and testing, while gait type was important in the testing phase. Discussion We created a predictive ML model that is capable of identifying aMCI at high risk of AD dementia within 36 months. Our ML model could be used to quickly identify individuals at higher risk of AD, facilitating secondary prevention (e.g., cognitive and/or physical training), and serving as screening for more expansive and invasive tests. Lastly, our results point toward theoretically and practically sound evidence of mind and body interaction in AD.
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Affiliation(s)
- Cosimo Tuena
- Applied Technology for Neuro-Psychology Lab, IRCCS Istituto Auxologico Italiano, Milan, Italy
| | - Chiara Pupillo
- Applied Technology for Neuro-Psychology Lab, IRCCS Istituto Auxologico Italiano, Milan, Italy
| | - Chiara Stramba-Badiale
- Applied Technology for Neuro-Psychology Lab, IRCCS Istituto Auxologico Italiano, Milan, Italy
| | - Marco Stramba-Badiale
- Department of Geriatrics and Cardiovascular Medicine, IRCCS Istituto Auxologico Italiano, Milan, Italy
| | - Giuseppe Riva
- Applied Technology for Neuro-Psychology Lab, IRCCS Istituto Auxologico Italiano, Milan, Italy
- Humane Technology Lab, Università Cattolica del Sacro Cuore, Milan, Italy
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27
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Kumar G, Srivastava A, Kumar P, Srikrishna S, Singh VP. A chromogenic diarylethene-based probe for the detection of Cu 2+ in aqueous medium in Drosophila for early diagnosis of Alzheimer. Heliyon 2024; 10:e24074. [PMID: 38230249 PMCID: PMC10789622 DOI: 10.1016/j.heliyon.2024.e24074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 12/16/2023] [Accepted: 01/03/2024] [Indexed: 01/18/2024] Open
Abstract
A diarylethene-based probe (Z)-N'-((2-amino-5-chlorophenyl)(phenyl)methylene)-2-hydroxy benzohydrazide (KBH) has been proficiently developed and its structure has been confirmed by single crystal X-ray diffraction technique. It displays a selective and sensitive colorimetric sensing of Cu2+ ions in aqueous medium with a naked eye colour change from colourless to yellow. It exhibits a significantly low limit of detection as 1.5 nM. A plausible binding mechanism has been proposed using Job's plot, FT-IR, 1H NMR titration, HRMS and DFT studies. The chemosensor is effectively reversible and reusable with EDTA. Test strip kit and real water sample analysis have been shown to establish its practical applicability. Further, the potential of KBH for the early diagnosis of Cu2+ ion-induced amyloid toxicity has been investigated in eye imaginal disc of Alzheimer's disease model of Drosophila 3rd instar larvae. The in-vivo interaction of KBH with Cu2+ in gut tissues of Drosophila larvae establishes its sensing capability in biological system. Interestingly, the in-vivo detection of Cu2+ has been done using bright field imaging which eliminates the necessity of a fluorescent label, hence making the method highly economical.
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Affiliation(s)
- Gautam Kumar
- Department of Chemistry, Institute of Science, Banaras Hindu University, Varanasi, India
| | - Ananya Srivastava
- Department of Chemistry, Institute of Science, Banaras Hindu University, Varanasi, India
| | - Prabhat Kumar
- Department of Bio Chemistry, Institute of Science, Banaras Hindu University, Varanasi, India
| | - S. Srikrishna
- Department of Bio Chemistry, Institute of Science, Banaras Hindu University, Varanasi, India
| | - Vinod P. Singh
- Department of Chemistry, Institute of Science, Banaras Hindu University, Varanasi, India
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28
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Mueller SG. Traumatic Brain Injury and Post-Traumatic Stress Disorder and Their Influence on Development and Pattern of Alzheimer's Disease Pathology in Later Life. J Alzheimers Dis 2024; 98:1427-1441. [PMID: 38552112 DOI: 10.3233/jad-231183] [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: 04/20/2024]
Abstract
Background Traumatic brain injury (TBI) and posttraumatic stress disorder (PTSD) are potential risk factors for the development of dementia including Alzheimer's disease (AD) in later life. The findings of studies investigating this question are inconsistent though. Objective To investigate if these inconsistencies are caused by the existence of subgroups with different vulnerability for AD pathology and if these subgroups are characterized by atypical tau load/atrophy pattern. Methods The MRI and PET data of 89 subjects with or without previous TBI and/or PTSD from the DoD ADNI database were used to calculate an age-corrected gray matter tau mismatch metric (ageN-T mismatch-score and matrix) for each subject. This metric provides a measure to what degree regional tau accumulation drives regional gray matter atrophy (matrix) and can be used to calculate a summary score (score) reflecting the severity of AD pathology in an individual. Results The ageN-T mismatch summary score was positively correlated with whole brain beta-amyloid load and general cognitive function but not with PTSD or TBI severity. Hierarchical cluster analysis identified five different spatial patterns of tau-gray matter interactions. These clusters reflected the different stages of the typical AD tau progression pattern. None was exclusively associated with PTSD and/or TBI. Conclusions These findings suggest that a) although subsets of patients with PTSD and/or TBI develop AD-pathology, a history of TBI or PTSD alone or both is not associated with a significantly higher risk to develop AD pathology in later life. b) remote TBI or PTSD do not modify the typical AD pathology distribution pattern.
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Affiliation(s)
- Susanne G Mueller
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA
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29
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Fukui Y, Tadokoro K, Hamada M, Asada K, Lee LJ, Tachiki H, Morihara R, Abe K, Yamashita T. A Novel Peptidome Technology for the Diagnosis of Mild Cognitive Impairment and Alzheimer's Disease by Selected Reaction Monitoring. J Alzheimers Dis 2024; 100:219-228. [PMID: 38848173 DOI: 10.3233/jad-230915] [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: 06/09/2024]
Abstract
Background With the aging of populations worldwide, Alzheimer's disease (AD) has become a concern due to its high prevalence and the continued lack of established treatments. Early diagnosis is required as a preventive intervention to modify the disease's progression. In our previous study, we performed peptidomic analysis of serum samples obtained from AD patients and age-matched healthy subjects to seek peptide biomarker candidates for AD by using BLOTCHIP-MS analysis, and identified four peptides as AD biomarker candidates. Objective The objective was to validate the serum biomarker peptides to distinguish mild cognitive impairment (MCI) and AD in comparison to cognitively healthy controls using a new peptidome technology, the Dementia Risk Test. Methods We enrolled 195 subjects with normal cognitive function (NC; n = 70), MCI (n = 55), and AD (n = 70), The concentrations of cognitive impairment marker peptides (Fibrinogen α chain (FAC), Fibrinogen β chain (FBC), Plasma protease C1 inhibitor (PPC1I), α2-HS-glycoprotein (AHSG)) were quantified by using a selected reaction monitoring assay based on liquid chromatography-MS/MS. Results The present study confirmed that three peptides, FAC, FBC, and PPC1I, were significantly upregulated during the onset of AD. This three-peptide set was both highly sensitive in determining AD (sensitivity: 85.7%, specificity: 95.7%, AUC: 0.900) and useful in distinguishing MCI (sensitivity: 61.8%, specificity: 98.6%, AUC: 0.824) from NC. Conclusions In this validation study, we confirmed the high diagnostic potential of the three peptides identified in our previous study as candidate serum biomarkers for AD. The Dementia Risk Test may be a powerful tool for detecting AD-related pathological changes.
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Affiliation(s)
- Yusuke Fukui
- Department of Neurology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Kitaku, Okayama, Japan
| | - Koh Tadokoro
- Department of Neurology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Kitaku, Okayama, Japan
| | - Minaki Hamada
- Protosera, Inc., KENTO Innovation Park NK, Settsu, Osaka, Japan
| | - Kyoichi Asada
- Protosera, Inc., KENTO Innovation Park NK, Settsu, Osaka, Japan
| | - Lyang-Ja Lee
- Protosera, Inc., KENTO Innovation Park NK, Settsu, Osaka, Japan
| | | | - Ryuta Morihara
- Department of Neurology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Kitaku, Okayama, Japan
| | - Koji Abe
- Department of Neurology, National Center of Neurology and Psychiatry, Kodaira, Tokyo, Japan
| | - Toru Yamashita
- Department of Neurology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Kitaku, Okayama, Japan
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Salimi Y, Domingo-Fernández D, Hofmann-Apitius M, Birkenbihl C. Data-Driven Thresholding Statistically Biases ATN Profiling across Cohort Datasets. J Prev Alzheimers Dis 2024; 11:185-195. [PMID: 38230732 PMCID: PMC10995057 DOI: 10.14283/jpad.2023.100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Accepted: 07/02/2023] [Indexed: 01/18/2024]
Abstract
BACKGROUND While the amyloid/tau/neurodegeneration (ATN) framework has found wide application in Alzheimer's disease research, it is unclear if thresholds obtained using distinct thresholding methods are concordant within the same dataset and interchangeable across cohorts. OBJECTIVES To investigate the robustness of data-driven thresholding methods and ATN profiling across cohort datasets. DESIGN AND SETTING We evaluated the impact of thresholding methods on ATN profiles by applying five commonly-used methodologies across cohort datasets. We assessed the generalizability of disease patterns discovered within ATN profiles by clustering individuals from different cohorts who were assigned to the same ATN profile. PARTICIPANTS AND MEASUREMENTS Participants with available CSF amyloid-β 1-42, phosphorylated tau, and total tau measurements were included from eleven AD cohort studies. RESULTS We observed high variability among obtained ATN thresholds, both across methods and datasets that impacted the resulting profile assignments of participants significantly. Clustering participants from different cohorts within the same ATN category indicated that identified disease patterns were comparable across most cohorts and biases introduced through distinct thresholding and data representations remained insignificant in most ATN profiles. CONLUSION Thresholding method selection is a decision of statistical relevance that will inevitably bias the resulting profiling and affect its sensitivity and specificity. Thresholds are likely not directly interchangeable between independent cohorts. To apply the ATN framework as an actionable and robust profiling scheme, a comprehensive understanding of the impact of used thresholding methods, their statistical implications, and a validation of results is crucial.
