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Jáni M, Mareček R, Marečková K. Development of white matter in young adulthood: The speed of brain aging and its relationship with changes in fractional anisotropy. Neuroimage 2024:120881. [PMID: 39362507 DOI: 10.1016/j.neuroimage.2024.120881] [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: 07/02/2024] [Revised: 09/25/2024] [Accepted: 09/30/2024] [Indexed: 10/05/2024] Open
Abstract
White matter (WM) development has been studied extensively, but most studies used cross-sectional data, and to the best of our knowledge, none of them considered the possible effects of biological (vs. chronological) age. Therefore, we conducted a longitudinal multimodal study of WM development and studied changes in fractional anisotropy (FA) in the different WM tracts and their relationship with cortical thickness-based measures of brain aging in young adulthood. A total of 105 participants from the European Longitudinal Study of Pregnancy and Childhood (ELSPAC) prenatal birth cohort underwent magnetic resonance imaging (MRI) at the age of 23-24, and the age of 28-30 years. At both time points, FA in the different WM tracts was extracted using the JHU atlas, and brain age gap estimate (BrainAGE) was calculated using the Neuroanatomical Age Prediction using R (NAPR) model based on cortical thickness maps. Changes in FA and the speed of cortical brain aging were calculated as the difference between the respective variables in the late vs. early 20s. We demonstrated tract-specific increases as well as decreases in FA, which indicate that the WM microstructure continues to develop in the third decade of life. Moreover, the significant interaction between the speed of cortical brain aging, tract, and sex on mean FA revealed that a greater speed of cortical brain aging in young adulthood predicted greater decreases in FA in the bilateral cingulum and left superior longitudinal fasciculus in young adult men. Overall, these changes in FA in the WM tracts in young adulthood point out the protracted development of WM microstructure, particularly in men.
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Affiliation(s)
- Martin Jáni
- Central European Institute of Technology, Masaryk University, Brno, Czech Republic
| | - Radek Mareček
- Central European Institute of Technology, Masaryk University, Brno, Czech Republic
| | - Klára Marečková
- Central European Institute of Technology, Masaryk University, Brno, Czech Republic; First Department of Neurology, Faculty of Medicine, Masaryk University and St. Anne's University Hospital, Brno, Czech Republic.
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Jack CR, Arani A, Borowski BJ, Cash DM, Crawford K, Das SR, DeCarli C, Fletcher E, Fox NC, Gunter JL, Ittyerah R, Harvey DJ, Jahanshad N, Maillard P, Malone IB, Nir TM, Reid RI, Reyes DA, Schwarz CG, Senjem ML, Thomas DL, Thompson PM, Tosun D, Yushkevich PA, Ward CP, Weiner MW. Overview of ADNI MRI. Alzheimers Dement 2024. [PMID: 39258539 DOI: 10.1002/alz.14166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2024] [Revised: 07/09/2024] [Accepted: 07/11/2024] [Indexed: 09/12/2024]
Abstract
The magnetic resonance imaging (MRI) Core has been operating since Alzheimer's Disease Neuroimaging Initiative's (ADNI) inception, providing 20 years of data including reliable, multi-platform standardized protocols, carefully curated image data, and quantitative measures provided by expert investigators. The overarching purposes of the MRI Core include: (1) optimizing and standardizing MRI acquisition methods, which have been adopted by many multicenter studies and trials worldwide and (2) providing curated images and numeric summary values from relevant MRI sequences/contrasts to the scientific community. Over time, ADNI MRI has become increasingly complex. To remain technically current, the ADNI MRI protocol has changed substantially over the past two decades. The ADNI 4 protocol contains nine different imaging types (e.g., three dimensional [3D] T1-weighted and fluid-attenuated inversion recovery [FLAIR]). Our view is that the ADNI MRI data are a greatly underutilized resource. The purpose of this paper is to educate the scientific community on ADNI MRI methods and content to promote greater awareness, accessibility, and use. HIGHLIGHTS: The MRI Core provides multi-platform standardized protocols, carefully curated image data, and quantitative analysis by expert groups. The ADNI MRI protocol has undergone major changes over the past two decades to remain technically current. As of April 25, 2024, the following numbers of image series are available: 17,141 3D T1w; 6877 FLAIR; 3140 T2/PD; 6623 GRE; 3237 dMRI; 2846 ASL; 2968 TF-fMRI; and 2861 HighResHippo (see Table 1 for abbreviations). As of April 25, 2024, the following numbers of quantitative analyses are available: FreeSurfer 10,997; BSI 6120; tensor based morphometry (TBM) and TBM-SYN 12,019; WMH 9944; dMRI 1913; ASL 925; TF-fMRI NFQ 2992; and medial temporal subregion volumes 2726 (see Table 4 for abbreviations). ADNI MRI is an underutilized resource that could be more useful to the research community.
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Affiliation(s)
- Clifford R Jack
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | - Arvin Arani
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | - Bret J Borowski
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | - Dave M Cash
- Dementia Research Centre, University College London Institute of Neurology, Queen Square, London, UK
| | - Karen Crawford
- Laboratory of Neuro Imaging (LONI), University of Southern California, Los Angeles, California, USA
| | - Sandhitsu R Das
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Charles DeCarli
- Department of Neurology, University of California, Davis, California, USA
| | - Evan Fletcher
- Department of Neurology, University of California, Davis, California, USA
| | - Nick C Fox
- Dementia Research Centre, University College London Institute of Neurology, Queen Square, London, UK
| | | | - Ranjit Ittyerah
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Danielle J Harvey
- Department of Public Health Sciences, Division of Biostatistics, University of California, Davis, California, USA
| | - Neda Jahanshad
- Keck School of Medicine of USC, Los Angeles, California, USA
| | - Pauline Maillard
- Department of Neurology, University of California, Davis, California, USA
| | - Ian B Malone
- Dementia Research Centre, University College London Institute of Neurology, Queen Square, London, UK
| | - Talia M Nir
- Keck School of Medicine of USC, Los Angeles, California, USA
| | - Robert I Reid
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | - Denise A Reyes
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | | | - Matthew L Senjem
- Department of Information Technology, Mayo Clinic, Rochester, Minnesota, USA
| | - David L Thomas
- Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, London, UK
| | - Paul M Thompson
- Laboratory of Neuro Imaging (LONI), University of Southern California, Los Angeles, California, USA
| | - Duygu Tosun
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA
| | - Paul A Yushkevich
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Chadwick P Ward
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | - Michael W Weiner
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA
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Jirsaraie RJ, Gatavins MM, Pines AR, Kandala S, Bijsterbosch JD, Marek S, Bogdan R, Barch DM, Sotiras A. Mapping the neurodevelopmental predictors of psychopathology. Mol Psychiatry 2024:10.1038/s41380-024-02682-7. [PMID: 39107582 DOI: 10.1038/s41380-024-02682-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 07/13/2024] [Accepted: 07/22/2024] [Indexed: 08/10/2024]
Abstract
Neuroimaging research has uncovered a multitude of neural abnormalities associated with psychopathology, but few prediction-based studies have been conducted during adolescence, and even fewer used neurobiological features that were extracted across multiple neuroimaging modalities. This gap in the literature is critical, as deriving accurate brain-based models of psychopathology is an essential step towards understanding key neural mechanisms and identifying high-risk individuals. As such, we trained adaptive tree-boosting algorithms on multimodal neuroimaging features from the Lifespan Human Connectome Developmental (HCP-D) sample that contained 956 participants between the ages of 8 to 22 years old. Our feature space consisted of 1037 anatomical, 1090 functional, and 192 diffusion MRI features, which were used to derive models that separately predicted internalizing symptoms, externalizing symptoms, and the general psychopathology factor. We found that multimodal models were the most accurate, but all brain-based models of psychopathology yielded out-of-sample predictions that were weakly correlated with actual symptoms (r2 < 0.15). White matter microstructural properties, including orientation dispersion indices and intracellular volume fractions, were the most predictive of general psychopathology, followed by cortical thickness and functional connectivity. Spatially, the most predictive features of general psychopathology were primarily localized within the default mode and dorsal attention networks. These results were mostly consistent across all dimensions of psychopathology, except orientation dispersion indices and the default mode network were not as heavily weighted in the prediction of internalizing and externalizing symptoms. Taken with prior literature, it appears that neurobiological features are an important part of the equation for predicting psychopathology but relying exclusively on neural markers is clearly not sufficient, especially among adolescent samples with subclinical symptoms. Consequently, risk factor models of psychopathology may benefit from incorporating additional sources of information that have also been shown to explain individual differences, such as psychosocial factors, environmental stressors, and genetic vulnerabilities.
