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Nadkarni R, Han ZY, Anderson RJ, Allphin AJ, Clark DP, Badea A, Badea CT. High-resolution hybrid micro-CT imaging pipeline for mouse brain region segmentation and volumetric morphometry. PLoS One 2024; 19:e0303288. [PMID: 38781243 PMCID: PMC11115241 DOI: 10.1371/journal.pone.0303288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Accepted: 04/23/2024] [Indexed: 05/25/2024] Open
Abstract
BACKGROUND Brain region segmentation and morphometry in humanized apolipoprotein E (APOE) mouse models with a human NOS2 background (HN) contribute to Alzheimer's disease (AD) research by demonstrating how various risk factors affect the brain. Photon-counting detector (PCD) micro-CT provides faster scan times than MRI, with superior contrast and spatial resolution to energy-integrating detector (EID) micro-CT. This paper presents a pipeline for mouse brain imaging, segmentation, and morphometry from PCD micro-CT. METHODS We used brains of 26 mice from 3 genotypes (APOE22HN, APOE33HN, APOE44HN). The pipeline included PCD and EID micro-CT scanning, hybrid (PCD and EID) iterative reconstruction, and brain region segmentation using the Small Animal Multivariate Brain Analysis (SAMBA) tool. We applied SAMBA to transfer brain region labels from our new PCD CT atlas to individual PCD brains via diffeomorphic registration. Region-based and voxel-based analyses were used for comparisons by genotype and sex. RESULTS Together, PCD and EID scanning take ~5 hours to produce images with a voxel size of 22 μm, which is faster than MRI protocols for mouse brain morphometry with voxel size above 40 μm. Hybrid iterative reconstruction generates PCD images with minimal artifacts and higher spatial resolution and contrast than EID images. Our PCD atlas is qualitatively and quantitatively similar to the prior MRI atlas and successfully transfers labels to PCD brains in SAMBA. Male and female mice had significant volume differences in 26 regions, including parts of the entorhinal cortex and cingulate cortex. APOE22HN brains were larger than APOE44HN brains in clusters from the hippocampus, a region where atrophy is associated with AD. CONCLUSIONS This work establishes a pipeline for mouse brain analysis using PCD CT, from staining to imaging and labeling brain images. Our results validate the effectiveness of the approach, setting a foundation for research on AD mouse models while reducing scanning durations.
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Affiliation(s)
- Rohan Nadkarni
- Quantitative Imaging and Analysis Lab, Department of Radiology, Duke University Medical Center, Durham, NC, United States of America
| | - Zay Yar Han
- Quantitative Imaging and Analysis Lab, Department of Radiology, Duke University Medical Center, Durham, NC, United States of America
| | - Robert J. Anderson
- Quantitative Imaging and Analysis Lab, Department of Radiology, Duke University Medical Center, Durham, NC, United States of America
| | - Alex J. Allphin
- Quantitative Imaging and Analysis Lab, Department of Radiology, Duke University Medical Center, Durham, NC, United States of America
| | - Darin P. Clark
- Quantitative Imaging and Analysis Lab, Department of Radiology, Duke University Medical Center, Durham, NC, United States of America
| | - Alexandra Badea
- Quantitative Imaging and Analysis Lab, Department of Radiology, Duke University Medical Center, Durham, NC, United States of America
| | - Cristian T. Badea
- Quantitative Imaging and Analysis Lab, Department of Radiology, Duke University Medical Center, Durham, NC, United States of America
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2
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Stout JA, Mahzarnia A, Dai R, Anderson RJ, Cousins S, Zhuang J, Lad EM, Whitaker DB, Madden DJ, Potter GG, Whitson HE, Badea A. Accelerated Brain Atrophy, Microstructural Decline and Connectopathy in Age-Related Macular Degeneration. Biomedicines 2024; 12:147. [PMID: 38255252 PMCID: PMC10813528 DOI: 10.3390/biomedicines12010147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 12/15/2023] [Accepted: 01/05/2024] [Indexed: 01/24/2024] Open
Abstract
Age-related macular degeneration (AMD) has recently been linked to cognitive impairment. We hypothesized that AMD modifies the brain aging trajectory, and we conducted a longitudinal diffusion MRI study on 40 participants (20 with AMD and 20 controls) to reveal the location, extent, and dynamics of AMD-related brain changes. Voxel-based analyses at the first visit identified reduced volume in AMD participants in the cuneate gyrus, associated with vision, and the temporal and bilateral cingulate gyrus, linked to higher cognition and memory. The second visit occurred 2 years after the first and revealed that AMD participants had reduced cingulate and superior frontal gyrus volumes, as well as lower fractional anisotropy (FA) for the bilateral occipital lobe, including the visual and the superior frontal cortex. We detected faster rates of volume and FA reduction in AMD participants in the left temporal cortex. We identified inter-lingual and lingual-cerebellar connections as important differentiators in AMD participants. Bundle analyses revealed that the lingual gyrus had a lower streamline length in the AMD participants at the first visit, indicating a connection between retinal and brain health. FA differences in select inter-lingual and lingual cerebellar bundles at the second visit showed downstream effects of vision loss. Our analyses revealed widespread changes in AMD participants, beyond brain networks directly involved in vision processing.
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Affiliation(s)
- Jacques A. Stout
- Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC 27710, USA; (J.A.S.); (J.Z.); (D.J.M.)
| | - Ali Mahzarnia
- Radiology Department, Duke University Medical Center, Durham, NC 27710, USA; (A.M.); (R.D.); (R.J.A.)
| | - Rui Dai
- Radiology Department, Duke University Medical Center, Durham, NC 27710, USA; (A.M.); (R.D.); (R.J.A.)
| | - Robert J. Anderson
- Radiology Department, Duke University Medical Center, Durham, NC 27710, USA; (A.M.); (R.D.); (R.J.A.)
| | - Scott Cousins
- Ophthalmology Department, Duke University Medical Center, Durham, NC 27710, USA; (S.C.); (E.M.L.); (D.B.W.); (H.E.W.)
| | - Jie Zhuang
- Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC 27710, USA; (J.A.S.); (J.Z.); (D.J.M.)
| | - Eleonora M. Lad
- Ophthalmology Department, Duke University Medical Center, Durham, NC 27710, USA; (S.C.); (E.M.L.); (D.B.W.); (H.E.W.)
| | - Diane B. Whitaker
- Ophthalmology Department, Duke University Medical Center, Durham, NC 27710, USA; (S.C.); (E.M.L.); (D.B.W.); (H.E.W.)
| | - David J. Madden
- Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC 27710, USA; (J.A.S.); (J.Z.); (D.J.M.)
- Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC 27710, USA;
| | - Guy G. Potter
- Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC 27710, USA;
| | - Heather E. Whitson
- Ophthalmology Department, Duke University Medical Center, Durham, NC 27710, USA; (S.C.); (E.M.L.); (D.B.W.); (H.E.W.)
- Department of Medicine, Duke University Medical School, Durham, NC 27710, USA
- Department of Neurology, Duke University Medical Center, Durham, NC 27710, USA
| | - Alexandra Badea
- Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC 27710, USA; (J.A.S.); (J.Z.); (D.J.M.)
- Radiology Department, Duke University Medical Center, Durham, NC 27710, USA; (A.M.); (R.D.); (R.J.A.)