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Affiliation(s)
- Y. Salimi
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, 53757 Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, 53115 Germany
- Schloß Birlinghoven, Sankt Augustin, 53757 Germany
| | - D. Domingo-Fernández
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, 53757 Germany
| | - M. Hofmann-Apitius
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, 53757 Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, 53115 Germany
| | - C. Birkenbihl
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, 53757 Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, 53115 Germany
- Schloß Birlinghoven, Sankt Augustin, 53757 Germany
| | - the Alzheimer’s Disease Neuroimaging Initiative
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, 53757 Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, 53115 Germany
- Schloß Birlinghoven, Sankt Augustin, 53757 Germany
| | - the Japanese Alzheimer’s Disease Neuroimaging Initiative
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, 53757 Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, 53115 Germany
- Schloß Birlinghoven, Sankt Augustin, 53757 Germany
| | - the Alzheimer’s Disease Repository Without Borders Investigators
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, 53757 Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, 53115 Germany
- Schloß Birlinghoven, Sankt Augustin, 53757 Germany
| | - the European Prevention of Alzheimer’s Disease (EPAD) Consortium
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, 53757 Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, 53115 Germany
- Schloß Birlinghoven, Sankt Augustin, 53757 Germany
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31
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Wegner P, Balabin H, Ay MC, Bauermeister S, Killin L, Gallacher J, Hofmann-Apitius M, Salimi Y. Semantic Harmonization of Alzheimer's Disease Datasets Using AD-Mapper. J Alzheimers Dis 2024; 99:1409-1423. [PMID: 38759012 PMCID: PMC11191441 DOI: 10.3233/jad-240116] [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: 04/18/2024] [Indexed: 05/19/2024]
Abstract
Background Despite numerous past endeavors for the semantic harmonization of Alzheimer's disease (AD) cohort studies, an automatic tool has yet to be developed. Objective As cohort studies form the basis of data-driven analysis, harmonizing them is crucial for cross-cohort analysis. We aimed to accelerate this task by constructing an automatic harmonization tool. Methods We created a common data model (CDM) through cross-mapping data from 20 cohorts, three CDMs, and ontology terms, which was then used to fine-tune a BioBERT model. Finally, we evaluated the model using three previously unseen cohorts and compared its performance to a string-matching baseline model. Results Here, we present our AD-Mapper interface for automatic harmonization of AD cohort studies, which outperformed a string-matching baseline on previously unseen cohort studies. We showcase our CDM comprising 1218 unique variables. Conclusion AD-Mapper leverages semantic similarities in naming conventions across cohorts to improve mapping performance.
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Affiliation(s)
- Philipp Wegner
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
- Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Helena Balabin
- Department of Neurosciences, Laboratory for Cognitive Neurology, KU Leuven, Leuven, Belgium
- Department of Computer Science, Language Intelligence and Information Retrieval Lab, KU Leuven, Leuven, Belgium
| | - Mehmet Can Ay
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Sarah Bauermeister
- Department of Psychiatry, Warneford Hospital, University of Oxford, Oxford, UK
| | - Lewis Killin
- SYNAPSE Research Management Partners, Barcelona, Spain
| | - John Gallacher
- Department of Psychiatry, Warneford Hospital, University of Oxford, Oxford, UK
| | - Martin Hofmann-Apitius
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Yasamin Salimi
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
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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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El Haffaf LM, Ronat L, Cannizzaro A, Hanganu A. Associations Between Hyperactive Neuropsychiatric Symptoms and Brain Morphology in Mild Cognitive Impairment and Alzheimer's Disease. J Alzheimers Dis 2024; 97:841-853. [PMID: 38143342 DOI: 10.3233/jad-220857] [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: 12/26/2023]
Abstract
BACKGROUND Hyperactive neuropsychiatric symptoms (NPS) (i.e., agitation, disinhibition, and irritability) are among the most challenging symptoms to manage in Alzheimer's disease (AD). However, their underlying brain correlates have been poorly studied. OBJECTIVE We aimed to investigate the associations between the total score of hyperactive NPS and brain structures in participants with AD, mild cognitive impairment (MCI), and cognitively normal older adults (CN). METHODS Neuropsychiatric and 3T MRI data from 216 AD, 564 MCI, and 660 CN participants were extracted from the Alzheimer's Disease Neuroimaging Initiative database. To define NPS and brain structures' associations, we fitted a general linear model (GLM) in two ways: 1) an overall GLM including all three groups (AD, MCI, CN) and 2) three pair-wise GLMs (AD versus MCI, MCI versus CN, AD versus CN). The cortical changes as a function of NPS total score were investigated using multiple regression analyses. RESULTS Results from the overall GLM include associations between 1) agitation and the right parietal supramarginal surface area in the MCI-CN contrast, 2) disinhibition and the cortical thickness of the right frontal pars opercularis and temporal inferior in the AD-MCI contrast, and 3) irritability and the right frontal pars opercularis, frontal superior, and temporal superior volumes in the MCI-CN contrast. CONCLUSIONS Our study shows that each hyperactive NPS is associated with distinct brain regions in AD, MCI, and CN (groups with different levels of cognitive performance). This suggests that each NPS is associated with a unique signature of brain morphology, including variations in volume, thickness, or area.
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Affiliation(s)
- Lyna Mariam El Haffaf
- Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, CIUSSS du Centre-Sud-de-l'Ile-de-Montreal, Montréal, QC, Canada
- Département de Psychologie, Faculté des Arts et des Sciences, Université de Montréal, Montréal, QC, Canada
| | - Lucas Ronat
- Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, CIUSSS du Centre-Sud-de-l'Ile-de-Montreal, Montréal, QC, Canada
- Département de Médecine, Faculté de Médecine, Université de Montréal, Montréal, QC, Canada
| | - Adriana Cannizzaro
- Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, CIUSSS du Centre-Sud-de-l'Ile-de-Montreal, Montréal, QC, Canada
- Département de Psychologie, Faculté des Arts et des Sciences, Université de Montréal, Montréal, QC, Canada
| | - Alexandru Hanganu
- Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, CIUSSS du Centre-Sud-de-l'Ile-de-Montreal, Montréal, QC, Canada
- Département de Psychologie, Faculté des Arts et des Sciences, Université de Montréal, Montréal, QC, Canada
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Nguyen HXT, Bradley K, McNamara BJ, Watson R, Malay R, LoGiudice D. Risk, protective, and biomarkers of dementia in Indigenous peoples: A systematic review. Alzheimers Dement 2024; 20:563-592. [PMID: 37746888 PMCID: PMC10917055 DOI: 10.1002/alz.13458] [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: 02/23/2023] [Revised: 08/10/2023] [Accepted: 08/13/2023] [Indexed: 09/26/2023]
Abstract
INTRODUCTION Dementia is an emergent health priority for Indigenous peoples worldwide, yet little is known about disease drivers and protective factors. METHODS Database searches were conducted in March 2022 to identify original publications on risk, protective, genetic, neuroradiological, and biological factors related to dementia and cognitive impairment involving Indigenous peoples. RESULTS Modifiable risk factors featured across multiple studies include childhood adversity, hearing loss, low education attainment, unskilled work history, stroke, head injury, epilepsy, diabetes, hypertension, hyperlipidemia, depression, low BMI, poor mobility, and continence issues. Non-modifiable risk factors included increasing age, sex, and genetic polymorphisms. Education, ex-smoking, physical and social activity, and engagement with cultural or religious practices were highlighted as potential protective factors. There is a paucity of research on dementia biomarkers involving Indigenous peoples. DISCUSSION Greater understanding of modifiable factors and biomarkers of dementia can assist in strength-based models to promote healthy ageing and cognition for Indigenous peoples.