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Affiliation(s)
- Robert J Jirsaraie
- Division of Computational and Data Sciences, Washington University in St. Louis, St. Louis, MO, USA
| | - Martins M Gatavins
- Lifespan Brain Institute, Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA, USA
| | - Adam R Pines
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Sridhar Kandala
- Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA
| | - Janine D Bijsterbosch
- Department of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Scott Marek
- Department of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
- AI for Health Institute, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Ryan Bogdan
- Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA
| | - Deanna M Barch
- Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA
- Department of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | - Aristeidis Sotiras
- Department of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA.
- Institute for Informatics, Data Science & Biostatistics, Washington University School of Medicine in St. Louis, St. Louis, MO, USA.
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Yu X, Przybelski SA, Reid RI, Lesnick TG, Raghavan S, Graff‐Radford J, Lowe VJ, Kantarci K, Knopman DS, Petersen RC, Jack CR, Vemuri P. NODDI in gray matter is a sensitive marker of aging and early AD changes. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2024; 16:e12627. [PMID: 39077685 PMCID: PMC11284641 DOI: 10.1002/dad2.12627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Revised: 06/17/2024] [Accepted: 07/02/2024] [Indexed: 07/31/2024]
Abstract
INTRODUCTION Age-related and Alzheimer's disease (AD) dementia-related neurodegeneration impact brain health. While morphometric measures from T1-weighted scans are established biomarkers, they may be less sensitive to earlier changes. Neurite orientation dispersion and density imaging (NODDI), offering biologically meaningful interpretation of tissue microstructure, may be an advanced brain health biomarker. METHODS We contrasted regional gray matter NODDI and morphometric evaluations concerning their correlation with (1) age, (2) clinical diagnosis stage, and (3) tau pathology as assessed by AV1451 positron emission tomography. RESULTS Our study hypothesizes that NODDI measures are more sensitive to aging and early AD changes than morphometric measures. One NODDI output, free water fraction (FWF), showed higher sensitivity to age-related changes, generally better effect sizes in separating mild cognitively impaired from cognitively unimpaired participants, and stronger associations with regional tau deposition than morphometric measures. DISCUSSION These findings underscore NODDI's utility in capturing early neurodegenerative changes and enhancing our understanding of aging and AD. Highlights Neurite orientation dispersion and density imaging can serve as an effective brain health biomarker for aging and early Alzheimer's disease (AD).Free water fraction has higher sensitivity to normal brain aging.Free water fraction has stronger associations with early AD and regional tau deposition.
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Affiliation(s)
- Xi Yu
- Department of Physiology and Biomedical EngineeringMayo ClinicRochesterMinnesotaUSA
| | - Scott A. Przybelski
- Department of Health Sciences ResearchDivision of Biomedical Statistics and InformaticsMayo Clinic‐RochesterRochesterMinnesotaUSA
| | - Robert I. Reid
- Department of Health Sciences ResearchDivision of Biomedical Statistics and InformaticsMayo Clinic‐RochesterRochesterMinnesotaUSA
- Department of RadiologyMayo Clinic‐RochesterRochesterMinnesotaUSA
| | - Timothy G. Lesnick
- Department of Health Sciences ResearchDivision of Biomedical Statistics and InformaticsMayo Clinic‐RochesterRochesterMinnesotaUSA
| | | | | | - Val J. Lowe
- Department of RadiologyMayo Clinic‐RochesterRochesterMinnesotaUSA
| | - Kejal Kantarci
- Department of RadiologyMayo Clinic‐RochesterRochesterMinnesotaUSA
| | - David S. Knopman
- Department of NeurologyMayo Clinic‐RochesterRochesterMinnesotaUSA
| | | | - Clifford R. Jack
- Department of RadiologyMayo Clinic‐RochesterRochesterMinnesotaUSA
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Korbmacher M, van der Meer D, Beck D, Askeland-Gjerde DE, Eikefjord E, Lundervold A, Andreassen OA, Westlye LT, Maximov II. Distinct Longitudinal Brain White Matter Microstructure Changes and Associated Polygenic Risk of Common Psychiatric Disorders and Alzheimer's Disease in the UK Biobank. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2024; 4:100323. [PMID: 39132576 PMCID: PMC11313202 DOI: 10.1016/j.bpsgos.2024.100323] [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: 03/21/2024] [Revised: 03/24/2024] [Accepted: 04/16/2024] [Indexed: 08/13/2024] Open
Abstract
Background During the course of adulthood and aging, white matter (WM) structure and organization are characterized by slow degradation processes such as demyelination and shrinkage. An acceleration of such aging processes has been linked to the development of a range of diseases. Thus, an accurate description of healthy brain maturation, particularly in terms of WM features, is fundamental to the understanding of aging. Methods We used longitudinal diffusion magnetic resonance imaging to provide an overview of WM changes at different spatial and temporal scales in the UK Biobank (UKB) (n = 2678; agescan 1 = 62.38 ± 7.23 years; agescan 2 = 64.81 ± 7.1 years). To examine the genetic overlap between WM structure and common clinical conditions, we tested the associations between WM structure and polygenic risk scores for the most common neurodegenerative disorder, Alzheimer's disease, and common psychiatric disorders (unipolar and bipolar depression, anxiety, obsessive-compulsive disorder, autism, schizophrenia, attention-deficit/hyperactivity disorder) in longitudinal (n = 2329) and cross-sectional (n = 31,056) UKB validation data. Results Our findings indicate spatially distributed WM changes across the brain, as well as distributed associations of polygenic risk scores with WM. Importantly, brain longitudinal changes reflected genetic risk for disorder development better than the utilized cross-sectional measures, with regional differences giving more specific insights into gene-brain change associations than global averages. Conclusions We extend recent findings by providing a detailed overview of WM microstructure degeneration on different spatial levels, helping to understand fundamental brain aging processes. Further longitudinal research is warranted to examine aging-related gene-brain associations.