- Department of Neurology, Duke University Medical Center, Durham, NC 27710, USA
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3
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Moon HS, Mahzarnia A, Stout J, Anderson RJ, Strain M, Tremblay JT, Han ZY, Niculescu A, MacFarlane A, King J, Ashley-Koch A, Clark D, Lutz MW, Badea A. Multivariate investigation of aging in mouse models expressing the Alzheimer's protective APOE2 allele: integrating cognitive metrics, brain imaging, and blood transcriptomics. Brain Struct Funct 2024; 229:231-249. [PMID: 38091051 PMCID: PMC11082910 DOI: 10.1007/s00429-023-02731-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Accepted: 11/03/2023] [Indexed: 01/31/2024]
Abstract
APOE allelic variation is critical in brain aging and Alzheimer's disease (AD). The APOE2 allele associated with cognitive resilience and neuroprotection against AD remains understudied. We employed a multipronged approach to characterize the transition from middle to old age in mice with APOE2 allele, using behavioral assessments, image-derived morphometry and diffusion metrics, structural connectomics, and blood transcriptomics. We used sparse multiple canonical correlation analyses (SMCCA) for integrative modeling, and graph neural network predictions. Our results revealed brain sub-networks associated with biological traits, cognitive markers, and gene expression. The cingulate cortex emerged as a critical region, demonstrating age-associated atrophy and diffusion changes, with higher fractional anisotropy in males and middle-aged subjects. Somatosensory and olfactory regions were consistently highlighted, indicating age-related atrophy and sex differences. The hippocampus exhibited significant volumetric changes with age, with differences between males and females in CA3 and CA1 regions. SMCCA underscored changes in the cingulate cortex, somatosensory cortex, olfactory regions, and hippocampus in relation to cognition and blood-based gene expression. Our integrative modeling in aging APOE2 carriers revealed a central role for changes in gene pathways involved in localization and the negative regulation of cellular processes. Our results support an important role of the immune system and response to stress. This integrative approach offers novel insights into the complex interplay among brain connectivity, aging, and sex. Our study provides a foundation for understanding the impact of APOE2 allele on brain aging, the potential for detecting associated changes in blood markers, and revealing novel therapeutic intervention targets.
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Affiliation(s)
- Hae Sol Moon
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
- Quantitative Imaging and Analysis Laboratory, Department of Radiology, Duke University School of Medicine, Durham, NC, USA
| | - Ali Mahzarnia
- Quantitative Imaging and Analysis Laboratory, Department of Radiology, Duke University School of Medicine, Durham, NC, USA
| | - Jacques Stout
- Brain Imaging and Analysis Center, Duke University School of Medicine, Durham, NC, USA
| | - Robert J Anderson
- Quantitative Imaging and Analysis Laboratory, Department of Radiology, Duke University School of Medicine, Durham, NC, USA
| | - Madison Strain
- Duke Molecular Physiology Institute, Duke University School of Medicine, Durham, NC, USA
| | - Jessica T Tremblay
- Quantitative Imaging and Analysis Laboratory, Department of Radiology, Duke University School of Medicine, Durham, NC, USA
| | - Zay Yar Han
- Quantitative Imaging and Analysis Laboratory, Department of Radiology, Duke University School of Medicine, Durham, NC, USA
| | - Andrei Niculescu
- Quantitative Imaging and Analysis Laboratory, Department of Radiology, Duke University School of Medicine, Durham, NC, USA
| | - Anna MacFarlane
- Department of Neuroscience, Duke University, Durham, NC, USA
| | - Jasmine King
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Allison Ashley-Koch
- Duke Molecular Physiology Institute, Duke University School of Medicine, Durham, NC, USA
| | - Darin Clark
- Quantitative Imaging and Analysis Laboratory, Department of Radiology, Duke University School of Medicine, Durham, NC, USA
| | - Michael W Lutz
- Department of Neurology, Duke University School of Medicine, Durham, NC, USA
| | - Alexandra Badea
- Department of Biomedical Engineering, Duke University, Durham, NC, USA.
- Quantitative Imaging and Analysis Laboratory, Department of Radiology, Duke University School of Medicine, Durham, NC, USA.
- Brain Imaging and Analysis Center, Duke University School of Medicine, Durham, NC, USA.
- Department of Neurology, Duke University School of Medicine, Durham, NC, USA.
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4
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Moon HS, Mahzarnia A, Stout J, Anderson RJ, Badea CT, Badea A. Feature attention graph neural network for estimating brain age and identifying important neural connections in mouse models of genetic risk for Alzheimer's disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.13.571574. [PMID: 38168445 PMCID: PMC10760088 DOI: 10.1101/2023.12.13.571574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
Alzheimer's disease (AD) remains one of the most extensively researched neurodegenerative disorders due to its widespread prevalence and complex risk factors. Age is a crucial risk factor for AD, which can be estimated by the disparity between physiological age and estimated brain age. To model AD risk more effectively, integrating biological, genetic, and cognitive markers is essential. Here, we utilized mouse models expressing the major APOE human alleles and human nitric oxide synthase 2 to replicate genetic risk for AD and a humanized innate immune response. We estimated brain age employing a multivariate dataset that includes brain connectomes, APOE genotype, subject traits such as age and sex, and behavioral data. Our methodology used Feature Attention Graph Neural Networks (FAGNN) for integrating different data types. Behavioral data were processed with a 2D Convolutional Neural Network (CNN), subject traits with a 1D CNN, brain connectomes through a Graph Neural Network using quadrant attention module. The model yielded a mean absolute error for age prediction of 31.85 days, with a root mean squared error of 41.84 days, outperforming other, reduced models. In addition, FAGNN identified key brain connections involved in the aging process. The highest weights were assigned to the connections between cingulum and corpus callosum, striatum, hippocampus, thalamus, hypothalamus, cerebellum, and piriform cortex. Our study demonstrates the feasibility of predicting brain age in models of aging and genetic risk for AD. To verify the validity of our findings, we compared Fractional Anisotropy (FA) along the tracts of regions with the highest connectivity, the Return-to-Origin Probability (RTOP), Return-to-Plane Probability (RTPP), and Return-to-Axis Probability (RTAP), which showed significant differences between young, middle-aged, and old age groups. Younger mice exhibited higher FA, RTOP, RTAP, and RTPP compared to older groups in the selected connections, suggesting that degradation of white matter tracts plays a critical role in aging and for FAGNN's selections. Our analysis suggests a potential neuroprotective role of APOE2, relative to APOE3 and APOE4, where APOE2 appears to mitigate age-related changes. Our findings highlighted a complex interplay of genetics and brain aging in the context of AD risk modeling.
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Affiliation(s)
- Hae Sol Moon
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
- Quantitative Imaging and Analysis Laboratory, Department of Radiology, Duke University School of Medicine, Durham, NC, USA
| | - Ali Mahzarnia
- Quantitative Imaging and Analysis Laboratory, Department of Radiology, Duke University School of Medicine, Durham, NC, USA
| | - Jacques Stout
- Quantitative Imaging and Analysis Laboratory, Department of Radiology, Duke University School of Medicine, Durham, NC, USA
| | - Robert J Anderson
- Quantitative Imaging and Analysis Laboratory, Department of Radiology, Duke University School of Medicine, Durham, NC, USA
| | - Cristian T. Badea
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
- Quantitative Imaging and Analysis Laboratory, Department of Radiology, Duke University School of Medicine, Durham, NC, USA
| | - Alexandra Badea
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
- Quantitative Imaging and Analysis Laboratory, Department of Radiology, Duke University School of Medicine, Durham, NC, USA
- Brain Imaging and Analysis Center, Duke University School of Medicine, Durham, NC, USA
- Department of Neurology, Duke University School of Medicine, Durham, NC, USA
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5
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Stapleton MC, Koch SP, Cortes DRE, Wyman S, Schwab KE, Mueller S, McKennan CG, Boehm-Sturm P, Wu YL. Apolipoprotein-E deficiency leads to brain network alteration characterized by diffusion MRI and graph theory. Front Neurosci 2023; 17:1183312. [PMID: 38075287 PMCID: PMC10702609 DOI: 10.3389/fnins.2023.1183312] [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: 03/09/2023] [Accepted: 09/18/2023] [Indexed: 02/12/2024] Open
Abstract
Late-onset Alzheimer's disease (LOAD) is a major health concern for senior citizens, characterized by memory loss, confusion, and impaired cognitive abilities. Apolipoprotein-E (ApoE) is a well-known risk factor for LOAD, though exactly how ApoE affects LOAD risks is unknown. We hypothesize that ApoE attenuation of LOAD resiliency or vulnerability has a neurodevelopmental origin via changing brain network architecture. We investigated the brain network structure in adult ApoE knock out (ApoE KO) and wild-type (WT) mice with diffusion tensor imaging (DTI) followed by graph theory to delineate brain network topology. Left and right hemisphere connectivity revealed significant differences in number of connections between the hippocampus, amygdala, caudate putamen and other brain regions. Network topology based on the graph theory of ApoE KO demonstrated decreased functional integration, network efficiency, and network segregation between the hippocampus and amygdala and the rest of the brain, compared to those in WT counterparts. Our data show that brain network developed differently in ApoE KO and WT mice at 5 months of age, especially in the network reflected in the hippocampus, amygdala, and caudate putamen. This indicates that ApoE is involved in brain network development which might modulate LOAD risks via changing brain network structures.