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Affiliation(s)
- Huong X. T. Nguyen
- Department of MedicineRoyal Melbourne HospitalMelbourneVictoriaAustralia
- Department of Population Health and ImmunityWalter and Eliza Hall Institute of Medical ResearchMelbourneVictoriaAustralia
| | - Kate Bradley
- Department of MedicineRoyal Melbourne HospitalMelbourneVictoriaAustralia
| | - Bridgette J. McNamara
- Centre for Epidemiology and BiostatisticsUniversity of MelbourneVictoriaAustralia
- Barwon South‐West Public Health UnitBarwon HealthGeelongVictoriaAustralia
| | - Rosie Watson
- Department of MedicineRoyal Melbourne HospitalMelbourneVictoriaAustralia
- Department of Population Health and ImmunityWalter and Eliza Hall Institute of Medical ResearchMelbourneVictoriaAustralia
| | - Roslyn Malay
- Western Australian Centre for Health and AgeingUniversity of Western AustraliaPerthWAAustralia
| | - Dina LoGiudice
- Department of MedicineRoyal Melbourne HospitalMelbourneVictoriaAustralia
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Ou Z, Lu X, Gu Y. HCS-Net: Multi-level deformation strategy combined with quadruple attention for image registration. Comput Biol Med 2024; 168:107832. [PMID: 38071839 DOI: 10.1016/j.compbiomed.2023.107832] [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: 07/20/2023] [Revised: 11/09/2023] [Accepted: 12/04/2023] [Indexed: 01/10/2024]
Abstract
BACKGROUND AND OBJECTIVE Non-rigid image registration plays a significant role in computer-aided diagnosis and surgical navigation for brain diseases. Registration methods that utilize convolutional neural networks (CNNs) have shown excellent accuracy when applied to brain magnetic resonance images (MRI). However, CNNs have limitations in understanding long-range spatial relationships in images, which makes it challenging to incorporate contextual information. And in intricate image registration tasks, it is difficult to achieve a satisfactory dense prediction field, resulting in poor registration performance. METHODS This paper proposes a multi-level deformable unsupervised registration model that combines Transformer and CNN to achieve non-rigid registration of brain MRI. Firstly, utilizing a dual encoder structure to establish the dependency relationship between the global features of two images and to merge features of varying scales, as well as to preserve the relative spatial position information of feature maps at different scales. Then the proposed multi-level deformation strategy utilizes different deformable fields of varying resolutions generated by the decoding structure to progressively deform the moving image. Ultimately, the proposed quadruple attention module is incorporated into the decoding structure to merge feature information from various directions and emphasize the spatial features in the dominant channels. RESULTS The experimental results on multiple brain MR datasets demonstrate that the promising network could provide accurate registration and is comparable to state-of-the-art methods. CONCLUSION The proposed registration model can generate superior deformable fields and achieve more precise registration effects, enhancing the auxiliary role of medical image registration in various fields and advancing the development of computer-aided diagnosis, surgical navigation, and related domains.
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Affiliation(s)
- Zhuolin Ou
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, 014010, China
| | - Xiaoqi Lu
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, 014010, China; School of Information Engineering, Inner Mongolia University of Technology, Hohhot, 010051, China.
| | - Yu Gu
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, 014010, China.
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Sanvito F, Kaufmann TJ, Cloughesy TF, Wen PY, Ellingson BM. Standardized brain tumor imaging protocols for clinical trials: current recommendations and tips for integration. FRONTIERS IN RADIOLOGY 2023; 3:1267615. [PMID: 38152383 PMCID: PMC10751345 DOI: 10.3389/fradi.2023.1267615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 11/24/2023] [Indexed: 12/29/2023]
Abstract
Standardized MRI acquisition protocols are crucial for reducing the measurement and interpretation variability associated with response assessment in brain tumor clinical trials. The main challenge is that standardized protocols should ensure high image quality while maximizing the number of institutions meeting the acquisition requirements. In recent years, extensive effort has been made by consensus groups to propose different "ideal" and "minimum requirements" brain tumor imaging protocols (BTIPs) for gliomas, brain metastases (BM), and primary central nervous system lymphomas (PCSNL). In clinical practice, BTIPs for clinical trials can be easily integrated with additional MRI sequences that may be desired for clinical patient management at individual sites. In this review, we summarize the general concepts behind the choice and timing of sequences included in the current recommended BTIPs, we provide a comparative overview, and discuss tips and caveats to integrate additional clinical or research sequences while preserving the recommended BTIPs. Finally, we also reflect on potential future directions for brain tumor imaging in clinical trials.
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Affiliation(s)
- Francesco Sanvito
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, United States
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | | | - Timothy F. Cloughesy
- UCLA Neuro-Oncology Program, University of California, Los Angeles, Los Angeles, CA, United States
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Patrick Y. Wen
- Center for Neuro-Oncology, Dana-Farber/Brigham and Women’s Cancer Center, Harvard Medical School, Boston, MA, United States
| | - Benjamin M. Ellingson
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, United States
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
- Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California, Los Angeles, Los Angeles, CA, United States
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
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Ortega-Cruz D, Bress KS, Gazula H, Rabano A, Iglesias JE, Strange BA. Three-dimensional histology reveals dissociable human hippocampal long axis gradients of Alzheimer's pathology. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.05.570038. [PMID: 38105985 PMCID: PMC10723286 DOI: 10.1101/2023.12.05.570038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
INTRODUCTION Three-dimensional (3D) histology analyses are essential to overcome sampling variability and understand pathological differences beyond the dissection axis. We present Path2MR, the first pipeline allowing 3D reconstruction of sparse human histology without an MRI reference. We implemented Path2MR with post-mortem hippocampal sections to explore pathology gradients in Alzheimer's Disease. METHODS Blockface photographs of brain hemisphere slices are used for 3D reconstruction, from which an MRI-like image is generated using machine learning. Histology sections are aligned to the reconstructed hemisphere and subsequently to an atlas in standard space. RESULTS Path2MR successfully registered histological sections to their anatomical position along the hippocampal longitudinal axis. Combined with histopathology quantification, we found an expected peak of tau pathology at the anterior end of the hippocampus, while amyloid-β displayed a quadratic anterior-posterior distribution. CONCLUSION Path2MR, which enables 3D histology using any brain bank dataset, revealed significant differences along the hippocampus between tau and amyloid-β.
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Affiliation(s)
- Diana Ortega-Cruz
- Laboratory for Clinical Neuroscience, Center for Biomedical Technology, Universidad Politécnica de Madrid, IdISSC, 28223, Madrid, Spain
- Alzheimer's Disease Research Unit, CIEN Foundation, Queen Sofia Foundation Alzheimer Center, 28031, Madrid, Spain
| | - Kimberly S Bress
- Alzheimer's Disease Research Unit, CIEN Foundation, Queen Sofia Foundation Alzheimer Center, 28031, Madrid, Spain
- Current address: Vanderbilt University School of Medicine, 37232, Nashville, TN, USA
| | - Harshvardhan Gazula
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, 02129, Boston, MA, USA
| | - Alberto Rabano
- Alzheimer's Disease Research Unit, CIEN Foundation, Queen Sofia Foundation Alzheimer Center, 28031, Madrid, Spain
| | - Juan Eugenio Iglesias
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, 02129, Boston, MA, USA
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 02139, Boston, MA, USA
- Centre for Medical Image Computing, University College London, WC1V 6LJ, London, United Kingdom
| | - Bryan A Strange
- Laboratory for Clinical Neuroscience, Center for Biomedical Technology, Universidad Politécnica de Madrid, IdISSC, 28223, Madrid, Spain
- Alzheimer's Disease Research Unit, CIEN Foundation, Queen Sofia Foundation Alzheimer Center, 28031, Madrid, Spain
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Liu J, van Beusekom H, Bu X, Chen G, Henrique Rosado de Castro P, Chen X, Chen X, Clarkson AN, Farr TD, Fu Y, Jia J, Jolkkonen J, Kim WS, Korhonen P, Li S, Liang Y, Liu G, Liu G, Liu Y, Malm T, Mao X, Oliveira JM, Modo MM, Ramos‐Cabrer P, Ruscher K, Song W, Wang J, Wang X, Wang Y, Wu H, Xiong L, Yang Y, Ye K, Yu J, Zhou X, Zille M, Masters CL, Walczak P, Boltze J, Ji X, Wang Y. Preserving cognitive function in patients with Alzheimer's disease: The Alzheimer's disease neuroprotection research initiative (ADNRI). NEUROPROTECTION 2023; 1:84-98. [PMID: 38223913 PMCID: PMC10783281 DOI: 10.1002/nep3.23] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 08/07/2023] [Accepted: 08/08/2023] [Indexed: 01/16/2024]
Abstract
The global trend toward aging populations has resulted in an increase in the occurrence of Alzheimer's disease (AD) and associated socioeconomic burdens. Abnormal metabolism of amyloid-β (Aβ) has been proposed as a significant pathomechanism in AD, supported by results of recent clinical trials using anti-Aβ antibodies. Nonetheless, the cognitive benefits of the current treatments are limited. The etiology of AD is multifactorial, encompassing Aβ and tau accumulation, neuroinflammation, demyelination, vascular dysfunction, and comorbidities, which collectively lead to widespread neurodegeneration in the brain and cognitive impairment. Hence, solely removing Aβ from the brain may be insufficient to combat neurodegeneration and preserve cognition. To attain effective treatment for AD, it is necessary to (1) conduct extensive research on various mechanisms that cause neurodegeneration, including advances in neuroimaging techniques for earlier detection and a more precise characterization of molecular events at scales ranging from cellular to the full system level; (2) identify neuroprotective intervention targets against different neurodegeneration mechanisms; and (3) discover novel and optimal combinations of neuroprotective intervention strategies to maintain cognitive function in AD patients. The Alzheimer's Disease Neuroprotection Research Initiative's objective is to facilitate coordinated, multidisciplinary efforts to develop systemic neuroprotective strategies to combat AD. The aim is to achieve mitigation of the full spectrum of pathological processes underlying AD, with the goal of halting or even reversing cognitive decline.