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Affiliation(s)
- Max Korbmacher
- Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway
- NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
- Mohn Medical Imaging and Visualization Centre, Bergen, Norway
| | - Dennis van der Meer
- NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
- Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands
| | - Dani Beck
- NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Daniel E. Askeland-Gjerde
- NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Eli Eikefjord
- Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway
- Mohn Medical Imaging and Visualization Centre, Bergen, Norway
| | - Arvid Lundervold
- Mohn Medical Imaging and Visualization Centre, Bergen, Norway
- Department of Biomedicine, University of Bergen, Bergen, Norway
| | - Ole A. Andreassen
- NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Lars T. Westlye
- NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Ivan I. Maximov
- Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway
- NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
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Raghavan S, Lesnick TG, Castillo AM, Reid RI, Fought AJ, Thostenson KB, Johnson Sparrman KL, Gehrking TL, Gehrking JA, Sletten DM, Low PA, Singer W, Vemuri P. White Matter Abnormalities Track Disease Progression in Multiple System Atrophy. Mov Disord Clin Pract 2024. [PMID: 38923361 DOI: 10.1002/mdc3.14147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Revised: 04/16/2024] [Accepted: 05/26/2024] [Indexed: 06/28/2024] Open
Abstract
BACKGROUND White matter (WM) abnormalities have been implicated in clinically relevant functional decline in multiple system atrophy (MSA). OBJECTIVE To identify the WM and gray matter (GM) abnormalities in MSA and assess the utility of longitudinal structural and diffusion changes as surrogate markers for tracking disease progression in MSA. METHODS Twenty-seven participants with early MSA [15 with clinically predominant cerebellar (MSA-C) and 12 with clinically predominant parkinsonian features (MSA-P)] and 14 controls were enrolled as a part of our prospective, longitudinal study of synucleinopathies. Using structural magnetic resonance imaging (MRI) and diffusion MRI (diffusion tensor and neurite orientation and dispersion density imaging), we analyzed whole and regional brain changes in these participants. We also evaluated temporal imaging trajectories based on up to three annual follow-up scans and assessed the impact of baseline diagnosis on these imaging biomarkers using mixed-effect models. RESULTS MSA patients exhibited more widespread WM changes than GM, particularly in the cerebellum and brainstem, with greater severity in MSA-C. Structural and diffusion measures in the cerebellum WM and brainstem deteriorated with disease progression. Rates of progression of these abnormalities were similar in both MSA subtypes, reflecting increasing overlap of clinical features over time. CONCLUSION WM abnormalities are core features of MSA disease progression and advance at similar rates in clinical MSA subtypes. Multimodal MRI imaging reveals novel insights into the distribution and pattern of brain abnormalities and their progression in MSA. Selected structural and diffusion measures may be useful for tracking disease progression in MSA clinical trials.
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Affiliation(s)
| | - Timothy G Lesnick
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Anna M Castillo
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Robert I Reid
- Department of Information Technology, Mayo Clinic, Rochester, MN, USA
| | - Angela J Fought
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | | | | | | | | | | | - Phillip A Low
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
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Wearn A, Tremblay SA, Tardif CL, Leppert IR, Gauthier CJ, Baracchini G, Hughes C, Hewan P, Tremblay-Mercier J, Rosa-Neto P, Poirier J, Villeneuve S, Schmitz TW, Turner GR, Spreng RN. Neuromodulatory subcortical nucleus integrity is associated with white matter microstructure, tauopathy and APOE status. Nat Commun 2024; 15:4706. [PMID: 38830849 PMCID: PMC11148077 DOI: 10.1038/s41467-024-48490-z] [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: 11/29/2023] [Accepted: 05/01/2024] [Indexed: 06/05/2024] Open
Abstract
The neuromodulatory subcortical nuclei within the isodendritic core (IdC) are the earliest sites of tauopathy in Alzheimer's disease (AD). They project broadly throughout the brain's white matter. We investigated the relationship between IdC microstructure and whole-brain white matter microstructure to better understand early neuropathological changes in AD. Using multiparametric quantitative magnetic resonance imaging we observed two covariance patterns between IdC and white matter microstructure in 133 cognitively unimpaired older adults (age 67.9 ± 5.3 years) with familial risk for AD. IdC integrity related to 1) whole-brain neurite density, and 2) neurite orientation dispersion in white matter tracts known to be affected early in AD. Pattern 2 was associated with CSF concentration of phosphorylated-tau, indicating AD specificity. Apolipoprotein-E4 carriers expressed both patterns more strongly than non-carriers. IdC microstructure variation is reflected in white matter, particularly in AD-affected tracts, highlighting an early mechanism of pathological development.
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Affiliation(s)
- Alfie Wearn
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, H3A 2B4, QC, Canada.
| | - Stéfanie A Tremblay
- Department of Physics, Concordia University, Montreal, H4B 1R6, QC, Canada
- Montreal Heart Institute, Montreal, H1T 1C8, QC, Canada
- School of Health, Concordia University, Montreal, H4B 1R6, QC, Canada
| | - Christine L Tardif
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, H3A 2B4, QC, Canada
- McConnell Brain Imaging Centre, McGill University, Montreal, H3A 2B4, QC, Canada
- Department of Biomedical Engineering, McGill University, McGill, H3A 2B4, QC, Canada
| | - Ilana R Leppert
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, H3A 2B4, QC, Canada
- McConnell Brain Imaging Centre, McGill University, Montreal, H3A 2B4, QC, Canada
| | - Claudine J Gauthier
- Department of Physics, Concordia University, Montreal, H4B 1R6, QC, Canada
- Montreal Heart Institute, Montreal, H1T 1C8, QC, Canada
- School of Health, Concordia University, Montreal, H4B 1R6, QC, Canada
| | - Giulia Baracchini
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, H3A 2B4, QC, Canada
| | - Colleen Hughes
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, H3A 2B4, QC, Canada
| | - Patrick Hewan
- Department of Psychology, York University, Toronto, M3J 1P3, ON, Canada
| | | | - Pedro Rosa-Neto
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, H3A 2B4, QC, Canada
- McConnell Brain Imaging Centre, McGill University, Montreal, H3A 2B4, QC, Canada
- Douglas Mental Health University Institute-Research Center, Verdun, H4H 1R3, QC, Canada
| | - Judes Poirier
- Douglas Mental Health University Institute-Research Center, Verdun, H4H 1R3, QC, Canada
- Department of Psychiatry, McGill University, Montreal, H3A 1A1, QC, Canada
| | - Sylvia Villeneuve
- McConnell Brain Imaging Centre, McGill University, Montreal, H3A 2B4, QC, Canada
- Douglas Mental Health University Institute-Research Center, Verdun, H4H 1R3, QC, Canada
- Department of Psychiatry, McGill University, Montreal, H3A 1A1, QC, Canada
| | - Taylor W Schmitz
- Department of Physiology & Pharmacology, Western Institute for Neuroscience, Western University, London, N6A 5C1, ON, Canada
| | - Gary R Turner
- Department of Psychology, York University, Toronto, M3J 1P3, ON, Canada
| | - R Nathan Spreng
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, H3A 2B4, QC, Canada.
- McConnell Brain Imaging Centre, McGill University, Montreal, H3A 2B4, QC, Canada.
- Douglas Mental Health University Institute-Research Center, Verdun, H4H 1R3, QC, Canada.
- Department of Psychiatry, McGill University, Montreal, H3A 1A1, QC, Canada.