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Affiliation(s)
- Margaret Caroline Stapleton
- Department of Developmental Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States
- Rangos Research Center Animal Imaging Core, Children’s Hospital of Pittsburgh of UPMC, Pittsburgh, PA, United States
| | - Stefan Paul Koch
- Charité 3R | Replace, Reduce, Refine, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Department of Experimental Neurology and Center for Stroke Research Berlin, Charité-Universitätsmedizin Berlin, Berlin, Germany
- NeuroCure Cluster of Excellence and Charité Core Facility 7T Experimental MRIs, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Devin Raine Everaldo Cortes
- Department of Developmental Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States
- Rangos Research Center Animal Imaging Core, Children’s Hospital of Pittsburgh of UPMC, Pittsburgh, PA, United States
- Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, United States
| | - Samuel Wyman
- Department of Developmental Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States
- Rangos Research Center Animal Imaging Core, Children’s Hospital of Pittsburgh of UPMC, Pittsburgh, PA, United States
| | - Kristina E. Schwab
- Department of Developmental Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States
- Rangos Research Center Animal Imaging Core, Children’s Hospital of Pittsburgh of UPMC, Pittsburgh, PA, United States
| | - Susanne Mueller
- Charité 3R | Replace, Reduce, Refine, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Department of Experimental Neurology and Center for Stroke Research Berlin, Charité-Universitätsmedizin Berlin, Berlin, Germany
- NeuroCure Cluster of Excellence and Charité Core Facility 7T Experimental MRIs, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | | | - Philipp Boehm-Sturm
- Charité 3R | Replace, Reduce, Refine, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Department of Experimental Neurology and Center for Stroke Research Berlin, Charité-Universitätsmedizin Berlin, Berlin, Germany
- NeuroCure Cluster of Excellence and Charité Core Facility 7T Experimental MRIs, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Yijen Lin Wu
- Department of Developmental Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States
- Rangos Research Center Animal Imaging Core, Children’s Hospital of Pittsburgh of UPMC, Pittsburgh, PA, United States
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6
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Sharma H, Chang KA, Hulme J, An SSA. Mammalian Models in Alzheimer's Research: An Update. Cells 2023; 12:2459. [PMID: 37887303 PMCID: PMC10605533 DOI: 10.3390/cells12202459] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 10/09/2023] [Accepted: 10/10/2023] [Indexed: 10/28/2023] Open
Abstract
A form of dementia distinct from healthy cognitive aging, Alzheimer's disease (AD) is a complex multi-stage disease that currently afflicts over 50 million people worldwide. Unfortunately, previous therapeutic strategies developed from murine models emulating different aspects of AD pathogenesis were limited. Consequently, researchers are now developing models that express several aspects of pathogenesis that better reflect the clinical situation in humans. As such, this review seeks to provide insight regarding current applications of mammalian models in AD research by addressing recent developments and characterizations of prominent transgenic models and their contributions to pathogenesis as well as discuss the advantages, limitations, and application of emerging models that better capture genetic heterogeneity and mixed pathologies observed in the clinical situation.
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Affiliation(s)
- Himadri Sharma
- Department of Bionano Technology, Gachon Bionano Research Institute, Gachon University, 1342 Seongnam-daero, Sujeong-gu, Seongnam-si 461-701, Gyeonggi-do, Republic of Korea
| | - Keun-A Chang
- Neuroscience Research Institute, Gachon University, Incheon 21565, Republic of Korea
| | - John Hulme
- Department of Bionano Technology, Gachon Bionano Research Institute, Gachon University, 1342 Seongnam-daero, Sujeong-gu, Seongnam-si 461-701, Gyeonggi-do, Republic of Korea
| | - Seong Soo A. An
- Department of Bionano Technology, Gachon Bionano Research Institute, Gachon University, 1342 Seongnam-daero, Sujeong-gu, Seongnam-si 461-701, Gyeonggi-do, Republic of Korea
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7
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Guo W, Mao X, Han D, Wang H, Zhang W, Zhang G, Zhang N, Nie B, Li H, Song Y, Wu Y, Chang L. Sleep deprivation aggravated amyloid β oligomers-induced damage to the cerebellum of rats: Evidence from magnetic resonance imaging. AGING BRAIN 2023; 4:100091. [PMID: 37600754 PMCID: PMC10432242 DOI: 10.1016/j.nbas.2023.100091] [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: 07/01/2023] [Revised: 07/29/2023] [Accepted: 08/01/2023] [Indexed: 08/22/2023] Open
Abstract
For quite a long time, researches on Alzheimer's disease (AD) primarily focused on the cortex and hippocampus, while the cerebellum has been ignored because of its abnormalities considered to appear in the late stage of AD. In recent years, increasing evidence suggest that the cerebellar pathological changes possibly occur in the preclinical phase of AD, which is also associated with sleep disorder. Sleep disturbance is a high risk factor of AD. However, the changes and roles of cerebellum has rarely been reported under conditions of AD accompanied with sleep disorders. In this study, using an amyloid-β oligomers (AβO)-induced rat model of AD subjected to sleep deprivation, combining with a 7.0 T animals structural magnetic resonance imaging (MRI), we assessed structural changes of cerebellum in MRI. Our results showed that sleep deprivation combined with AβO led to an increased FA value in the anterior lobe of cerebellum, decreased ADC value in the cerebellar lobes and cerebellar nuclei, and increased cerebellum volume. Besides that, sleep deprivation exacerbated the damage of AβO to the cerebellar structural network. This study demonstrated that sleep deprivation could aggravate the damage to cerebellum induced by AβO. The present findings provide supporting evidence for the involvement of cerebellum in the early pathology of AD and sleep loss. Our data would contribute to advancing the understanding of the mysterious role of cerebellum in AD and sleep disorders, as well as would be helpful for developing non-invasive MRI biomarkers for screening early AD patients with self-reported sleep disturbances.