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Affiliation(s)
- Jie Liu
- Department of Neurology, Daping HospitalThird Military Medical UniversityChongqingChina
- Chongqing Key Laboratory of Ageing and Brain DiseasesChongqingChina
| | - Heleen van Beusekom
- Division of Experimental Cardiology, Department of Cardiology, Erasmus MCUniversity Medical CenterRotterdamThe Netherlands
| | - Xian‐Le Bu
- Department of Neurology, Daping HospitalThird Military Medical UniversityChongqingChina
- Chongqing Key Laboratory of Ageing and Brain DiseasesChongqingChina
- Institute of Brain and IntelligenceThird Military Medical UniversityChongqingChina
| | - Gong Chen
- Guangdong‐HongKong‐Macau Institute of CNS Regeneration (GHMICR)Jinan UniversityGuangzhouGuangdongChina
| | | | - Xiaochun Chen
- Fujian Key Laboratory of Molecular Neurology, Department of Neurology and Geriatrics, Fujian Institute of Geriatrics, Fujian Medical University Union Hospital, Institute of NeuroscienceFujian Medical UniversityFuzhouFujianChina
| | - Xiaowei Chen
- Institute of Brain and IntelligenceThird Military Medical UniversityChongqingChina
- Guangyang Bay LaboratoryChongqing Institute for Brain and IntelligenceChongqingChina
- Center for Excellence in Brain Science and Intelligence TechnologyChinese Academy of SciencesShanghaiChina
| | - Andrew N. Clarkson
- Department of Anatomy, Brain Health Research Centre and Brain Research New ZealandUniversity of OtagoDunedinNew Zealand
| | - Tracy D. Farr
- School of Life SciencesUniversity of NottinghamNottinghamUK
| | - Yuhong Fu
- Brain and Mind Centre & School of Medical SciencesThe University of SydneySydneyNew South WalesAustralia
| | - Jianping Jia
- Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, National Clinical Research Center for Geriatric DiseasesCapital Medical UniversityBeijingChina
| | - Jukka Jolkkonen
- A.I. Virtanen Institute for Molecular SciencesUniversity of Eastern FinlandKuopioFinland
| | - Woojin Scott Kim
- Brain and Mind Centre & School of Medical SciencesThe University of SydneySydneyNew South WalesAustralia
| | - Paula Korhonen
- A.I. Virtanen Institute for Molecular SciencesUniversity of Eastern FinlandKuopioFinland
| | - Shen Li
- Department of Neurology and Psychiatry, Beijing Shijitan HospitalCapital Medical UniversityBeijingChina
| | - Yajie Liang
- Department of Diagnostic Radiology and Nuclear MedicineUniversity of Maryland School of MedicineBaltimoreMarylandUSA
| | - Guang‐Hui Liu
- University of Chinese Academy of SciencesBeijingChina
- State Key Laboratory of Membrane Biology, Institute of ZoologyChinese Academy of SciencesBeijingChina
| | - Guiyou Liu
- Beijing Institute of Brain Disorders, Laboratory of Brain Disorders, Ministry of Science and Technology, Collaborative Innovation Center for Brain DisordersCapital Medical UniversityBeijingChina
| | - Yu‐Hui Liu
- Department of Neurology, Daping HospitalThird Military Medical UniversityChongqingChina
- Chongqing Key Laboratory of Ageing and Brain DiseasesChongqingChina
- Institute of Brain and IntelligenceThird Military Medical UniversityChongqingChina
| | - Tarja Malm
- A.I. Virtanen Institute for Molecular SciencesUniversity of Eastern FinlandKuopioFinland
| | - Xiaobo Mao
- Institute for Cell Engineering, Department of NeurologyThe Johns Hopkins University School of MedicineBaltimoreMarylandUSA
| | - Joaquim Miguel Oliveira
- 3B's Research Group, I3Bs—Research Institute on Biomaterials, Biodegradables and Biomimetics, Headquarters of the European Institute of Excellence on Tissue Engineering and Regenerative MedicineUniversity of MinhoGuimarãesPortugal
- ICVS/3B's—PT Government Associate LaboratoryBraga/GuimarãesPortugal
| | - Mike M. Modo
- Department of Bioengineering, McGowan Institute for Regenerative MedicineUniversity of PittsburghPittsburghPennsylvaniaUSA
- Department of Radiology, McGowan Institute for Regenerative MedicineUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - Pedro Ramos‐Cabrer
- Magnetic Resonance Imaging LaboratoryCIC BiomaGUNE Research Center, Basque Research and Technology Alliance (BRTA)Donostia‐San SebastianSpain
| | - Karsten Ruscher
- Laboratory for Experimental Brain Research, Division of Neurosurgery, Department of Clinical SciencesLund UniversityLundSweden
| | - Weihong Song
- Institute of Aging, Key Laboratory of Alzheimer's Disease of Zhejiang Province. Zhejiang Clinical Research Center for Mental Disorders, School of Mental Health and The Affiliated Kangning Hospital, Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health)Wenzhou Medical UniversityZhejiangChina
| | - Jun Wang
- Department of Neurology, Daping HospitalThird Military Medical UniversityChongqingChina
- Chongqing Key Laboratory of Ageing and Brain DiseasesChongqingChina
| | - Xuanyue Wang
- School of Optometry and Vision ScienceUniversity of New South WalesSydneyNew South WalesAustralia
| | - Yun Wang
- Neuroscience Research Institute, Department of Neurobiology, School of Basic, Medical Sciences, Key Laboratory for Neuroscience, Ministry of Education/National, Health Commission and State Key Laboratory of Natural and Biomimetic DrugsPeking UniversityBeijingChina
- PKU‐IDG/McGovern Institute for Brain ResearchPeking UniversityBeijingChina
| | - Haitao Wu
- Department of NeurobiologyBeijing Institute of Basic Medical SciencesBeijingChina
| | - Lize Xiong
- Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Clinical Research Center for Anesthesiology and Perioperative Medicine, Translational Research Institute of Brain and Brain‐Like Intelligence, Shanghai Fourth People's Hospital, School of MedicineTongji UniversityShanghaiChina
| | - Yi Yang
- Department of NeurologyThe First Hospital of Jilin University, Chang ChunJilinChina
| | - Keqiang Ye
- Faculty of Life and Health SciencesBrain Cognition and Brain Disease Institute (BCBDI), Shenzhen Institute of Advanced TechnologyShenzhenChina
| | - Jin‐Tai Yu
- State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Department of Neurology and Institute of Neurology, Huashan Hospital, Shanghai Medical CollegeFudan UniversityShanghaiChina
| | - Xin‐Fu Zhou
- Division of Health Sciences, School of Pharmacy and Medical Sciences and Sansom InstituteUniversity of South AustraliaAdelaideSouth AustraliaAustralia
- Suzhou Auzone BiotechSuzhouJiangsuChina
| | - Marietta Zille
- Department of Pharmaceutical Sciences, Division of Pharmacology and ToxicologyUniversity of ViennaViennaAustria
| | - Colin L. Masters
- The Florey InstituteThe University of Melbourne, ParkvilleVictoriaAustralia
| | - Piotr Walczak
- Department of Diagnostic Radiology and Nuclear MedicineUniversity of Maryland School of MedicineBaltimoreMarylandUSA
| | | | - Xunming Ji
- Department of NeurosurgeryXuanwu Hospital, Capital Medical UniversityBeijingChina
| | - Yan‐Jiang Wang
- Department of Neurology, Daping HospitalThird Military Medical UniversityChongqingChina
- Chongqing Key Laboratory of Ageing and Brain DiseasesChongqingChina
- Institute of Brain and IntelligenceThird Military Medical UniversityChongqingChina
- Guangyang Bay LaboratoryChongqing Institute for Brain and IntelligenceChongqingChina
- Center for Excellence in Brain Science and Intelligence TechnologyChinese Academy of SciencesShanghaiChina
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Borchert RJ, Azevedo T, Badhwar A, Bernal J, Betts M, Bruffaerts R, Burkhart MC, Dewachter I, Gellersen HM, Low A, Lourida I, Machado L, Madan CR, Malpetti M, Mejia J, Michopoulou S, Muñoz-Neira C, Pepys J, Peres M, Phillips V, Ramanan S, Tamburin S, Tantiangco HM, Thakur L, Tomassini A, Vipin A, Tang E, Newby D, Ranson JM, Llewellyn DJ, Veldsman M, Rittman T. Artificial intelligence for diagnostic and prognostic neuroimaging in dementia: A systematic review. Alzheimers Dement 2023; 19:5885-5904. [PMID: 37563912 DOI: 10.1002/alz.13412] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 05/18/2023] [Accepted: 06/02/2023] [Indexed: 08/12/2023]
Abstract
INTRODUCTION Artificial intelligence (AI) and neuroimaging offer new opportunities for diagnosis and prognosis of dementia. METHODS We systematically reviewed studies reporting AI for neuroimaging in diagnosis and/or prognosis of cognitive neurodegenerative diseases. RESULTS A total of 255 studies were identified. Most studies relied on the Alzheimer's Disease Neuroimaging Initiative dataset. Algorithmic classifiers were the most commonly used AI method (48%) and discriminative models performed best for differentiating Alzheimer's disease from controls. The accuracy of algorithms varied with the patient cohort, imaging modalities, and stratifiers used. Few studies performed validation in an independent cohort. DISCUSSION The literature has several methodological limitations including lack of sufficient algorithm development descriptions and standard definitions. We make recommendations to improve model validation including addressing key clinical questions, providing sufficient description of AI methods and validating findings in independent datasets. Collaborative approaches between experts in AI and medicine will help achieve the promising potential of AI tools in practice. HIGHLIGHTS There has been a rapid expansion in the use of machine learning for diagnosis and prognosis in neurodegenerative disease Most studies (71%) relied on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset with no other individual dataset used more than five times There has been a recent rise in the use of more complex discriminative models (e.g., neural networks) that performed better than other classifiers for classification of AD vs healthy controls We make recommendations to address methodological considerations, addressing key clinical questions, and validation We also make recommendations for the field more broadly to standardize outcome measures, address gaps in the literature, and monitor sources of bias.