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8
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Mak E, Reid RI, Przybelski SA, Lesnick TG, Schwarz CG, Senjem ML, Raghavan S, Vemuri P, Jack CR, Min HK, Jain MK, Miyagawa T, Forsberg LK, Fields JA, Savica R, Graff-Radford J, Jones DT, Botha H, St Louis EK, Knopman DS, Ramanan VK, Dickson DW, Graff-Radford NR, Ferman TJ, Petersen RC, Lowe VJ, Boeve BF, O'Brien JT, Kantarci K. Influences of amyloid-β and tau on white matter neurite alterations in dementia with Lewy bodies. NPJ Parkinsons Dis 2024; 10:76. [PMID: 38570511 PMCID: PMC10991290 DOI: 10.1038/s41531-024-00684-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 03/13/2024] [Indexed: 04/05/2024] Open
Abstract
Dementia with Lewy bodies (DLB) is a neurodegenerative condition often co-occurring with Alzheimer's disease (AD) pathology. Characterizing white matter tissue microstructure using Neurite Orientation Dispersion and Density Imaging (NODDI) may help elucidate the biological underpinnings of white matter injury in individuals with DLB. In this study, diffusion tensor imaging (DTI) and NODDI metrics were compared in 45 patients within the dementia with Lewy bodies spectrum (mild cognitive impairment with Lewy bodies (n = 13) and probable dementia with Lewy bodies (n = 32)) against 45 matched controls using conditional logistic models. We evaluated the associations of tau and amyloid-β with DTI and NODDI parameters and examined the correlations of AD-related white matter injury with Clinical Dementia Rating (CDR). Structural equation models (SEM) explored relationships among age, APOE ε4, amyloid-β, tau, and white matter injury. The DLB spectrum group exhibited widespread white matter abnormalities, including reduced fractional anisotropy, increased mean diffusivity, and decreased neurite density index. Tau was significantly associated with limbic and temporal white matter injury, which was, in turn, associated with worse CDR. SEM revealed that amyloid-β exerted indirect effects on white matter injury through tau. We observed widespread disruptions in white matter tracts in DLB that were not attributed to AD pathologies, likely due to α-synuclein-related injury. However, a fraction of the white matter injury could be attributed to AD pathology. Our findings underscore the impact of AD pathology on white matter integrity in DLB and highlight the utility of NODDI in elucidating the biological basis of white matter injury in DLB.
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Affiliation(s)
- Elijah Mak
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Robert I Reid
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
- Department of Information Technology, Mayo Clinic, Rochester, MN, USA
| | - Scott A Przybelski
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Timothy G Lesnick
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | | | - Matthew L Senjem
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
- Department of Information Technology, Mayo Clinic, Rochester, MN, USA
| | | | | | | | - Hoon Ki Min
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - Manoj K Jain
- Department of Radiology, Mayo Clinic, Jacksonville, FL, USA
| | - Toji Miyagawa
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | | | - Julie A Fields
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
| | - Rodolfo Savica
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | | | - David T Jones
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - Hugo Botha
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - Erik K St Louis
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
- Center for Sleep Medicine, Division of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo Clinic, Rochester, MN, USA
| | | | | | - Dennis W Dickson
- Department of Psychiatry and Psychology, Mayo Clinic, Jacksonville, FL, USA
| | | | - Tanis J Ferman
- Department of Neurology, Mayo Clinic, Jacksonville, FL, USA
| | - Ronald C Petersen
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - Val J Lowe
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | | | - John T O'Brien
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Kejal Kantarci
- Department of Radiology, Mayo Clinic, Rochester, MN, USA.
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9
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Jochems ACC, Muñoz Maniega S, Clancy U, Arteaga C, Jaime Garcia D, Chappell FM, Hewins W, Locherty R, Backhouse EV, Barclay G, Jardine C, McIntyre D, Gerrish I, Kampaite A, Sakka E, Valdés Hernández M, Wiseman S, Bastin ME, Stringer MS, Thrippleton MJ, Doubal FN, Wardlaw JM. Magnetic Resonance Imaging Tissue Signatures Associated With White Matter Changes Due to Sporadic Cerebral Small Vessel Disease Indicate That White Matter Hyperintensities Can Regress. J Am Heart Assoc 2024; 13:e032259. [PMID: 38293936 PMCID: PMC11056146 DOI: 10.1161/jaha.123.032259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Accepted: 12/21/2023] [Indexed: 02/01/2024]
Abstract
BACKGROUND White matter hyperintensities (WMHs) might regress and progress contemporaneously, but we know little about underlying mechanisms. We examined WMH change and underlying quantitative magnetic resonance imaging tissue measures over 1 year in patients with minor ischemic stroke with sporadic cerebral small vessel disease. METHODS AND RESULTS We defined areas of stable normal-appearing white matter, stable WMHs, progressing and regressing WMHs based on baseline and 1-year brain magnetic resonance imaging. In these areas we assessed tissue characteristics with quantitative T1, fractional anisotropy (FA), mean diffusivity (MD), and neurite orientation dispersion and density imaging (baseline only). We compared tissue signatures cross-sectionally between areas, and longitudinally within each area. WMH change masks were available for N=197. Participants' mean age was 65.61 years (SD, 11.10), 59% had a lacunar infarct, and 68% were men. FA and MD were available for N=195, quantitative T1 for N=182, and neurite orientation dispersion and density imaging for N=174. Cross-sectionally, all 4 tissue classes differed for FA, MD, T1, and Neurite Density Index. Longitudinally, in regressing WMHs, FA increased with little change in MD and T1 (difference estimate, 0.011 [95% CI, 0.006-0.017]; -0.002 [95% CI, -0.008 to 0.003] and -0.003 [95% CI, -0.009 to 0.004]); in progressing and stable WMHs, FA decreased (-0.022 [95% CI, -0.027 to -0.017] and -0.009 [95% CI, -0.011 to -0.006]), whereas MD and T1 increased (progressing WMHs, 0.057 [95% CI, 0.050-0.063], 0.058 [95% CI, 0.050 -0.066]; stable WMHs, 0.054 [95% CI, 0.045-0.063], 0.049 [95% CI, 0.039-0.058]); and in stable normal-appearing white matter, MD increased (0.004 [95% CI, 0.003-0.005]), whereas FA and T1 slightly decreased and increased (-0.002 [95% CI, -0.004 to -0.000] and 0.005 [95% CI, 0.001-0.009]). CONCLUSIONS Quantitative magnetic resonance imaging shows that WMHs that regress have less abnormal microstructure at baseline than stable WMHs and follow trajectories indicating tissue improvement compared with stable and progressing WMHs.