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Affiliation(s)
- Wensheng Guo
- Department of Anatomy, School of Basic Medical Sciences, Beijing Key Laboratory of Neural Regeneration and Repair, Beijing Institute of Brain Disorders, Capital Medical University, Beijing, China
| | - Xin Mao
- Department of Anatomy, School of Basic Medical Sciences, Beijing Key Laboratory of Neural Regeneration and Repair, Beijing Institute of Brain Disorders, Capital Medical University, Beijing, China
| | - Ding Han
- Department of Anatomy, School of Basic Medical Sciences, Beijing Key Laboratory of Neural Regeneration and Repair, Beijing Institute of Brain Disorders, Capital Medical University, Beijing, China
| | - Hongqi Wang
- Department of Anatomy, School of Basic Medical Sciences, Beijing Key Laboratory of Neural Regeneration and Repair, Beijing Institute of Brain Disorders, Capital Medical University, Beijing, China
| | - Wanning Zhang
- Department of Anatomy, School of Basic Medical Sciences, Beijing Key Laboratory of Neural Regeneration and Repair, Beijing Institute of Brain Disorders, Capital Medical University, Beijing, China
| | - Guitao Zhang
- Department of Anatomy, School of Basic Medical Sciences, Beijing Key Laboratory of Neural Regeneration and Repair, Beijing Institute of Brain Disorders, Capital Medical University, Beijing, China
| | - Ning Zhang
- Department of Neuropsychiatry and Behavioral Neurology and Clinical Psychology, China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Binbin Nie
- Beijing Engineering Research Center of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, China
| | - Hui Li
- Department of Anatomy, School of Basic Medical Sciences, Beijing Key Laboratory of Neural Regeneration and Repair, Beijing Institute of Brain Disorders, Capital Medical University, Beijing, China
| | - Yizhi Song
- Department of Anatomy, School of Basic Medical Sciences, Beijing Key Laboratory of Neural Regeneration and Repair, Beijing Institute of Brain Disorders, Capital Medical University, Beijing, China
| | - Yan Wu
- Department of Anatomy, School of Basic Medical Sciences, Beijing Key Laboratory of Neural Regeneration and Repair, Beijing Institute of Brain Disorders, Capital Medical University, Beijing, China
| | - Lirong Chang
- Department of Anatomy, School of Basic Medical Sciences, Beijing Key Laboratory of Neural Regeneration and Repair, Beijing Institute of Brain Disorders, Capital Medical University, Beijing, China
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8
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Whitson HE, Colton C, El Khoury J, Gate D, Goate A, Heneka MT, Kaddurah-Daouk R, Klein RS, Shinohara ML, Sisodia S, Spudich SS, Stevens B, Tanzi R, Ting JP, Garden G. Infection and inflammation: New perspectives on Alzheimer's disease. Brain Behav Immun Health 2022; 22:100462. [PMID: 36118272 PMCID: PMC9475126 DOI: 10.1016/j.bbih.2022.100462] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Accepted: 04/10/2022] [Indexed: 11/24/2022] Open
Abstract
Neuroinflammation has been recognized as a component of Alzheimer's Disease (AD) pathology since the original descriptions by Alois Alzheimer and a role for infections in AD pathogenesis has long been hypothesized. More recently, this hypothesis has gained strength as human genetics and experimental data suggest key roles for inflammatory cells in AD pathogenesis. To review this topic, Duke/University of North Carolina (Duke/UNC) Alzheimer's Disease Research Center hosted a virtual symposium: "Infection and Inflammation: New Perspectives on Alzheimer's Disease (AD)." Participants considered current evidence for and against the hypothesis that AD could be caused or exacerbated by infection or commensal microbes. Discussion focused on connecting microglial transcriptional states to functional states, mouse models that better mimic human immunity, the potential involvement of inflammasome signaling, metabolic alterations, self-reactive T cells, gut microbes and fungal infections, and lessons learned from Covid-19 patients with neurologic symptoms. The content presented in the symposium, and major topics raised in discussions are reviewed in this summary of the proceedings.
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Affiliation(s)
- Heather E. Whitson
- Duke Center for the Study of Aging and Human Development, Duke University School of Medicine, Busse Bldg Rm 3502, Durham, NC, 27710, USA
- Durham VA Medical Center, Geriatric Research Education and Clinical Center, 508 Fulton Street, Durham, NC, 27705, USA
| | - Carol Colton
- Department of Neurology, Duke University School of Medicine, 3116 N Duke St, Durham, NM, 27704, USA
| | - Joseph El Khoury
- Center for Immunology & Inflammatory Diseases, Division of Infectious Diseases, Massachusetts General Hospital, 55 Fruit St, Boston, MA, 02114, USA
| | - David Gate
- The Ken & Ruth Davee Dept of Neurology, Northwestern University Feinberg School of Medicine, 303 E Chicago Ave, Ward 12-140, Chicago, IL 60611, USA
| | - Alison Goate
- Dept of Genetics and Genomic Sciences, Icahn School of Medicine at Mt. Sinai, One Gustave L. Levy Place, Box 1498, New York, NY, 10029-6574, USA
| | - Michael T. Heneka
- Dept of Neurodegenerative Disease and Geriatric Psychiatry/Neurology, University of Bonn Medical Center, Sigmund-Freud Str. 25, 53127, Bonn, Germany
| | - Rima Kaddurah-Daouk
- Dept of Psychiatry and Behavioral Sciences, Dept of Medicine, Duke Institute of Brain Sciences, Duke University School of Medicine, DUMC Box 3903, Blue Zone, South, Durham, NC, 27710, USA
| | - Robyn S. Klein
- Center for Neuroimmunology & Neuroinfectious Diseases, Depts of Medicine, Pathology & Immunology, and Neuroscience, Washington University School of Medicine, 660 S Euclid Ave, Box 8015, St. Louis, MO, 63110, USA
| | - Mari L. Shinohara
- Dept of Immunology, Duke University School of Medicine, 207 Research Dr, Box 3010, Durham, NC, 27710, USA
| | - Sangram Sisodia
- Dept of Neurobiology, University of Chicago, Abbott Memorial Hall, 947 East 58th St, MC 0928, Chicago, IL, 60637, USA
| | - Serena S. Spudich
- Dept of Neurology, Yale School of Medicine, PO Box 208018, New Haven, CT, 06520, USA
| | - Beth Stevens
- F.M. Kirby Neurobiology Center, Children's Hospital Boston, 300 Longwood Ave, Center for Life Sciences 12th Floor, Boston, MA, 02115, USA
| | - Rudolph Tanzi
- McCance Center for Brain Health, Massachusetts General Hospital, 114 16th St, Charlestown, MA, 02129, USA
| | - Jenny P. Ting
- Depts of Genetics, Microbiology and Immunology, Lineberger Comprehensive Cancer Center, Center for Translational Immunology, UNC School of Medicine, 125 Mason Farm Road, 6th Floor Marsico Hall, Chapel Hill, NC, 27599-7290, USA
| | - Gwenn Garden
- Dept of Neurology, UNC School of Medicine, Physicians Office Building, 170 Manning Drive, Campus Box 7025, Chapel Hill, NC, 27599-7025, USA
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9
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Badea A, Li D, Niculescu AR, Anderson RJ, Stout JA, Williams CL, Colton CA, Maeda N, Dunson DB. Absolute Winding Number Differentiates Mouse Spatial Navigation Strategies With Genetic Risk for Alzheimer's Disease. Front Neurosci 2022; 16:848654. [PMID: 35784847 PMCID: PMC9247395 DOI: 10.3389/fnins.2022.848654] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Accepted: 04/29/2022] [Indexed: 11/13/2022] Open
Abstract
Spatial navigation and orientation are emerging as promising markers for altered cognition in prodromal Alzheimer's disease, and even in cognitively normal individuals at risk for Alzheimer's disease. The different APOE gene alleles confer various degrees of risk. The APOE2 allele is considered protective, APOE3 is seen as control, while APOE4 carriage is the major known genetic risk for Alzheimer's disease. We have used mouse models carrying the three humanized APOE alleles and tested them in a spatial memory task in the Morris water maze. We introduce a new metric, the absolute winding number, to characterize the spatial search strategy, through the shape of the swim path. We show that this metric is robust to noise, and works for small group samples. Moreover, the absolute winding number better differentiated APOE3 carriers, through their straighter swim paths relative to both APOE2 and APOE4 genotypes. Finally, this novel metric supported increased vulnerability in APOE4 females. We hypothesized differences in spatial memory and navigation strategies are linked to differences in brain networks, and showed that different genotypes have different reliance on the hippocampal and caudate putamen circuits, pointing to a role for white matter connections. Moreover, differences were most pronounced in females. This departure from a hippocampal centric to a brain network approach may open avenues for identifying regions linked to increased risk for Alzheimer's disease, before overt disease manifestation. Further exploration of novel biomarkers based on spatial navigation strategies may enlarge the windows of opportunity for interventions. The proposed framework will be significant in dissecting vulnerable circuits associated with cognitive changes in prodromal Alzheimer's disease.