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Affiliation(s)
- Robin J Borchert
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
- Department of Radiology, University of Cambridge, Cambridge, UK
| | - Tiago Azevedo
- Department of Computer Science and Technology, University of Cambridge, Cambridge, UK
| | - AmanPreet Badhwar
- Department of Pharmacology and Physiology, University of Montreal, Montreal, Canada
- Centre de recherche de l'Institut Universitaire de Gériatrie (CRIUGM), Montreal, Canada
| | - Jose Bernal
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK
- Institute of Cognitive Neurology and Dementia Research, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Matthew Betts
- Institute of Cognitive Neurology and Dementia Research, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
- Center for Behavioral Brain Sciences, University of Magdeburg, Magdeburg, Germany
| | - Rose Bruffaerts
- Computational Neurology, Experimental Neurobiology Unit, Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
- Biomedical Research Institute, Hasselt University, Diepenbeek, Belgium
| | | | - Ilse Dewachter
- Biomedical Research Institute, Hasselt University, Diepenbeek, Belgium
| | - Helena M Gellersen
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
- Department of Psychology, University of Cambridge, Cambridge, UK
| | - Audrey Low
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | | | - Luiza Machado
- Department of Biochemistry, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | | | - Maura Malpetti
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Jhony Mejia
- Department of Biomedical Engineering, Universidad de Los Andes, Bogotá, Colombia
| | - Sofia Michopoulou
- Imaging Physics, University Hospital Southampton NHS Foundation Trust, Southampton, UK
| | - Carlos Muñoz-Neira
- Research into Memory, Brain sciences and dementia Group (ReMemBr Group), Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Artificial Intelligence & Computational Neuroscience Group (AICN Group), Sheffield Institute for Translational Neuroscience (SITraN), Department of Neuroscience, University of Sheffield, Sheffield, UK
| | - Jack Pepys
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy
| | - Marion Peres
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | | | - Siddharth Ramanan
- Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - Stefano Tamburin
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
| | | | - Lokendra Thakur
- Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Broad Institute of MIT and Harvard, Cambridge, UK
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Alessandro Tomassini
- Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | | | - Eugene Tang
- Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Danielle Newby
- Department of Psychiatry, University of Oxford, Oxford, UK
| | | | - David J Llewellyn
- University of Exeter Medical School, Exeter, UK
- Alan Turing Institute, London, UK
| | - Michele Veldsman
- Department of Experimental Psychology, University of Oxford, Oxford, UK
| | - Timothy Rittman
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
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Garcia-Martin E, Jimeno-Huete D, Dongil-Moreno FJ, Boquete L, Sánchez-Morla EM, Miguel-Jiménez JM, López-Dorado A, Vilades E, Fuertes MI, Pueyo A, Ortiz del Castillo M. Differential Study of Retinal Thicknesses in the Eyes of Alzheimer's Patients, Multiple Sclerosis Patients and Healthy Subjects. Biomedicines 2023; 11:3126. [PMID: 38137347 PMCID: PMC10740772 DOI: 10.3390/biomedicines11123126] [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: 09/30/2023] [Revised: 11/18/2023] [Accepted: 11/22/2023] [Indexed: 12/24/2023] Open
Abstract
Multiple sclerosis (MS) and Alzheimer's disease (AD) cause retinal thinning that is detectable in vivo using optical coherence tomography (OCT). To date, no papers have compared the two diseases in terms of the structural differences they produce in the retina. The purpose of this study is to analyse and compare the neuroretinal structure in MS patients, AD patients and healthy subjects using OCT. Spectral domain OCT was performed on 21 AD patients, 33 MS patients and 19 control subjects using the Posterior Pole protocol. The area under the receiver operating characteristic (AUROC) curve was used to analyse the differences between the cohorts in nine regions of the retinal nerve fibre layer (RNFL), ganglion cell layer (GCL), inner plexiform layer (IPL) and outer nuclear layer (ONL). The main differences between MS and AD are found in the ONL, in practically all the regions analysed (AUROCFOVEAL = 0.80, AUROCPARAFOVEAL = 0.85, AUROCPERIFOVEAL = 0.80, AUROC_PMB = 0.77, AUROCPARAMACULAR = 0.85, AUROCINFERO_NASAL = 0.75, AUROCINFERO_TEMPORAL = 0.83), and in the paramacular zone (AUROCPARAMACULAR = 0.75) and infero-temporal quadrant (AUROCINFERO_TEMPORAL = 0.80) of the GCL. In conclusion, our findings suggest that OCT data analysis could facilitate the differential diagnosis of MS and AD.
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Affiliation(s)
- Elena Garcia-Martin
- Department of Ophthalmology, Miguel Servet University Hospital, 50009 Zaragoza, Spain; (E.V.); (M.I.F.); (A.P.)
- Miguel Servet Ophthalmology Innovation and Research Group (GIMSO), Aragon Institute for Health Research (IIS Aragon), Biotech Vision SLP (Spin-Off Company), University of Zaragoza, 50009 Zaragoza, Spain
| | - Daniel Jimeno-Huete
- Biomedical Engineering Group, Department of Electronics, University of Alcalá, 28871 Alcalá de Henares, Spain; (D.J.-H.); (F.J.D.-M.); (J.M.M.-J.); (A.L.-D.)
| | - Francisco J. Dongil-Moreno
- Biomedical Engineering Group, Department of Electronics, University of Alcalá, 28871 Alcalá de Henares, Spain; (D.J.-H.); (F.J.D.-M.); (J.M.M.-J.); (A.L.-D.)
| | - Luciano Boquete
- Biomedical Engineering Group, Department of Electronics, University of Alcalá, 28871 Alcalá de Henares, Spain; (D.J.-H.); (F.J.D.-M.); (J.M.M.-J.); (A.L.-D.)
| | - Eva M. Sánchez-Morla
- Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, 28007 Madrid, Spain
- School of Medicine, Universidad Complutense, 28040 Madrid, Spain
| | - Juan M. Miguel-Jiménez
- Biomedical Engineering Group, Department of Electronics, University of Alcalá, 28871 Alcalá de Henares, Spain; (D.J.-H.); (F.J.D.-M.); (J.M.M.-J.); (A.L.-D.)
| | - Almudena López-Dorado
- Biomedical Engineering Group, Department of Electronics, University of Alcalá, 28871 Alcalá de Henares, Spain; (D.J.-H.); (F.J.D.-M.); (J.M.M.-J.); (A.L.-D.)
| | - Elisa Vilades
- Department of Ophthalmology, Miguel Servet University Hospital, 50009 Zaragoza, Spain; (E.V.); (M.I.F.); (A.P.)
- Miguel Servet Ophthalmology Innovation and Research Group (GIMSO), Aragon Institute for Health Research (IIS Aragon), Biotech Vision SLP (Spin-Off Company), University of Zaragoza, 50009 Zaragoza, Spain
| | - Maria I. Fuertes
- Department of Ophthalmology, Miguel Servet University Hospital, 50009 Zaragoza, Spain; (E.V.); (M.I.F.); (A.P.)
- Miguel Servet Ophthalmology Innovation and Research Group (GIMSO), Aragon Institute for Health Research (IIS Aragon), Biotech Vision SLP (Spin-Off Company), University of Zaragoza, 50009 Zaragoza, Spain
| | - Ana Pueyo
- Department of Ophthalmology, Miguel Servet University Hospital, 50009 Zaragoza, Spain; (E.V.); (M.I.F.); (A.P.)
- Miguel Servet Ophthalmology Innovation and Research Group (GIMSO), Aragon Institute for Health Research (IIS Aragon), Biotech Vision SLP (Spin-Off Company), University of Zaragoza, 50009 Zaragoza, Spain
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Corriveau-Lecavalier N, Botha H, Graff-Radford J, Switzer AR, Przybelski SA, Wiste HJ, Murray ME, Reichard RR, Dickson DW, Nguyen AT, Ramanan VK, McCarter SJ, Boeve BF, Machulda MM, Fields JA, Stricker NH, Nelson PT, Grothe MJ, Knopman DS, Lowe VJ, Petersen RC, Jack CR, Jones DT. A limbic-predominant amnestic neurodegenerative syndrome associated with TDP-43 pathology. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.11.19.23298314. [PMID: 38045300 PMCID: PMC10690340 DOI: 10.1101/2023.11.19.23298314] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/05/2023]
Abstract
Limbic-predominant age-related TDP-43 encephalopathy (LATE) is a neuropathologically-defined disease that affects 40% of persons in advanced age, but its associated neurological syndrome is not defined. LATE neuropathological changes (LATE-NC) are frequently comorbid with Alzheimer's disease neuropathologic changes (ADNC). When seen in isolation, LATE-NC have been associated with a predominantly amnestic profile and slow clinical progression. We propose a set of clinical criteria for a limbic-predominant amnestic neurodegenerative syndrome (LANS) that is highly associated with LATE-NC but also other pathologic entities. The LANS criteria incorporate core, standard and advanced features that are measurable in vivo, including older age at evaluation, mild clinical syndrome, disproportionate hippocampal atrophy, impaired semantic memory, limbic hypometabolism, absence of neocortical degenerative patterns and low likelihood of neocortical tau, with degrees of certainty (highest, high, moderate, low). We operationalized this set of criteria using clinical, imaging and biomarker data to validate its associations with clinical and pathologic outcomes. We screened autopsied patients from Mayo Clinic (n = 922) and ADNI (n = 93) cohorts and applied the LANS criteria to those with an antemortem predominant amnestic syndrome (Mayo, n = 165; ADNI, n = 53). ADNC, ADNC/LATE-NC and LATE-NC accounted for 35%, 37% and 4% of cases in the Mayo cohort, respectively, and 30%, 22%, and 9% of cases in the ADNI cohort, respectively. The LANS criteria effectively categorized these cases, with ADNC having the lowest LANS likelihoods, LATE-NC patients having the highest likelihoods, and ADNC/LATE-NC patients having intermediate likelihoods. A logistic regression model using the LANS features as predictors of LATE-NC achieved a balanced accuracy of 74.6% in the Mayo cohort, and out-of-sample predictions in the ADNI cohort achieved a balanced accuracy of 73.3%. Patients with high LANS likelihoods had a milder and slower clinical course and more severe temporo-limbic degeneration compared to those with low likelihoods. Stratifying ADNC/LATE-NC patients from the Mayo cohort according to their LANS likelihood revealed that those with higher likelihoods had more temporo-limbic degeneration and a slower rate of cognitive decline, and those with lower likelihoods had more lateral temporo-parietal degeneration and a faster rate of cognitive decline. The implementation of LANS criteria has implications to disambiguate the different driving etiologies of progressive amnestic presentations in older age and guide prognosis, treatment, and clinical trials. The development of in vivo biomarkers specific to TDP-43 pathology are needed to refine molecular associations between LANS and LATE-NC and precise antemortem diagnoses of LATE.