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Affiliation(s)
- Angela C. C. Jochems
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUnited Kingdom
- UK Dementia Research Institute at the University of EdinburghEdinburghUnited Kingdom
| | - Susana Muñoz Maniega
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUnited Kingdom
- UK Dementia Research Institute at the University of EdinburghEdinburghUnited Kingdom
| | - Una Clancy
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUnited Kingdom
- UK Dementia Research Institute at the University of EdinburghEdinburghUnited Kingdom
| | - Carmen Arteaga
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUnited Kingdom
- UK Dementia Research Institute at the University of EdinburghEdinburghUnited Kingdom
| | - Daniela Jaime Garcia
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUnited Kingdom
- UK Dementia Research Institute at the University of EdinburghEdinburghUnited Kingdom
| | - Francesca M. Chappell
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUnited Kingdom
- UK Dementia Research Institute at the University of EdinburghEdinburghUnited Kingdom
| | - Will Hewins
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUnited Kingdom
- UK Dementia Research Institute at the University of EdinburghEdinburghUnited Kingdom
| | - Rachel Locherty
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUnited Kingdom
- UK Dementia Research Institute at the University of EdinburghEdinburghUnited Kingdom
| | - Ellen V. Backhouse
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUnited Kingdom
- UK Dementia Research Institute at the University of EdinburghEdinburghUnited Kingdom
| | - Gayle Barclay
- Edinburgh Imaging Facility, Royal Infirmary of EdinburghEdinburghUnited Kingdom
| | - Charlotte Jardine
- Edinburgh Imaging Facility, Royal Infirmary of EdinburghEdinburghUnited Kingdom
| | - Donna McIntyre
- Edinburgh Imaging Facility, Royal Infirmary of EdinburghEdinburghUnited Kingdom
| | - Iona Gerrish
- Edinburgh Imaging Facility, Royal Infirmary of EdinburghEdinburghUnited Kingdom
| | - Agniete Kampaite
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUnited Kingdom
| | - Eleni Sakka
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUnited Kingdom
| | - Maria Valdés Hernández
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUnited Kingdom
- UK Dementia Research Institute at the University of EdinburghEdinburghUnited Kingdom
| | - Stewart Wiseman
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUnited Kingdom
- UK Dementia Research Institute at the University of EdinburghEdinburghUnited Kingdom
| | - Mark E. Bastin
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUnited Kingdom
| | - Michael S. Stringer
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUnited Kingdom
- UK Dementia Research Institute at the University of EdinburghEdinburghUnited Kingdom
| | - Michael J. Thrippleton
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUnited Kingdom
- UK Dementia Research Institute at the University of EdinburghEdinburghUnited Kingdom
- Edinburgh Imaging Facility, Royal Infirmary of EdinburghEdinburghUnited Kingdom
| | - Fergus N. Doubal
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUnited Kingdom
- UK Dementia Research Institute at the University of EdinburghEdinburghUnited Kingdom
| | - Joanna M. Wardlaw
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUnited Kingdom
- UK Dementia Research Institute at the University of EdinburghEdinburghUnited Kingdom
- Edinburgh Imaging Facility, Royal Infirmary of EdinburghEdinburghUnited Kingdom
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10
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Raghavan S, Przybelski SA, Lesnick TG, Fought AJ, Reid RI, Gebre RK, Windham BG, Algeciras‐Schimnich A, Machulda MM, Vassilaki M, Knopman DS, Jack CR, Petersen RC, Graff‐Radford J, Vemuri P. Vascular risk, gait, behavioral, and plasma indicators of VCID. Alzheimers Dement 2024; 20:1201-1213. [PMID: 37932910 PMCID: PMC10916988 DOI: 10.1002/alz.13540] [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] [Revised: 10/06/2023] [Accepted: 10/11/2023] [Indexed: 11/08/2023]
Abstract
INTRODUCTION Cost-effective screening tools for vascular contributions to cognitive impairment and dementia (VCID) has significant implications. We evaluated non-imaging indicators of VCID using magnetic resonance imaging (MRI)-measured white matter (WM) damage and hypothesized that these indicators differ based on age. METHODS In 745 participants from the Mayo Clinic Study of Aging (≥50 years of age) with serial WM assessments from diffusion MRI and fluid-attenuated inversion recovery (FLAIR)-MRI, we examined associations between baseline non-imaging indicators (demographics, vascular risk factors [VRFs], gait, behavioral, plasma glial fibrillary acidic protein [GFAP], and plasma neurofilament light chain [NfL]) and WM damage across three age tertiles. RESULTS VRFs and gait were associated with diffusion changes even in low age strata. All measures (VRFs, gait, behavioral, plasma GFAP, plasma NfL) were associated with white matter hyperintensities (WMHs) but mainly in intermediate and high age strata. DISCUSSION Non-imaging indicators of VCID were related to WM damage and may aid in screening participants and assessing outcomes for VCID. HIGHLIGHTS Non-imaging indicators of VCID can aid in prediction of MRI-measured WM damage but their importance differed by age. Vascular risk and gait measures were associated with early VCID changes measured using diffusion MRI. Plasma markers explained variability in WMH across age strata. Most non-imaging measures explained variability in WMH and vascular WM scores in intermediate and older age groups. The framework developed here can be used to evaluate new non-imaging VCID indicators proposed in the future.
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Affiliation(s)
| | | | - Timothy G. Lesnick
- Department of Quantitative Health SciencesMayo ClinicRochesterMinnesotaUSA
| | - Angela J. Fought
- Department of Quantitative Health SciencesMayo ClinicRochesterMinnesotaUSA
| | - Robert I. Reid
- Department of Information TechnologyMayo ClinicRochesterMinnesotaUSA
| | | | - B. Gwen Windham
- Department of MedicineUniversity of Mississippi Medical CenterJacksonUSA
| | | | | | - Maria Vassilaki
- Department of Quantitative Health SciencesMayo ClinicRochesterMinnesotaUSA
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11
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Corriveau-Lecavalier N, Tosakulwong N, Lesnick TG, Fought AJ, Reid RI, Schwarz CG, Senjem ML, Jack CR, Jones DT, Vemuri P, Rademakers R, Ramos EM, Geschwind DH, Knopman DS, Botha H, Savica R, Graff-Radford J, Ramanan VK, Fields JA, Graff-Radford N, Wszolek Z, Forsberg LK, Petersen RC, Heuer HW, Boxer AL, Rosen HJ, Boeve BF, Kantarci K. Neurite-based white matter alterations in MAPT mutation carriers: A multi-shell diffusion MRI study in the ALLFTD consortium. Neurobiol Aging 2024; 134:135-145. [PMID: 38091751 PMCID: PMC10872472 DOI: 10.1016/j.neurobiolaging.2023.12.001] [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: 07/05/2023] [Revised: 11/28/2023] [Accepted: 12/01/2023] [Indexed: 12/23/2023]
Abstract
We assessed white matter (WM) integrity in MAPT mutation carriers (16 asymptomatic, 5 symptomatic) compared to 31 non-carrier family controls using diffusion tensor imaging (DTI) (fractional anisotropy; FA, mean diffusivity; MD) and neurite orientation dispersion and density imaging (NODDI) (neurite density index; NDI, orientation and dispersion index; ODI). Linear mixed-effects models accounting for age and family relatedness revealed alterations across DTI and NODDI metrics in all mutation carriers and in symptomatic carriers, with the most significant differences involving fronto-temporal WM tracts. Asymptomatic carriers showed higher entorhinal MD and lower cingulum FA and patterns of higher ODI mostly involving temporal areas and long association and projections fibers. Regression models between estimated time to or time from disease and DTI and NODDI metrics in key regions (amygdala, cingulum, entorhinal, inferior temporal, uncinate fasciculus) in all carriers showed increasing abnormalities with estimated time to or time from disease onset, with FA and NDI showing the strongest relationships. Neurite-based metrics, particularly ODI, appear to be particularly sensitive to early WM involvement in asymptomatic carriers.
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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
| | | | - Timothy G Lesnick
- Departmenf of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Angela J Fought
- Departmenf of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Robert I Reid
- 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
| | | | - Rosa Rademakers
- Department of Neuroscience, Mayo Clinic Jacksonville, FL, USA; Center for Molecular Neurology, Antwerp University, Belgium
| | | | | | | | - Hugo Botha
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - Rodolfo Savica
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | | | | | - Julie A Fields
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
| | | | | | | | | | - Hilary W Heuer
- Department of Neurology, University of California San Francisco, CA, USA
| | - Adam L Boxer
- Department of Neurology, University of California San Francisco, CA, USA
| | - Howard J Rosen
- Department of Neurology, University of California San Francisco, CA, USA
| | | | - Kejal Kantarci
- Department of Radiology, Mayo Clinic, Rochester, MN, USA.