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Affiliation(s)
- Alexandra Badea
- Department of Radiology, Duke University, Durham, NC, United States
- Department of Neurology, Duke University, Durham, NC, United States
- Brain Imaging and Analysis Center, Duke University, Durham, NC, United States
- Biomedical Engineering, Duke University, Durham, NC, United States
| | - Didong Li
- Department of Computer Science, Princeton University, Princeton, NJ, United States
- Department of Biostatistics, University of California, Los Angeles, Los Angeles, CA, United States
| | | | | | - Jacques A. Stout
- Brain Imaging and Analysis Center, Duke University, Durham, NC, United States
| | - Christina L. Williams
- Department of Psychology and Neuroscience, Duke University, Durham, NC, United States
| | - Carol A. Colton
- Department of Neurology, Duke University, Durham, NC, United States
| | - Nobuyo Maeda
- Department of Pathology and Laboratory Medicine, The University of North Carolina, Chapel Hill, Chapel Hill, NC, United States
| | - David B. Dunson
- Department of Statistical Science, Duke University, Durham, NC, United States
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10
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Hall GR, Boehm-Sturm P, Dirnagl U, Finke C, Foddis M, Harms C, Koch SP, Kuchling J, Madan CR, Mueller S, Sassi C, Sotiropoulos SN, Trueman RC, Wallis MD, Yildirim F, Farr TD. Long-Term Connectome Analysis Reveals Reshaping of Visual, Spatial Networks in a Model With Vascular Dementia Features. Stroke 2022; 53:1735-1745. [PMID: 35105183 PMCID: PMC9022688 DOI: 10.1161/strokeaha.121.036997] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2022]
Abstract
Connectome analysis of neuroimaging data is a rapidly expanding field that offers the potential to diagnose, characterize, and predict neurological disease. Animal models provide insight into biological mechanisms that underpin disease, but connectivity approaches are currently lagging in the rodent.
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Affiliation(s)
- Gerard R Hall
- School of Life Sciences, University of Nottingham, United Kingdom (G.R.H., R.C.T., M.D.W., T.D.F.)
| | - Philipp Boehm-Sturm
- Department of Experimental Neurology, Center for Stroke Research Berlin, Charité-Universitätsmedizin Berlin (P.B.-S., U.D., M.F., C.H., S.P.K., S.M., C.S., T.D.F.).,corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Germany. NeuroCure Cluster of Excellence and Charité Core Facility 7T Experimental MRIs, Charité-Universitätsmedizin Berlin, Germany. (P.B.-S., U.D., M.F., C.H., S.P.K., S.M., C.S., F.Y., T.D.F.)
| | - Ulrich Dirnagl
- Department of Experimental Neurology, Center for Stroke Research Berlin, Charité-Universitätsmedizin Berlin (P.B.-S., U.D., M.F., C.H., S.P.K., S.M., C.S., T.D.F.).,corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Germany. NeuroCure Cluster of Excellence and Charité Core Facility 7T Experimental MRIs, Charité-Universitätsmedizin Berlin, Germany. (P.B.-S., U.D., M.F., C.H., S.P.K., S.M., C.S., F.Y., T.D.F.).,German Center for Neurodegenerative Diseases, Berlin Site, Germany (U.D.)
| | - Carsten Finke
- Department of Neurology, Charité-Universitätsmedizin Berlin, Germany. (C.F., J.K.).,Berlin School of Mind and Brain, Humboldt Universität zu Berlin, Germany (C.F.)
| | - Marco Foddis
- Department of Experimental Neurology, Center for Stroke Research Berlin, Charité-Universitätsmedizin Berlin (P.B.-S., U.D., M.F., C.H., S.P.K., S.M., C.S., T.D.F.).,corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Germany. NeuroCure Cluster of Excellence and Charité Core Facility 7T Experimental MRIs, Charité-Universitätsmedizin Berlin, Germany. (P.B.-S., U.D., M.F., C.H., S.P.K., S.M., C.S., F.Y., T.D.F.)
| | - Christoph Harms
- Department of Experimental Neurology, Center for Stroke Research Berlin, Charité-Universitätsmedizin Berlin (P.B.-S., U.D., M.F., C.H., S.P.K., S.M., C.S., T.D.F.).,corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Germany. NeuroCure Cluster of Excellence and Charité Core Facility 7T Experimental MRIs, Charité-Universitätsmedizin Berlin, Germany. (P.B.-S., U.D., M.F., C.H., S.P.K., S.M., C.S., F.Y., T.D.F.)
| | - Stefan Paul Koch
- Department of Experimental Neurology, Center for Stroke Research Berlin, Charité-Universitätsmedizin Berlin (P.B.-S., U.D., M.F., C.H., S.P.K., S.M., C.S., T.D.F.).,corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Germany. NeuroCure Cluster of Excellence and Charité Core Facility 7T Experimental MRIs, Charité-Universitätsmedizin Berlin, Germany. (P.B.-S., U.D., M.F., C.H., S.P.K., S.M., C.S., F.Y., T.D.F.)
| | - Joseph Kuchling
- Experimental and Clinical Research Center, Max Delbrueck Center for Molecular Medicine and Charité-Universitätsmedizin Berlin (J.K.).,Department of Neurology, Charité-Universitätsmedizin Berlin, Germany. (C.F., J.K.)
| | | | - Susanne Mueller
- Department of Experimental Neurology, Center for Stroke Research Berlin, Charité-Universitätsmedizin Berlin (P.B.-S., U.D., M.F., C.H., S.P.K., S.M., C.S., T.D.F.).,corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Germany. NeuroCure Cluster of Excellence and Charité Core Facility 7T Experimental MRIs, Charité-Universitätsmedizin Berlin, Germany. (P.B.-S., U.D., M.F., C.H., S.P.K., S.M., C.S., F.Y., T.D.F.)
| | - Celeste Sassi
- Department of Experimental Neurology, Center for Stroke Research Berlin, Charité-Universitätsmedizin Berlin (P.B.-S., U.D., M.F., C.H., S.P.K., S.M., C.S., T.D.F.).,corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Germany. NeuroCure Cluster of Excellence and Charité Core Facility 7T Experimental MRIs, Charité-Universitätsmedizin Berlin, Germany. (P.B.-S., U.D., M.F., C.H., S.P.K., S.M., C.S., F.Y., T.D.F.)
| | - Stamatios N Sotiropoulos
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, United Kingdom (S.N.S.).,Centre for Functional MRI of the Brain, University of Oxford, United Kingdom (S.N.S.)
| | - Rebecca C Trueman
- School of Life Sciences, University of Nottingham, United Kingdom (G.R.H., R.C.T., M.D.W., T.D.F.)
| | - Marcus D Wallis
- School of Life Sciences, University of Nottingham, United Kingdom (G.R.H., R.C.T., M.D.W., T.D.F.)
| | - Ferah Yildirim
- corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Germany. NeuroCure Cluster of Excellence and Charité Core Facility 7T Experimental MRIs, Charité-Universitätsmedizin Berlin, Germany. (P.B.-S., U.D., M.F., C.H., S.P.K., S.M., C.S., F.Y., T.D.F.).,NeuroCure Cluster of Excellence and Department of Psychiatry and Psychotherapy, Charité-Universitätsmedizin Berlin, Germany. (F.Y.)
| | - Tracy D Farr
- School of Life Sciences, University of Nottingham, United Kingdom (G.R.H., R.C.T., M.D.W., T.D.F.).,Department of Experimental Neurology, Center for Stroke Research Berlin, Charité-Universitätsmedizin Berlin (P.B.-S., U.D., M.F., C.H., S.P.K., S.M., C.S., T.D.F.).,corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Germany. NeuroCure Cluster of Excellence and Charité Core Facility 7T Experimental MRIs, Charité-Universitätsmedizin Berlin, Germany. (P.B.-S., U.D., M.F., C.H., S.P.K., S.M., C.S., F.Y., T.D.F.)