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Affiliation(s)
- Nick Corriveau-Lecavalier
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
| | - Hugo Botha
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | | | | | | | - Heather J. Wiste
- Department of Quantitative Health Sciences, Mayo Clinic Rochester, MN, USA
| | | | - R. Ross Reichard
- Department of Laboratory Medicine and Pathology, Mayo Clinic Rochester, MN, USA
| | | | - Aivi T. Nguyen
- Department of Laboratory Medicine and Pathology, Mayo Clinic Rochester, MN, USA
| | | | | | | | - Mary M. Machulda
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
| | - Julie A. Fields
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
| | - Nikki H. Stricker
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
| | - Peter T. Nelson
- Department of Pathology, University of Kentucky, Lexington, KY, USA
| | - Michel J. Grothe
- CIEN Foundation/Queen Sofia Foundation Alzheimer Center, Madrid, Spain
- Wallenberg Center for Molecular and Translational Medicine and Department of Psychiatry and Neurochemistry, University of Gothenburg, Gothenburg, Sweden
| | | | - Val J. Lowe
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | | | - Clifford R. Jack
- Department of Neuroscience, Mayo Clinic Jacksonville, FL, USA
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - David T. Jones
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
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Cai J, Hu W, Ma J, Si A, Chen S, Gong L, Zhang Y, Yan H, Chen F. Explainable Machine Learning with Pairwise Interactions for Predicting Conversion from Mild Cognitive Impairment to Alzheimer's Disease Utilizing Multi-Modalities Data. Brain Sci 2023; 13:1535. [PMID: 38002495 PMCID: PMC10670176 DOI: 10.3390/brainsci13111535] [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: 08/12/2023] [Revised: 10/04/2023] [Accepted: 10/29/2023] [Indexed: 11/26/2023] Open
Abstract
BACKGROUND Predicting cognition decline in patients with mild cognitive impairment (MCI) is crucial for identifying high-risk individuals and implementing effective management. To improve predicting MCI-to-AD conversion, it is necessary to consider various factors using explainable machine learning (XAI) models which provide interpretability while maintaining predictive accuracy. This study used the Explainable Boosting Machine (EBM) model with multimodal features to predict the conversion of MCI to AD during different follow-up periods while providing interpretability. METHODS This retrospective case-control study is conducted with data obtained from the ADNI database, with records of 1042 MCI patients from 2006 to 2022 included. The exposures included in this study were MRI biomarkers, cognitive scores, demographics, and clinical features. The main outcome was AD conversion from aMCI during follow-up. The EBM model was utilized to predict aMCI converting to AD based on three feature combinations, obtaining interpretability while ensuring accuracy. Meanwhile, the interaction effect was considered in the model. The three feature combinations were compared in different follow-up periods with accuracy, sensitivity, specificity, and AUC-ROC. The global and local explanations are displayed by importance ranking and feature interpretability plots. RESULTS The five-years prediction accuracy reached 85% (AUC = 0.92) using both cognitive scores and MRI markers. Apart from accuracies, we obtained features' importance in different follow-up periods. In early stage of AD, the MRI markers play a major role, while for middle-term, the cognitive scores are more important. Feature risk scoring plots demonstrated insightful nonlinear interactive associations between selected factors and outcome. In one-year prediction, lower right inferior temporal volume (<9000) is significantly associated with AD conversion. For two-year prediction, low left inferior temporal thickness (<2) is most critical. For three-year prediction, higher FAQ scores (>4) is the most important. During four-year prediction, APOE4 is the most critical. For five-year prediction, lower right entorhinal volume (<1000) is the most critical feature. CONCLUSIONS The established glass-box model EBMs with multimodal features demonstrated a superior ability with detailed interpretability in predicting AD conversion from MCI. Multi features with significant importance were identified. Further study may be of significance to determine whether the established prediction tool would improve clinical management for AD patients.
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Affiliation(s)
- Jiaxin Cai
- Department of Epidemiology and Biostatistics, School of Public Health, Xi’an Jiaotong University, Xi’an 710061, China; (J.C.); (W.H.); (A.S.); (S.C.); (L.G.)
| | - Weiwei Hu
- Department of Epidemiology and Biostatistics, School of Public Health, Xi’an Jiaotong University, Xi’an 710061, China; (J.C.); (W.H.); (A.S.); (S.C.); (L.G.)
| | - Jiaojiao Ma
- Department of Neurology, Xi’an Gaoxin Hospital, Xi’an 710077, China;
| | - Aima Si
- Department of Epidemiology and Biostatistics, School of Public Health, Xi’an Jiaotong University, Xi’an 710061, China; (J.C.); (W.H.); (A.S.); (S.C.); (L.G.)
| | - Shiyu Chen
- Department of Epidemiology and Biostatistics, School of Public Health, Xi’an Jiaotong University, Xi’an 710061, China; (J.C.); (W.H.); (A.S.); (S.C.); (L.G.)
| | - Lingmin Gong
- Department of Epidemiology and Biostatistics, School of Public Health, Xi’an Jiaotong University, Xi’an 710061, China; (J.C.); (W.H.); (A.S.); (S.C.); (L.G.)
| | - Yong Zhang
- Department of Surgical Oncology, First Affiliate Hospital of Xi’an Jiaotong University, Xi’an 710061, China;
| | - Hong Yan
- Department of Epidemiology and Biostatistics, School of Public Health, Xi’an Jiaotong University, Xi’an 710061, China; (J.C.); (W.H.); (A.S.); (S.C.); (L.G.)
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi’an Jiaotong University, Xi’an 710061, China
| | - Fangyao Chen
- Department of Epidemiology and Biostatistics, School of Public Health, Xi’an Jiaotong University, Xi’an 710061, China; (J.C.); (W.H.); (A.S.); (S.C.); (L.G.)
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi’an Jiaotong University, Xi’an 710061, China
- Department of Radiology, First Affiliate Hospital of Xi’an Jiaotong University, Xi’an 710061, China
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Christopher-Hayes NJ, Embury CM, Wiesman AI, May PE, Schantell M, Johnson CM, Wolfson SL, Murman DL, Wilson TW. Piecing it together: atrophy profiles of hippocampal subfields relate to cognitive impairment along the Alzheimer's disease spectrum. Front Aging Neurosci 2023; 15:1212197. [PMID: 38020776 PMCID: PMC10644116 DOI: 10.3389/fnagi.2023.1212197] [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: 04/25/2023] [Accepted: 10/13/2023] [Indexed: 12/01/2023] Open
Abstract
Introduction People with Alzheimer's disease (AD) experience more rapid declines in their ability to form hippocampal-dependent memories than cognitively normal healthy adults. Degeneration of the whole hippocampal formation has previously been found to covary with declines in learning and memory, but the associations between subfield-specific hippocampal neurodegeneration and cognitive impairments are not well characterized in AD. To improve prognostic procedures, it is critical to establish in which hippocampal subfields atrophy relates to domain-specific cognitive declines among people along the AD spectrum. In this study, we examine high-resolution structural magnetic resonance imaging (MRI) of the medial temporal lobe and extensive neuropsychological data from 29 amyloid-positive people on the AD spectrum and 17 demographically-matched amyloid-negative healthy controls. Methods Participants completed a battery of neuropsychological exams including select tests of immediate recollection, delayed recollection, and general cognitive status (i.e., performance on the Mini-Mental State Examination [MMSE] and Montreal Cognitive Assessment [MoCA]). Hippocampal subfield volumes (CA1, CA2, CA3, dentate gyrus, and subiculum) were measured using a dedicated MRI slab sequence targeting the medial temporal lobe and used to compute distance metrics to quantify AD spectrum-specific atrophic patterns and their impact on cognitive outcomes. Results Our results replicate prior studies showing that CA1, dentate gyrus, and subiculum hippocampal subfield volumes were significantly reduced in AD spectrum participants compared to amyloid-negative controls, whereas CA2 and CA3 did not exhibit such patterns of atrophy. Moreover, degeneration of the subiculum along the AD spectrum was linked to a significant decline in general cognitive status measured by the MMSE, while degeneration scores of the CA1 and dentate gyrus were more widely associated with declines on the MMSE and tests of learning and memory. Discussion These findings provide evidence that subfield-specific patterns of hippocampal degeneration, in combination with cognitive assessments, may constitute a sensitive prognostic approach and could be used to better track disease trajectories among individuals on the AD spectrum.