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12
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Fernandez L, Corben LA, Bilal H, Delatycki MB, Egan GF, Harding IH. Free-Water Imaging in Friedreich Ataxia Using Multi-Compartment Models. Mov Disord 2024; 39:370-379. [PMID: 37927246 DOI: 10.1002/mds.29648] [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: 07/28/2023] [Revised: 09/14/2023] [Accepted: 10/11/2023] [Indexed: 11/07/2023] Open
Abstract
BACKGROUND The neurological phenotype of Friedreich ataxia (FRDA) is characterized by neurodegeneration and neuroinflammation in the cerebellum and brainstem. Novel neuroimaging approaches quantifying brain free-water using diffusion magnetic resonance imaging (dMRI) are potentially more sensitive to these processes than standard imaging markers. OBJECTIVES To quantify the extent of free-water and microstructural change in FRDA-relevant brain regions using neurite orientation dispersion and density imaging (NODDI), and bitensor diffusion tensor imaging (btDTI). METHOD Multi-shell dMRI was acquired from 14 individuals with FRDA and 14 controls. Free-water measures from NODDI (FISO) and btDTI (FW) were compared between groups in the cerebellar cortex, dentate nuclei, cerebellar peduncles, and brainstem. The relative sensitivity of the free-water measures to group differences was compared to microstructural measures of NODDI intracellular volume, free-water corrected fractional anisotropy, and conventional uncorrected fractional anisotropy. RESULTS In individuals with FRDA, FW was elevated in the cerebellar cortex, peduncles (excluding middle), dentate, and brainstem (P < 0.005). FISO was elevated primarily in the cerebellar lobules (P < 0.001). On average, FW effect sizes were larger than all other markers (mean ηρ 2 = 0.43), although microstructural measures also had very large effects in the superior and inferior cerebellar peduncles and brainstem (ηρ 2 > 0.37). Across all regions and metrics, effect sizes were largest in the superior cerebellar peduncles (ηρ 2 > 0.46). CONCLUSIONS Multi-compartment diffusion measures of free-water and neurite integrity distinguish FRDA from controls with large effects. Free-water magnitude in the brainstem and cerebellum provided the greatest distinction between groups. This study supports further applications of multi-compartment diffusion modeling, and investigations of free-water as a measure of disease expression and progression in FRDA. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Lara Fernandez
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia
| | - Louise A Corben
- Bruce Lefroy Centre for Genetic Health Research, Murdoch Children's Research Institute, Parkville, Victoria, Australia
- Department of Paediatrics, University of Melbourne, Parkville, Victoria, Australia
- Turner Institute for Brain and Mental Health & School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia
| | - Hiba Bilal
- Turner Institute for Brain and Mental Health & School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia
| | - Martin B Delatycki
- Bruce Lefroy Centre for Genetic Health Research, Murdoch Children's Research Institute, Parkville, Victoria, Australia
- Department of Paediatrics, University of Melbourne, Parkville, Victoria, Australia
- Victorian Clinical Genetics Service, Melbourne, Victoria, Australia
| | - Gary F Egan
- Turner Institute for Brain and Mental Health & School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia
- Monash Biomedical Imaging, Monash University, Melbourne, Victoria, Australia
| | - Ian H Harding
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia
- Monash Biomedical Imaging, Monash University, Melbourne, Victoria, Australia
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13
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Burzynska AZ, Anderson C, Arciniegas DB, Calhoun V, Choi IY, Mendez Colmenares A, Kramer AF, Li K, Lee J, Lee P, Thomas ML. Correlates of axonal content in healthy adult span: Age, sex, myelin, and metabolic health. CEREBRAL CIRCULATION - COGNITION AND BEHAVIOR 2024; 6:100203. [PMID: 38292016 PMCID: PMC10827486 DOI: 10.1016/j.cccb.2024.100203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 01/10/2024] [Accepted: 01/11/2024] [Indexed: 02/01/2024]
Abstract
As the emerging treatments that target grey matter pathology in Alzheimer's Disease have limited effectiveness, there is a critical need to identify new neural targets for treatments. White matter's (WM) metabolic vulnerability makes it a promising candidate for new interventions. This study examined the age and sex differences in estimates of axonal content, as well the associations of with highly prevalent modifiable health risk factors such as metabolic syndrome and adiposity. We estimated intra-axonal volume fraction (ICVF) using the Neurite Orientation Dispersion and Density Imaging (NODDI) in a sample of 89 cognitively and neurologically healthy adults (20-79 years). We showed that ICVF correlated positively with age and estimates of myelin content. The ICVF was also lower in women than men, across all ages, which difference was accounted for by intracranial volume. Finally, we found no association of metabolic risk or adiposity scores with the current estimates of ICVF. In addition, the previously observed adiposity-myelin associations (Burzynska et al., 2023) were independent of ICVF. Although our findings confirm the vulnerability of axons to aging, they suggest that metabolic dysfunction may selectively affect myelin content, at least in cognitively and neurologically healthy adults with low metabolic risk, and when using the specific MRI techniques. Future studies need to revisit our findings using larger samples and different MRI approaches, and identify modifiable factors that accelerate axonal deterioration as well as mechanisms linking peripheral metabolism with the health of myelin.
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Affiliation(s)
- Agnieszka Z Burzynska
- The BRAiN lab, Department of Human Development and Family Studies/Molecular, Cellular and Integrative Neurosciences, Colorado State University, Fort Collins, CO, USA
| | - Charles Anderson
- Department of Computer Science, Colorado State University, Fort Collins, CO, USA
| | - David B. Arciniegas
- Marcus Institute for Brain Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Vince Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory, Atlanta, GA, USA
| | - In-Young Choi
- Department of Neurology, Department of Radiology, Hoglund Biomedical Imaging Center, University of Kansas Medical Center, Kansas City, KS, USA
| | - Andrea Mendez Colmenares
- The BRAiN lab, Department of Human Development and Family Studies/Molecular, Cellular and Integrative Neurosciences, Colorado State University, Fort Collins, CO, USA
| | - Arthur F Kramer
- Beckman Institute for Advanced Science and Technology at the University of Illinois, IL, USA
- Center for Cognitive & Brain Health, Northeastern University, Boston, MA, USA
| | - Kaigang Li
- Department of Health and Exercise Science, Colorado State University, Fort Collins, CO, USA
| | - Jongho Lee
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea
| | - Phil Lee
- Department of Radiology, Hoglund Biomedical Imaging Center, University of Kansas Medical Center, Kansas City, KS, USA
| | - Michael L Thomas
- Department of Psychology, Colorado State University, Fort Collins, CO, USA
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14
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Zhong S, Lou J, Ma K, Shu Z, Chen L, Li C, Ye Q, Zhou L, Shen Y, Ye X, Zhang J. Disentangling in-vivo microstructural changes of white and gray matter in mild cognitive impairment and Alzheimer's disease: a systematic review and meta-analysis. Brain Imaging Behav 2023; 17:764-777. [PMID: 37752311 DOI: 10.1007/s11682-023-00805-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/14/2023] [Indexed: 09/28/2023]
Abstract
The microstructural characteristics of white and gray matter in mild cognitive impairment (MCI) and the early-stage of Alzheimer's disease (AD) remain unclear. This study aimed to systematically identify the microstructural damages of MCI/AD in studies using neurite orientation dispersion and density imaging (NODDI), and explore their correlations with cognitive performance. Multiple databases were searched for eligible studies. The 10 eligible NODDI studies were finally included. Patients with MCI/AD showed overall significant reductions in neurite density index (NDI) of specific white matter structures in bilateral hemispheres (left hemisphere: -0.40 [-0.53, -0.27], P < 0.001; right: -0.33 [-0.47, -0.19], P < 0.001), involving the bilateral superior longitudinal fasciculus (SLF), uncinate fasciculus (UF), the left posterior thalamic radiation (PTR), and the left cingulum. White matter regions exhibited significant increased orientation dispersion index (ODI) (left: 0.25 [0.02, 0.48], P < 0.05; right: 0.27 [0.07, 0.46], P < 0.05), including the left cingulum, the right UF, and the bilateral parahippocampal cingulum (PHC), and PTR. Additionally, the ODI of gray matter showed significant reduction in bilateral hippocampi (left: -0.97 [-1.42, -0.51], P < 0.001; right: -0.90 [-1.35, -0.45], P < 0.001). The cognitive performance in MCI/AD was significantly associated with NDI (r = 0.50, P < 0.001). Our findings highlight the microstructural changes in MCI/AD were characterized by decreased fiber orientation dispersion in the hippocampus, and decreased neurite density and increased fiber orientation dispersion in specific white matter tracts, including the cingulum, UF, and PTR. Moreover, the decreased NDI may indicate the declined cognitive level of MCI/AD patients.