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11
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Xiao J, Hornburg KJ, Cofer G, Cook JJ, Pratson F, Qi Y, Johnson GA. A time-course study of actively stained mouse brains: Diffusion tensor imaging parameters and connectomic stability over 1 year. NMR IN BIOMEDICINE 2022; 35:e4611. [PMID: 34558744 PMCID: PMC10461792 DOI: 10.1002/nbm.4611] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 07/21/2021] [Accepted: 08/10/2021] [Indexed: 06/13/2023]
Abstract
While the application of diffusion tensor imaging (DTI), tractography, and connectomics to fixed tissue is a common practice today, there have been limited studies examining the effects of fixation on brain microstructure over extended periods. This mouse model time-course study reports the changes of regional brain volumes and diffusion scalar parameters, such as fractional anisotropy, across 12 representative brain regions as measures of brain structural stability. The scalar DTI parameters and regional volumes were highly variable over the first 2 weeks after fixation. The same parameters were consistent over a 2-8-week window after fixation, which means confounds from tissue stability over that scanning window were minimal. Quantitative connectomes were analyzed over the same time with extension out to 1 year. While there was some change in the scalar metrics at 1 year after fixation, these changes were sufficiently small, particularly in white matter, to support reproducible connectomes over a period ranging from 2-weeks to 1-year post-fixation. These findings delineate a scanning period, during which brain volumes, diffusion scalar metrics, and connectomes are remarkably consistent.
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Affiliation(s)
- Jaclyn Xiao
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, USA
| | - Kathryn J. Hornburg
- Duke Center for In Vivo Microscopy, Department of Radiology, Duke University Medical Center, Durham, North Carolina, USA
| | - Gary Cofer
- Duke Center for In Vivo Microscopy, Department of Radiology, Duke University Medical Center, Durham, North Carolina, USA
| | - James J. Cook
- Duke Center for In Vivo Microscopy, Department of Radiology, Duke University Medical Center, Durham, North Carolina, USA
| | - Forrest Pratson
- Duke Center for In Vivo Microscopy, Department of Radiology, Duke University Medical Center, Durham, North Carolina, USA
| | - Yi Qi
- Duke Center for In Vivo Microscopy, Department of Radiology, Duke University Medical Center, Durham, North Carolina, USA
| | - G. Allan Johnson
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, USA
- Duke Center for In Vivo Microscopy, Department of Radiology, Duke University Medical Center, Durham, North Carolina, USA
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12
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Turner DA. Contrasting Metabolic Insufficiency in Aging and Dementia. Aging Dis 2021; 12:1081-1096. [PMID: 34221551 PMCID: PMC8219502 DOI: 10.14336/ad.2021.0104] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2020] [Accepted: 01/04/2021] [Indexed: 12/14/2022] Open
Abstract
Metabolic insufficiency and neuronal dysfunction occur in normal aging but is exaggerated in dementia and Alzheimer's disease (AD). Metabolic insufficiency includes factors important for both substrate supply and utilization in the brain. Metabolic insufficiency occurs through a number of serial mechanisms, particularly changes in cerebrovascular supply through blood vessel abnormalities (ie, small and large vessel vasculopathy, stroke), alterations in neurovascular coupling providing dynamic blood flow supply in relation to neuronal demand, abnormalities in blood brain barrier including decreased glucose and amino acid transport, altered glymphatic flow in terms of substrate supply across the extracellular space to cells and drainage into CSF of metabolites, impaired transport into cells, and abnormal intracellular metabolism with more reliance on glycolysis and less on mitochondrial function. Recent studies have confirmed abnormal neurovascular coupling in a mouse model of AD in response to metabolic challenges, but the supply chain from the vascular system into neurons is disrupted much earlier in dementia than in equivalently aged individuals, contributing to the progressive neuronal degeneration and cognitive dysfunction associated with dementia. We discuss several metabolic treatment approaches, but these depend on characterizing patients as to who would benefit the most. Surrogate biomarkers of metabolism are being developed to include dynamic estimates of neuronal demand, sufficiency of neurovascular coupling, and glymphatic flow to supplement traditional static measurements. These surrogate biomarkers could be used to gauge efficacy of metabolic treatments in slowing down or modifying dementia time course.
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Affiliation(s)
- Dennis A Turner
- Neurosurgery, Neurobiology, and Biomedical Engineering, Duke University Medical Center, Durham, NC 27710, USA.
- Research and Surgery Services, Durham Veterans Affairs Medical Center, Durham, NC 27705, USA.
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13
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Bryan J, Mandan A, Kamat G, Gottschalk WK, Badea A, Adams KJ, Thompson JW, Colton CA, Mukherjee S, Lutz MW. Likelihood ratio statistics for gene set enrichment in Alzheimer's disease pathways. Alzheimers Dement 2021; 17:561-573. [PMID: 33480182 PMCID: PMC8044005 DOI: 10.1002/alz.12223] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
INTRODUCTION The study of Alzheimer's disease (AD) has revealed biological pathways with implications for disease neuropathology and pathophysiology. These pathway-level effects may also be mediated by individual characteristics or covariates such as age or sex. Evaluation of AD biological pathways in the context of interactions with these covariates is critical to the understanding of AD as well as the development of model systems used to study the disease. METHODS Gene set enrichment methods are powerful tools used to interpret gene-level statistics at the level of biological pathways. We introduce a method for quantifying gene set enrichment using likelihood ratio-derived test statistics (gsLRT), which accounts for sample covariates like age and sex. We then use our method to test for age and sex interactions with protein expression levels in AD and to compare the pathway results between human and mouse species. RESULTS Our method, based on nested logistic regressions is competitive with the existing standard for gene set testing in the context of linear models and complex experimental design. The gene sets we identify as having a significant association with AD-both with and without additional covariate interactions-are validated by previous studies. Differences between gsLRT results on mouse and human datasets are observed. DISCUSSION Characterizing biological pathways involved in AD builds on the important work involving single gene drivers. Our gene set enrichment method finds pathways that are significantly related to AD while accounting for covariates that may be relevant to disease development. The method highlights commonalities and differences between human AD and mouse models, which may inform the development of higher fidelity models for the study of AD.