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Affiliation(s)
- Nicholas J. Christopher-Hayes
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE, United States
- Center for Mind and Brain, University of California, Davis, CA, United States
| | - Christine M. Embury
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE, United States
- Department of Psychology, University of Nebraska at Omaha, Omaha, NE, United States
| | - Alex I. Wiesman
- Montreal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Pamela E. May
- Department of Neurological Sciences, University of Nebraska Medical Center, Omaha, NE, United States
| | - Mikki Schantell
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE, United States
- College of Medicine, UNMC, Omaha, NE, United States
| | | | | | - Daniel L. Murman
- Department of Neurological Sciences, University of Nebraska Medical Center, Omaha, NE, United States
- Memory Disorders and Behavioral Neurology Program, UNMC, Omaha, NE, United States
| | - Tony W. Wilson
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE, United States
- College of Medicine, UNMC, Omaha, NE, United States
- Department of Pharmacology and Neuroscience, Creighton University, Omaha, NE, United States
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Subash P, Gray A, Boswell M, Cohen SL, Garner R, Salehi S, Fisher C, Hobel S, Ghosh S, Halchenko Y, Dichter B, Poldrack RA, Markiewicz C, Hermes D, Delorme A, Makeig S, Behan B, Sparks A, Arnott SR, Wang Z, Magnotti J, Beauchamp MS, Pouratian N, Toga AW, Duncan D. A comparison of neuroelectrophysiology databases. Sci Data 2023; 10:719. [PMID: 37857685 PMCID: PMC10587056 DOI: 10.1038/s41597-023-02614-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 10/02/2023] [Indexed: 10/21/2023] Open
Abstract
As data sharing has become more prevalent, three pillars - archives, standards, and analysis tools - have emerged as critical components in facilitating effective data sharing and collaboration. This paper compares four freely available intracranial neuroelectrophysiology data repositories: Data Archive for the BRAIN Initiative (DABI), Distributed Archives for Neurophysiology Data Integration (DANDI), OpenNeuro, and Brain-CODE. The aim of this review is to describe archives that provide researchers with tools to store, share, and reanalyze both human and non-human neurophysiology data based on criteria that are of interest to the neuroscientific community. The Brain Imaging Data Structure (BIDS) and Neurodata Without Borders (NWB) are utilized by these archives to make data more accessible to researchers by implementing a common standard. As the necessity for integrating large-scale analysis into data repository platforms continues to grow within the neuroscientific community, this article will highlight the various analytical and customizable tools developed within the chosen archives that may advance the field of neuroinformatics.
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Affiliation(s)
- Priyanka Subash
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, 2025 Zonal Avenue, Los Angeles, CA, 90033, USA
| | - Alex Gray
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, 2025 Zonal Avenue, Los Angeles, CA, 90033, USA
| | - Misque Boswell
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, 2025 Zonal Avenue, Los Angeles, CA, 90033, USA
| | - Samantha L Cohen
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, 2025 Zonal Avenue, Los Angeles, CA, 90033, USA
| | - Rachael Garner
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, 2025 Zonal Avenue, Los Angeles, CA, 90033, USA
| | - Sana Salehi
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, 2025 Zonal Avenue, Los Angeles, CA, 90033, USA
| | - Calvary Fisher
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, 2025 Zonal Avenue, Los Angeles, CA, 90033, USA
| | - Samuel Hobel
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, 2025 Zonal Avenue, Los Angeles, CA, 90033, USA
| | - Satrajit Ghosh
- McGovern Institute for Brain Research, MIT Brain and Cognitive Sciences, 77 Massachusetts Avenue, Cambridge, MA, 02139, USA
| | - Yaroslav Halchenko
- Department of Psychological & Brain Sciences, Center for Cognitive Neuroscience, Dartmouth Brain Imaging Center, Dartmouth College, 6207 Moore Hall, Hanover, NH, 03755, USA
| | | | - Russell A Poldrack
- Department of Psychology, Stanford University, 450 Jane Stanford Way, Stanford, CA, 94305, USA
| | - Chris Markiewicz
- Department of Psychology, Stanford University, 450 Jane Stanford Way, Stanford, CA, 94305, USA
| | - Dora Hermes
- Mayo Clinic, Department of Physiology & Biomedical Engineering, 200 1st Street SW, Rochester, MN, 55905, USA
| | - Arnaud Delorme
- Swartz Center of Computational Neuroscience, INC, University of California San Diego, La Jolla, CA, 92093, USA
| | - Scott Makeig
- Swartz Center of Computational Neuroscience, INC, University of California San Diego, La Jolla, CA, 92093, USA
| | - Brendan Behan
- Ontario Brain Institute, 1 Richmond Street West, Toronto, ON, M5H 3W4, Canada
| | | | | | - Zhengjia Wang
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, 3700 Hamilton Walk, Philadelphia, PA, 19104, USA
| | - John Magnotti
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, 3700 Hamilton Walk, Philadelphia, PA, 19104, USA
| | - Michael S Beauchamp
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, 3700 Hamilton Walk, Philadelphia, PA, 19104, USA
| | - Nader Pouratian
- Department of Neurological Surgery, University of Texas Southwestern Medical Center, 5303 Harry Hines Blvd, Dallas, TX, 75390, USA
| | - Arthur W Toga
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, 2025 Zonal Avenue, Los Angeles, CA, 90033, USA
| | - Dominique Duncan
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, 2025 Zonal Avenue, Los Angeles, CA, 90033, USA.
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Bhujbal SS, Kad MM, Patole VC. Recent diagnostic techniques for the detection of Alzheimer's disease: a short review. Ir J Med Sci 2023; 192:2417-2426. [PMID: 36525239 DOI: 10.1007/s11845-022-03244-y] [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: 07/28/2022] [Accepted: 11/25/2022] [Indexed: 12/23/2022]
Abstract
Alzheimer's disease (AD) is a neurological condition that affects millions of individuals around the world and for which there are few effective therapies. Dementia is characterized by the formation of senile plaques and neurofibrillary tangles, which is followed by neurotoxicity, which results in memory loss and mortality. Pathogenesis occurs several years before the onset of disease. As the disease-modifying drugs are most effective in the early stages of Alzheimer's disease, biomarkers for early detection of disease and their development are crucial. This review discusses the diagnostic utility, benefits, and limitations of traditional techniques such as neuroimaging, cognitive testing, positron emission tomography, and biomarkers, as well as the novel techniques such as artificial intelligence, machine learning, immunotherapy, and blood test approaches for early detection, understanding, and treatment of AD.
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Affiliation(s)
- Santosh S Bhujbal
- Dr. D. Y. Patil Institute of Pharmaceutical Sciences & Research, Pimpri, Pune, India.
| | - Minal M Kad
- Dr. D. Y. Patil Institute of Pharmaceutical Sciences & Research, Pimpri, Pune, India
| | - Vinita C Patole
- Dr. D. Y. Patil Institute of Pharmaceutical Sciences & Research, Pimpri, Pune, India
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Yi F, Zhang Y, Yuan J, Liu Z, Zhai F, Hao A, Wu F, Somekh J, Peleg M, Zhu YC, Huang Z. Identifying underlying patterns in Alzheimer's disease trajectory: a deep learning approach and Mendelian randomization analysis. EClinicalMedicine 2023; 64:102247. [PMID: 37811490 PMCID: PMC10556591 DOI: 10.1016/j.eclinm.2023.102247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 09/12/2023] [Accepted: 09/13/2023] [Indexed: 10/10/2023] Open
Abstract
Background Alzheimer's disease (AD) is a heterogeneously progressive neurodegeneration disorder with varied rates of deterioration, either between subjects or within different stages of a certain subject. Estimating the course of AD at early stages has treatment implications. We aimed to analyze disease progression to identify distinct patterns in AD trajectory. Methods We proposed a deep learning model to identify underlying patterns in the trajectory from cognitively normal (CN) to a state of mild cognitive impairment (MCI) to AD dementia, by jointly predicting time-to-conversion and clustering out distinct subgroups characterized by comprehensive features as well as varied progression rates. We designed and validated our model on the ADNI dataset (1370 participants). Prediction of time-to-conversion in AD trajectory was used to validate the expression of the identified patterns. Causality between patterns and time-to-conversion was further inferred using Mendelian randomization (MR) analysis. External validation was performed on the AIBL dataset (233 participants). Findings The proposed model clustered out patterns characterized by significantly different biomarkers and varied progression rates. The discovered patterns also showed a strong prediction ability, as indicated by hazard ratio (CN→MCI, HR = 3.51, p < 0.001; MCI→AD, HR = 8.11, p < 0.001), C-Index (CN→MCI, 0.618; MCI→AD, 0.718), and AUC (CN→MCI, 3 years 0.802, 5 years 0.876; MCI→AD, 3 years 0.914, 5 years 0.957). In the external validation cohort, our model demonstrated competitive performance on conversion time prediction (CN→MCI, C-Index = 0.693; MCI→AD, C-Index = 0.752). Moreover, suggestive associations between CN→MCI/MCI→AD patterns with four/three SNPs were mediated and MR analysis indicated a causal link between MCI→AD patterns and time-to-conversion in the first three years. Interpretation Our proposed model identifies biologically and clinically meaningful patterns from real-world data and provides promising performance on time-to-conversion prediction in AD trajectory, which could promote the understanding of disease progression, facilitate clinical trial design, and provide potential for decision-making. Funding The National Key Research and Development Program of China, the Key R&D Program of Zhejiang, and the National Nature Science Foundation of China.