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Affiliation(s)
- Shuchang Zhong
- Center for Rehabilitation Medicine, Rehabilitation & Sports Medicine Research Institute of Zhejiang Province, Department of Rehabilitation Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Jingjing Lou
- Center for Rehabilitation Medicine, Rehabilitation & Sports Medicine Research Institute of Zhejiang Province, Department of Rehabilitation Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Ke Ma
- Center for Rehabilitation Medicine, Rehabilitation & Sports Medicine Research Institute of Zhejiang Province, Department of Rehabilitation Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Zhenyu Shu
- Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Lin Chen
- Center for Rehabilitation Medicine, Rehabilitation & Sports Medicine Research Institute of Zhejiang Province, Department of Rehabilitation Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Chao Li
- Center for Rehabilitation Medicine, Rehabilitation & Sports Medicine Research Institute of Zhejiang Province, Department of Rehabilitation Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Qing Ye
- Center for Rehabilitation Medicine, Rehabilitation & Sports Medicine Research Institute of Zhejiang Province, Department of Rehabilitation Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Liang Zhou
- Center for Rehabilitation Medicine, Rehabilitation & Sports Medicine Research Institute of Zhejiang Province, Department of Rehabilitation Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Ye Shen
- Center for Rehabilitation Medicine, Rehabilitation & Sports Medicine Research Institute of Zhejiang Province, Department of Rehabilitation Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Xiangming Ye
- Center for Rehabilitation Medicine, Rehabilitation & Sports Medicine Research Institute of Zhejiang Province, Department of Rehabilitation Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Jie Zhang
- Center for Rehabilitation Medicine, Rehabilitation & Sports Medicine Research Institute of Zhejiang Province, Department of Rehabilitation Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China.
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15
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Pittock RR, Aakre JA, Castillo AM, Ramanan VK, Kremers WK, Jack CR, Vemuri P, Lowe VJ, Knopman DS, Petersen RC, Graff-Radford J, Vassilaki M. Eligibility for Anti-Amyloid Treatment in a Population-Based Study of Cognitive Aging. Neurology 2023; 101:e1837-e1849. [PMID: 37586881 PMCID: PMC10663008 DOI: 10.1212/wnl.0000000000207770] [Citation(s) in RCA: 31] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 08/04/2023] [Indexed: 08/18/2023] Open
Abstract
BACKGROUND AND OBJECTIVES Treatment options for Alzheimer disease (AD) are limited and have focused mainly on symptomatic therapy and improving quality of life. Recently, lecanemab, an anti-β-amyloid monoclonal antibody (mAb), received accelerated approval by the US Food and Drug Administration for treatment in the early stages of biomarker-confirmed symptomatic AD. An additional anti-β-amyloid mAb, aducanumab, was approved in 2021, and more will potentially become available in the near future. Research on the applicability and generalizability of the anti-β-amyloid mAb eligibility criteria on adults with biomarkers available in the general population has been lacking. The study's primary aim was to apply the clinical trial eligibility criteria for lecanemab treatment to participants with early AD of the population-based Mayo Clinic Study of Aging (MCSA) and assess the generalizability of anti-amyloid treatment. The secondary aim of this study was to apply the clinical trial eligibility criteria for aducanumab treatment in MCSA participants. METHODS This cross-sectional study aimed to apply the clinical trial eligibility criteria for lecanemab and aducanumab treatment to participants with early AD of the population-based MCSA and assess the generalizability of anti-amyloid treatment. RESULTS Two hundred thirty-seven MCSA participants (mean age [SD] 80.9 [6.3] years, 54.9% male, and 97.5% White) with mild cognitive impairment (MCI) or mild dementia and increased brain amyloid burden by PiB PET comprised the study sample. Lecanemab trial's inclusion criteria reduced the study sample to 112 (47.3% of 237) participants. The trial's exclusion criteria further narrowed the number of potentially eligible participants to 19 (overall 8% of 237). Modifying the eligibility criteria to include all participants with MCI (instead of applying additional cognitive criteria) resulted in 17.4% of participants with MCI being eligible for lecanemab treatment. One hundred four participants (43.9% of 237) fulfilled the aducanumab clinical trial's inclusion criteria. The aducanumab trial's exclusion criteria further reduced the number of available participants, narrowing those eligible to 12 (5.1% of 237). Common exclusions were related to other chronic conditions and neuroimaging findings. DISCUSSION Findings estimate the limited eligibility in typical older adults with cognitive impairment for anti-β-amyloid mAbs.