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Affiliation(s)
- Jordan Bryan
- Department of Statistical Science, Duke University, Durham, NC 27708, USA
| | - Arpita Mandan
- Department of Statistical Science, Duke University, Durham, NC 27708, USA
| | - Gauri Kamat
- Department of Statistical Science, Duke University, Durham, NC 27708, USA
| | | | - Alexandra Badea
- Department of Neurology, Duke University, Durham, NC 27708, USA
| | - Kendra J. Adams
- Department of Neurology, Duke University, Durham, NC 27708, USA
| | | | - Carol A. Colton
- Department of Neurology, Duke University, Durham, NC 27708, USA
| | - Sayan Mukherjee
- Department of Statistical Science, Duke University, Durham, NC 27708, USA
- Departments of Mathematics, Computer Science, and Biostatistics & Bioinformatics, Duke University, Durham, NC 27708, USA
| | - Michael W. Lutz
- Department of Neurology, Duke University, Durham, NC 27708, USA
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14
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Badea A, Schmalzigaug R, Kim W, Bonner P, Ahmed U, Johnson GA, Cofer G, Foster M, Anderson RJ, Badea C, Premont RT. Microcephaly with altered cortical layering in GIT1 deficiency revealed by quantitative neuroimaging. Magn Reson Imaging 2021; 76:26-38. [PMID: 33010377 PMCID: PMC7802083 DOI: 10.1016/j.mri.2020.09.023] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Revised: 09/25/2020] [Accepted: 09/25/2020] [Indexed: 01/06/2023]
Abstract
G Protein-Coupled Receptor Kinase-Interacting Protein-1 (GIT1) regulates neuronal functions, including cell and axon migration and synapse formation and maintenance, and GIT1 knockout (KO) mice exhibit learning and memory deficits. We noted that male and female GIT1-KO mice exhibit neuroimaging phenotypes including microcephaly, and altered cortical layering, with a decrease in neuron density in cortical layer V. Micro-CT and magnetic resonance microscopy (MRM) were used to identify morphometric phenotypes for the skulls and throughout the GIT1-KO brains. High field MRM of actively-stained mouse brains from GIT1-KO and wild type (WT) controls (n = 6 per group) allowed segmenting 37 regions, based on co-registration to the Waxholm Space atlas. Overall brain size in GIT1-KO mice was ~32% smaller compared to WT controls. After correcting for brain size, several regions were significantly different in GIT1-KO mice relative to WT, including the gray matter of the ventral thalamic nuclei and the rest of the thalamus, the inferior colliculus, and pontine nuclei. GIT1-KO mice had reduced volume of white matter tracts, most notably in the anterior commissure (~26% smaller), but also in the cerebral peduncle, fornix, and spinal trigeminal tract. On the other hand, the basal ganglia appeared enlarged in GIT1-KO mice, including the globus pallidus, caudate putamen, and particularly the accumbens - supporting a possible vulnerability to addiction. Volume based morphometry based on high-resolution MRM (21.5 μm isotropic voxels) was effective in detecting overall, and local differences in brain volumes in GIT1-KO mice, including in white matter tracts. The reduced relative volume of specific brain regions suggests a critical, but not uniform, role for GIT1 in brain development, conducive to brain microcephaly, and aberrant connectivity.
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Affiliation(s)
- Alexandra Badea
- Department of Radiology, Duke University Medical Center, Durham, NC 27710, United States of America; Department of Neurology, Duke University Medical Center, Durham, NC 27710, United States of America; Departments of Biomedical Engineering, Duke University Medical Center, Durham, NC 27710, United States of America; Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC 27710, United States of America.
| | - Robert Schmalzigaug
- Department of Medicine, Duke University Medical Center, Durham, NC 27710, United States of America
| | - Woojoo Kim
- Department of Medicine, Duke University Medical Center, Durham, NC 27710, United States of America
| | - Pamela Bonner
- Department of Medicine, Duke University Medical Center, Durham, NC 27710, United States of America
| | - Umer Ahmed
- Department of Medicine, Duke University Medical Center, Durham, NC 27710, United States of America
| | - G Allan Johnson
- Department of Radiology, Duke University Medical Center, Durham, NC 27710, United States of America; Departments of Biomedical Engineering, Duke University Medical Center, Durham, NC 27710, United States of America
| | - Gary Cofer
- Department of Radiology, Duke University Medical Center, Durham, NC 27710, United States of America
| | - Mark Foster
- Department of Radiology, Duke University Medical Center, Durham, NC 27710, United States of America
| | - Robert J Anderson
- Department of Radiology, Duke University Medical Center, Durham, NC 27710, United States of America
| | - Cristian Badea
- Department of Radiology, Duke University Medical Center, Durham, NC 27710, United States of America; Departments of Biomedical Engineering, Duke University Medical Center, Durham, NC 27710, United States of America
| | - Richard T Premont
- Department of Medicine, Duke University Medical Center, Durham, NC 27710, United States of America.
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15
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Vitek MP, Araujo JA, Fossel M, Greenberg BD, Howell GR, Rizzo SJS, Seyfried NT, Tenner AJ, Territo PR, Windisch M, Bain LJ, Ross A, Carrillo MC, Lamb BT, Edelmayer RM. Translational animal models for Alzheimer's disease: An Alzheimer's Association Business Consortium Think Tank. ALZHEIMER'S & DEMENTIA (NEW YORK, N. Y.) 2021; 6:e12114. [PMID: 33457489 PMCID: PMC7798310 DOI: 10.1002/trc2.12114] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Revised: 07/04/2020] [Accepted: 07/09/2020] [Indexed: 12/12/2022]
Abstract
Over 5 million Americans and 50 million individuals worldwide are living with Alzheimer's disease (AD). The progressive dementia associated with AD currently has no cure. Although clinical trials in patients are ultimately required to find safe and effective drugs, animal models of AD permit the integration of brain pathologies with learning and memory deficits that are the first step in developing these new drugs. The purpose of the Alzheimer's Association Business Consortium Think Tank meeting was to address the unmet need to improve the discovery and successful development of Alzheimer's therapies. We hypothesize that positive responses to new therapies observed in validated models of AD will provide predictive evidence for positive responses to these same therapies in AD patients. To achieve this goal, we convened a meeting of experts to explore the current state of AD animal models, identify knowledge gaps, and recommend actions for development of next-generation models with better predictability. Among our findings, we all recognize that models reflecting only single aspects of AD pathogenesis do not mimic AD. Models or combinations of new models are needed that incorporate genetics with environmental interactions, timing of disease development, heterogeneous mechanisms and pathways, comorbidities, and other pathologies that lead to AD and related dementias. Selection of the best models requires us to address the following: (1) which animal species, strains, and genetic backgrounds are most appropriate; (2) which models permit efficient use throughout the drug development pipeline; (3) the translatability of behavioral-cognitive assays from animals to patients; and (4) how to match potential AD therapeutics with particular models. Best practice guidelines to improve reproducibility also need to be developed for consistent use of these models in different research settings. To enhance translational predictability, we discuss a multi-model evaluation strategy to de-risk the successful transition of pre-clinical drug assets to the clinic.
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Affiliation(s)
| | | | | | | | | | | | - Nicholas T. Seyfried
- Departments of Biochemistry and NeurologyEmory School of MedicineAtlantaGeorgiaUSA
| | - Andrea J. Tenner
- Department of Molecular Biology and BiochemistryUniversity of CaliforniaIrvineCaliforniaUSA
| | | | | | - Lisa J. Bain
- Independent Science and Medical WriterElversonPennsylvaniaUSA
| | - April Ross
- Former Alzheimer's Association EmployeeChicagoIllinoisUSA
| | | | - Bruce T. Lamb
- Indiana University School of MedicineStark Neurosciences Research InstituteIndianapolisIndianaUSA
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16
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Müller HP, Roselli F, Rasche V, Kassubek J. Diffusion Tensor Imaging-Based Studies at the Group-Level Applied to Animal Models of Neurodegenerative Diseases. Front Neurosci 2020; 14:734. [PMID: 32982659 PMCID: PMC7487414 DOI: 10.3389/fnins.2020.00734] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Accepted: 06/22/2020] [Indexed: 12/11/2022] Open
Abstract
The understanding of human and non-human microstructural brain alterations in the course of neurodegenerative diseases has substantially improved by the non-invasive magnetic resonance imaging (MRI) technique of diffusion tensor imaging (DTI). Animal models (including disease or knockout models) allow for a variety of experimental manipulations, which are not applicable to humans. Thus, the DTI approach provides a promising tool for cross-species cross-sectional and longitudinal investigations of the neurobiological targets and mechanisms of neurodegeneration. This overview with a systematic review focuses on the principles of DTI analysis as used in studies at the group level in living preclinical models of neurodegeneration. The translational aspect from in-vivo animal models toward (clinical) applications in humans is covered as well as the DTI-based research of the non-human brains' microstructure, the methodological aspects in data processing and analysis, and data interpretation at different abstraction levels. The aim of integrating DTI in multiparametric or multimodal imaging protocols will allow the interrogation of DTI data in terms of directional flow of information and may identify the microstructural underpinnings of neurodegeneration-related patterns.