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Affiliation(s)
- Fan Yi
- College of Computer Science and Technology, Zhejiang University, China
| | | | - Jing Yuan
- Department of Neurology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Ziyue Liu
- Department of Neurology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Feifei Zhai
- Department of Neurology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Ankai Hao
- College of Computer Science and Technology, Zhejiang University, China
| | - Fei Wu
- College of Computer Science and Technology, Zhejiang University, China
| | - Judith Somekh
- Department of Information Systems, University of Haifa, Haifa, Israel
| | - Mor Peleg
- Department of Information Systems, University of Haifa, Haifa, Israel
| | - Yi-Cheng Zhu
- Department of Neurology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Zhengxing Huang
- College of Computer Science and Technology, Zhejiang University, China
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Kim HB, Kim SH, Um YH, Wang SM, Kim REY, Choe YS, Lee J, Kim D, Lim HK, Lee CU, Kang DW. Modulation of associations between education years and cortical volume in Alzheimer's disease vulnerable brain regions by Aβ deposition and APOE ε4 carrier status in cognitively normal older adults. Front Aging Neurosci 2023; 15:1248531. [PMID: 37829142 PMCID: PMC10565031 DOI: 10.3389/fnagi.2023.1248531] [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: 06/28/2023] [Accepted: 09/05/2023] [Indexed: 10/14/2023] Open
Abstract
Background Education years, as a measure of cognitive reserve, have been shown to affect the progression of Alzheimer's disease (AD), both pathologically and clinically. However, inconsistent results have been reported regarding the association between years of education and intermediate structural changes in AD-vulnerable brain regions, particularly when AD risk factors were not considered during the preclinical phase. Objective This study aimed to examine how Aβ deposition and APOE ε4 carrier status moderate the relationship between years of education and cortical volume in AD-vulnerable regions among cognitively normal older adults. Methods A total of 121 participants underwent structural MRI, [18F] flutemetamol PET-CT imaging, and neuropsychological battery assessment. Multiple regression analysis was conducted to examine the interaction between years of education and the effects of potential modifiers on cortical volume. The associations between cortical volume and neuropsychological performance were further explored in subgroups categorized based on AD risk factors. Results The cortical volume of the left lateral occipital cortex and bilateral fusiform gyrus demonstrated a significant differential association with years of education, depending on the presence of Aβ deposition and APOE ε4 carrier status. Furthermore, a significant relationship between the cortical volume of the bilateral fusiform gyrus and AD-nonspecific cognitive function was predominantly observed in individuals without AD risk factors. Conclusion AD risk factors exerted varying influences on the association between years of education and cortical volume during the preclinical phase. Further investigations into the long-term implications of these findings would enhance our understanding of cognitive reserves in the preclinical stages of AD.
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Affiliation(s)
- Hak-Bin Kim
- Department of Psychiatry, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Sung-Hwan Kim
- Department of Psychiatry, Yeouido St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Yoo Hyun Um
- Department of Psychiatry, St. Vincent’s Hospital, College of Medicine, The Catholic University of Korea, Suwon, Republic of Korea
| | - Sheng-Min Wang
- Department of Psychiatry, Yeouido St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | | | - Yeong Sim Choe
- Research Institute, NEUROPHET Inc., Seoul, Republic of Korea
| | - Jiyeon Lee
- Research Institute, NEUROPHET Inc., Seoul, Republic of Korea
| | - Donghyeon Kim
- Research Institute, NEUROPHET Inc., Seoul, Republic of Korea
| | - Hyun Kook Lim
- Department of Psychiatry, Yeouido St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Research Institute, NEUROPHET Inc., Seoul, Republic of Korea
| | - Chang Uk Lee
- Department of Psychiatry, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Dong Woo Kang
- Department of Psychiatry, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
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48
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Fan X, Li Z, Li Z, Wang X, Liu R, Luo Z, Huang H. Automated Learning for Deformable Medical Image Registration by Jointly Optimizing Network Architectures and Objective Functions. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2023; 32:4880-4892. [PMID: 37624710 DOI: 10.1109/tip.2023.3307215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/27/2023]
Abstract
Deformable image registration plays a critical role in various tasks of medical image analysis. A successful registration algorithm, either derived from conventional energy optimization or deep networks, requires tremendous efforts from computer experts to well design registration energy or to carefully tune network architectures with respect to medical data available for a given registration task/scenario. This paper proposes an automated learning registration algorithm (AutoReg) that cooperatively optimizes both architectures and their corresponding training objectives, enabling non-computer experts to conveniently find off-the-shelf registration algorithms for various registration scenarios. Specifically, we establish a triple-level framework to embrace the searching for both network architectures and objectives with a cooperating optimization. Extensive experiments on multiple volumetric datasets and various registration scenarios demonstrate that AutoReg can automatically learn an optimal deep registration network for given volumes and achieve state-of-the-art performance. The automatically learned network also improves computational efficiency over the mainstream UNet architecture from 0.558 to 0.270 seconds for a volume pair on the same configuration.
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49
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Zhou Z, Tong B, Tarzanagh DA, Hou B, Saykin AJ, Long Q, Shen L. Multi-Group Tensor Canonical Correlation Analysis. ACM-BCB ... ... : THE ... ACM CONFERENCE ON BIOINFORMATICS, COMPUTATIONAL BIOLOGY AND BIOMEDICINE. ACM CONFERENCE ON BIOINFORMATICS, COMPUTATIONAL BIOLOGY AND BIOMEDICINE 2023; 2023:12. [PMID: 37876849 PMCID: PMC10593155 DOI: 10.1145/3584371.3612962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/26/2023]
Abstract
Tensor Canonical Correlation Analysis (TCCA) is a commonly employed statistical method utilized to examine linear associations between two sets of tensor datasets. However, the existing TCCA models fail to adequately address the heterogeneity present in real-world tensor data, such as brain imaging data collected from diverse groups characterized by factors like sex and race. Consequently, these models may yield biased outcomes. In order to surmount this constraint, we propose a novel approach called Multi-Group TCCA (MG-TCCA), which enables the joint analysis of multiple subgroups. By incorporating a dual sparsity structure and a block coordinate ascent algorithm, our MG-TCCA method effectively addresses heterogeneity and leverages information across different groups to identify consistent signals. This novel approach facilitates the quantification of shared and individual structures, reduces data dimensionality, and enables visual exploration. To empirically validate our approach, we conduct a study focused on investigating correlations between two brain positron emission tomography (PET) modalities (AV-45 and FDG) within an Alzheimer's disease (AD) cohort. Our results demonstrate that MG-TCCA surpasses traditional TCCA in identifying sex-specific cross-modality imaging correlations. This heightened performance of MG-TCCA provides valuable insights for the characterization of multimodal imaging biomarkers in AD.
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Affiliation(s)
| | - Boning Tong
- University of Pennsylvania, Philadelphia, USA
| | | | - Bojian Hou
- University of Pennsylvania, Philadelphia, USA
| | | | - Qi Long
- University of Pennsylvania, Philadelphia, USA
| | - Li Shen
- University of Pennsylvania, Philadelphia, USA
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50
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Liu Z, Lv Q, Yang Z, Li Y, Lee CH, Shen L. Recent progress in transformer-based medical image analysis. Comput Biol Med 2023; 164:107268. [PMID: 37494821 DOI: 10.1016/j.compbiomed.2023.107268] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 05/30/2023] [Accepted: 07/16/2023] [Indexed: 07/28/2023]
Abstract
The transformer is primarily used in the field of natural language processing. Recently, it has been adopted and shows promise in the computer vision (CV) field. Medical image analysis (MIA), as a critical branch of CV, also greatly benefits from this state-of-the-art technique. In this review, we first recap the core component of the transformer, the attention mechanism, and the detailed structures of the transformer. After that, we depict the recent progress of the transformer in the field of MIA. We organize the applications in a sequence of different tasks, including classification, segmentation, captioning, registration, detection, enhancement, localization, and synthesis. The mainstream classification and segmentation tasks are further divided into eleven medical image modalities. A large number of experiments studied in this review illustrate that the transformer-based method outperforms existing methods through comparisons with multiple evaluation metrics. Finally, we discuss the open challenges and future opportunities in this field. This task-modality review with the latest contents, detailed information, and comprehensive comparison may greatly benefit the broad MIA community.
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Affiliation(s)
- Zhaoshan Liu
- Department of Mechanical Engineering, National University of Singapore, 9 Engineering Drive 1, Singapore, 117575, Singapore.
| | - Qiujie Lv
- Department of Mechanical Engineering, National University of Singapore, 9 Engineering Drive 1, Singapore, 117575, Singapore; School of Intelligent Systems Engineering, Sun Yat-sen University, No. 66, Gongchang Road, Guangming District, 518107, China.
| | - Ziduo Yang
- Department of Mechanical Engineering, National University of Singapore, 9 Engineering Drive 1, Singapore, 117575, Singapore; School of Intelligent Systems Engineering, Sun Yat-sen University, No. 66, Gongchang Road, Guangming District, 518107, China.
| | - Yifan Li
- Department of Mechanical Engineering, National University of Singapore, 9 Engineering Drive 1, Singapore, 117575, Singapore.
| | - Chau Hung Lee
- Department of Radiology, Tan Tock Seng Hospital, 11 Jalan Tan Tock Seng, Singapore, 308433, Singapore.
| | - Lei Shen
- Department of Mechanical Engineering, National University of Singapore, 9 Engineering Drive 1, Singapore, 117575, Singapore.
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