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Affiliation(s)
| | - Francesco Roselli
- Department of Neurology, University of Ulm, Ulm, Germany.,German Center for Neurodegenerative Diseases (DZNE), Ulm, Germany
| | - Volker Rasche
- Core Facility Small Animal MRI, University of Ulm, Ulm, Germany
| | - Jan Kassubek
- Department of Neurology, University of Ulm, Ulm, Germany
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17
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Rong X, Xiang L, Li Y, Yang H, Chen W, Li L, Liang D, Zhou X. Chronic Periodontitis and Alzheimer Disease: A Putative Link of Serum Proteins Identification by 2D-DIGE Proteomics. Front Aging Neurosci 2020; 12:248. [PMID: 32973486 PMCID: PMC7472842 DOI: 10.3389/fnagi.2020.00248] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2020] [Accepted: 07/20/2020] [Indexed: 01/16/2023] Open
Abstract
Increasing evidence indicates Chronic Periodontitis (CP) is a comorbidity of Alzheimer’s disease (AD), which is the most common form of age-related dementia, and for the latter, effective diagnostic and treatment strategies are lacking. Although inflammation is present in both diseases, the exact mechanisms and cross-links between CP and AD are poorly understood; and a direct association between the two has not been reported. This study aimed to identify a direct serum proteins link between AD and CP. Two-dimensional differential in-gel electrophoresis was employed to analyze serum samples from 12 CP patients and 12 age-matched controls. Furthermore, to determine the molecular link between CP and AD, neuroblastoma SK-N-SH APPwt cells were treated with 1 μg/ml of lipopolysaccharide from Porphyromonas gingivalis (P.g-LPS). Ten differentially expressed proteins were identified in CP patients. Among them, nine proteins were up-regulated, and one protein was down-regulated. Of the 10 differentially expressed proteins, five proteins were reportedly involved in the pathology of AD: Cofilin-2, Cathepsin B, Clusterin, Triosephosphate isomerase, and inter-alpha-trypsin inhibitor heavy chain H4 (ITI-H4). Western blotting indicated significantly higher expression of Cofilin-2, Cathepsin B, and Clusterin and lower expression of ITI-H4 in the CP group than in the Control group. The serum concentration of Cathepsin B has a good correlation with MMSE scores. Moreover, the protein level of Cathepsin B (but not that of ADAM10 and BACE1) increased significantly along with a prominent increase in Aβ1–40 and Aβ1–42 in the cell lysates of P.g-LPS-treated SK-N-SH APPwt cells. Cathepsin B inhibition resulted in a sharp decrease in Aβ1–40 and Aβ1–42 in the cell lysates. Furthermore, TNF-α was one of the most important inflammatory cytokines for the P.g-LPS-induced Cathepsin B upregulation in SK-N-SH APPwt cells. These results show that CP and AD share an association, while Cathepsin B could be a key link between the two diseases. The discovery of the identical serum proteins provides a potential mechanism underlying the increased risk of AD in CP patients, which could be critical for elucidating the pathophysiology of AD.
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Affiliation(s)
- Xianfang Rong
- Department of Stomatology, Shenzhen Baoan Women's and Children's Hospital, Jinan University, Shenzhen, China
| | - Liping Xiang
- Department of Stomatology, Shenzhen Baoan Women's and Children's Hospital, Jinan University, Shenzhen, China
| | - Yanfen Li
- Department of Stomatology, Shenzhen Baoan Women's and Children's Hospital, Jinan University, Shenzhen, China
| | - Hongfa Yang
- Department of Cardiology, The Second Affiliated Hospital of the University of South China, Hengyang, China
| | - Weijian Chen
- Department of Stomatology, Shenzhen Baoan Women's and Children's Hospital, Jinan University, Shenzhen, China
| | - Lei Li
- Department of Stomatology, Shenzhen Baoan Women's and Children's Hospital, Jinan University, Shenzhen, China
| | - Defeng Liang
- Department of Stomatology, Shenzhen Baoan Women's and Children's Hospital, Jinan University, Shenzhen, China
| | - Xincai Zhou
- Department of Stomatology, Shenzhen Baoan Women's and Children's Hospital, Jinan University, Shenzhen, China
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18
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Anderson RJ, Long CM, Calabrese ED, Robertson SH, Johnson GA, Cofer GP, O’Brien RJ, Badea A. Optimizing Diffusion Imaging Protocols for Structural Connectomics in Mouse Models of Neurological Conditions. FRONTIERS IN PHYSICS 2020; 8:88. [PMID: 33928076 PMCID: PMC8081353 DOI: 10.3389/fphy.2020.00088] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Network approaches provide sensitive biomarkers for neurological conditions, such as Alzheimer's disease (AD). Mouse models can help advance our understanding of underlying pathologies, by dissecting vulnerable circuits. While the mouse brain contains less white matter compared to the human brain, axonal diameters compare relatively well (e.g., ~0.6 μm in the mouse and ~0.65-1.05 μm in the human corpus callosum). This makes the mouse an attractive test bed for novel diffusion models and imaging protocols. Remaining questions on the accuracy and uncertainty of connectomes have prompted us to evaluate diffusion imaging protocols with various spatial and angular resolutions. We have derived structural connectomes by extracting gradient subsets from a high-spatial, high-angular resolution diffusion acquisition (120 directions, 43-μm-size voxels). We have simulated protocols with 12, 15, 20, 30, 45, 60, 80, 100, and 120 angles and at 43, 86, or 172-μm voxel sizes. The rotational stability of these schemes increased with angular resolution. The minimum condition number was achieved for 120 directions, followed by 60 and 45 directions. The percentage of voxels containing one dyad was exceeded by those with two dyads after 45 directions, and for the highest spatial resolution protocols. For the 86- or 172-μm resolutions, these ratios converged toward 55% for one and 39% for two dyads, respectively, with <7% from voxels with three dyads. Tractography errors, estimated through dyad dispersion, decreased most with angular resolution. Spatial resolution effects became noticeable at 172 μm. Smaller tracts, e.g., the fornix, were affected more than larger ones, e.g., the fimbria. We observed an inflection point for 45 directions, and an asymptotic behavior after 60 directions, corresponding to similar projection density maps. Spatially downsampling to 86 μm, while maintaining the angular resolution, achieved a subgraph similarity of 96% relative to the reference. Using 60 directions with 86- or 172-μm voxels resulted in 94% similarity. Node similarity metrics indicated that major white matter tracts were more robust to downsampling relative to cortical regions. Our study provides guidelines for new protocols in mouse models of neurological conditions, so as to achieve similar connectomes, while increasing efficiency.
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Affiliation(s)
| | | | - Evan D. Calabrese
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, United States
| | | | - G. Allan Johnson
- Department of Radiology, Duke University, Durham, CA, United States
| | - Gary P. Cofer
- Department of Radiology, Duke University, Durham, CA, United States
| | - Richard J. O’Brien
- Department of Neurology, School of Medicine, Duke University, Durham, CA, United States
| | - Alexandra Badea
- Department of Radiology, Duke University, Durham, CA, United States
- Department of Neurology, School of Medicine, Duke University, Durham, CA, United States
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