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Affiliation(s)
- Rioghna R Pittock
- From the Department of Neurology (R.R.P., V.K.R., D.S.K., R.C.P., J.G.-R.), Mayo Clinic, Rochester, MN; The College (R.R.P.), University of Chicago, IL; Departments of Quantitative Health Sciences (J.A.A., A.M.C., W.K.K., R.C.P., M.V.) and Radiology (C.R.J., P.V., V.J.L.), Mayo Clinic, Rochester, MN
| | - Jeremiah A Aakre
- From the Department of Neurology (R.R.P., V.K.R., D.S.K., R.C.P., J.G.-R.), Mayo Clinic, Rochester, MN; The College (R.R.P.), University of Chicago, IL; Departments of Quantitative Health Sciences (J.A.A., A.M.C., W.K.K., R.C.P., M.V.) and Radiology (C.R.J., P.V., V.J.L.), Mayo Clinic, Rochester, MN
| | - Anna M Castillo
- From the Department of Neurology (R.R.P., V.K.R., D.S.K., R.C.P., J.G.-R.), Mayo Clinic, Rochester, MN; The College (R.R.P.), University of Chicago, IL; Departments of Quantitative Health Sciences (J.A.A., A.M.C., W.K.K., R.C.P., M.V.) and Radiology (C.R.J., P.V., V.J.L.), Mayo Clinic, Rochester, MN
| | - Vijay K Ramanan
- From the Department of Neurology (R.R.P., V.K.R., D.S.K., R.C.P., J.G.-R.), Mayo Clinic, Rochester, MN; The College (R.R.P.), University of Chicago, IL; Departments of Quantitative Health Sciences (J.A.A., A.M.C., W.K.K., R.C.P., M.V.) and Radiology (C.R.J., P.V., V.J.L.), Mayo Clinic, Rochester, MN
| | - Walter K Kremers
- From the Department of Neurology (R.R.P., V.K.R., D.S.K., R.C.P., J.G.-R.), Mayo Clinic, Rochester, MN; The College (R.R.P.), University of Chicago, IL; Departments of Quantitative Health Sciences (J.A.A., A.M.C., W.K.K., R.C.P., M.V.) and Radiology (C.R.J., P.V., V.J.L.), Mayo Clinic, Rochester, MN
| | - Clifford R Jack
- From the Department of Neurology (R.R.P., V.K.R., D.S.K., R.C.P., J.G.-R.), Mayo Clinic, Rochester, MN; The College (R.R.P.), University of Chicago, IL; Departments of Quantitative Health Sciences (J.A.A., A.M.C., W.K.K., R.C.P., M.V.) and Radiology (C.R.J., P.V., V.J.L.), Mayo Clinic, Rochester, MN
| | - Prashanthi Vemuri
- From the Department of Neurology (R.R.P., V.K.R., D.S.K., R.C.P., J.G.-R.), Mayo Clinic, Rochester, MN; The College (R.R.P.), University of Chicago, IL; Departments of Quantitative Health Sciences (J.A.A., A.M.C., W.K.K., R.C.P., M.V.) and Radiology (C.R.J., P.V., V.J.L.), Mayo Clinic, Rochester, MN
| | - Val J Lowe
- From the Department of Neurology (R.R.P., V.K.R., D.S.K., R.C.P., J.G.-R.), Mayo Clinic, Rochester, MN; The College (R.R.P.), University of Chicago, IL; Departments of Quantitative Health Sciences (J.A.A., A.M.C., W.K.K., R.C.P., M.V.) and Radiology (C.R.J., P.V., V.J.L.), Mayo Clinic, Rochester, MN
| | - David S Knopman
- From the Department of Neurology (R.R.P., V.K.R., D.S.K., R.C.P., J.G.-R.), Mayo Clinic, Rochester, MN; The College (R.R.P.), University of Chicago, IL; Departments of Quantitative Health Sciences (J.A.A., A.M.C., W.K.K., R.C.P., M.V.) and Radiology (C.R.J., P.V., V.J.L.), Mayo Clinic, Rochester, MN
| | - Ronald C Petersen
- From the Department of Neurology (R.R.P., V.K.R., D.S.K., R.C.P., J.G.-R.), Mayo Clinic, Rochester, MN; The College (R.R.P.), University of Chicago, IL; Departments of Quantitative Health Sciences (J.A.A., A.M.C., W.K.K., R.C.P., M.V.) and Radiology (C.R.J., P.V., V.J.L.), Mayo Clinic, Rochester, MN
| | - Jonathan Graff-Radford
- From the Department of Neurology (R.R.P., V.K.R., D.S.K., R.C.P., J.G.-R.), Mayo Clinic, Rochester, MN; The College (R.R.P.), University of Chicago, IL; Departments of Quantitative Health Sciences (J.A.A., A.M.C., W.K.K., R.C.P., M.V.) and Radiology (C.R.J., P.V., V.J.L.), Mayo Clinic, Rochester, MN
| | - Maria Vassilaki
- From the Department of Neurology (R.R.P., V.K.R., D.S.K., R.C.P., J.G.-R.), Mayo Clinic, Rochester, MN; The College (R.R.P.), University of Chicago, IL; Departments of Quantitative Health Sciences (J.A.A., A.M.C., W.K.K., R.C.P., M.V.) and Radiology (C.R.J., P.V., V.J.L.), Mayo Clinic, Rochester, MN.
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Eloyan A, Thangarajah M, An N, Borowski BJ, Reddy AL, Aisen P, Dage JL, Foroud T, Ghetti B, Griffin P, Hammers D, Iaccarino L, Jack CR, Kirby K, Kramer J, Koeppe R, Kukull WA, La Joie R, Mundada NS, Murray ME, Nudelman K, Rumbaugh M, Soleimani-Meigooni DN, Toga A, Touroutoglou A, Atri A, Day GS, Duara R, Graff-Radford NR, Honig LS, Jones DT, Masdeu J, Mendez MF, Musiek E, Onyike CU, Rogalski E, Salloway S, Sha S, Turner RS, Wingo TS, Wolk DA, Womack K, Beckett L, Gao S, Carrillo MC, Rabinovici G, Apostolova LG, Dickerson B, Vemuri P. White matter hyperintensities are higher among early-onset Alzheimer's disease participants than their cognitively normal and early-onset nonAD peers: Longitudinal Early-onset Alzheimer's Disease Study (LEADS). Alzheimers Dement 2023; 19 Suppl 9:S89-S97. [PMID: 37491599 PMCID: PMC10808262 DOI: 10.1002/alz.13402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 05/23/2023] [Accepted: 05/24/2023] [Indexed: 07/27/2023]
Abstract
INTRODUCTION We compared white matter hyperintensities (WMHs) in early-onset Alzheimer's disease (EOAD) with cognitively normal (CN) and early-onset amyloid-negative cognitively impaired (EOnonAD) groups in the Longitudinal Early-Onset Alzheimer's Disease Study. METHODS We investigated the role of increased WMH in cognition and amyloid and tau burden. We compared WMH burden of 205 EOAD, 68 EOnonAD, and 89 CN participants in lobar regions using t-tests and analyses of covariance. Linear regression analyses were used to investigate the association between WMH and cognitive impairment and that between amyloid and tau burden. RESULTS EOAD showed greater WMHs compared with CN and EOnonAD participants across all regions with no significant differences between CN and EOnonAD groups. Greater WMHs were associated with worse cognition. Tau burden was positively associated with WMH burden in the EOAD group. DISCUSSION EOAD consistently showed higher WMH volumes. Overall, greater WMHs were associated with worse cognition and higher tau burden in EOAD. HIGHLIGHTS This study represents a comprehensive characterization of WMHs in sporadic EOAD. WMH volumes are associated with tau burden from positron emission tomography (PET) in EOAD, suggesting WMHs are correlated with increasing burden of AD. Greater WMH volumes are associated with worse performance on global cognitive tests. EOAD participants have higher WMH volumes compared with CN and early-onset amyloid-negative cognitively impaired (EOnonAD) groups across all brain regions.
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Affiliation(s)
- Ani Eloyan
- Department of Biostatistics, Center for Statistical Sciences, Brown University, Providence, Rhode Island, USA, 02903
| | - Maryanne Thangarajah
- Department of Biostatistics, Center for Statistical Sciences, Brown University, Providence, Rhode Island, USA, 02903
| | - Na An
- Department of Biostatistics, Center for Statistical Sciences, Brown University, Providence, Rhode Island, USA, 02903
| | - Bret J. Borowski
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA, 55905
| | - Ashritha L. Reddy
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA, 55905
| | - Paul Aisen
- Alzheimer’s Therapeutic Research Institute, University of Southern California, San Diego, California, USA, 92121
| | - Jeffrey L. Dage
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA, 46202
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA, 46202
| | - Tatiana Foroud
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA, 46202
| | - Bernardino Ghetti
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA, 46202
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA, 46202
- Department of Pathology & Laboratory Medicine Indiana University School of Medicine, Indianapolis, Indiana, USA, 02912
| | - Percy Griffin
- Medical & Scientific Relations Division, Alzheimer’s Association, Chicago, Illinois, USA, 60603
| | - Dustin Hammers
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA, 46202
| | - Leonardo Iaccarino
- Department of Neurology, University of California – San Francisco, San Francisco, California, USA, 94143
| | - Clifford R. Jack
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA, 46202
| | - Kala Kirby
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA, 46202
| | - Joel Kramer
- Department of Neurology, University of California – San Francisco, San Francisco, California, USA, 94143
| | - Robert Koeppe
- Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA, 48109
| | - Walter A. Kukull
- Department of Epidemiology, University of Washington, Seattle, Washington, USA, 98195
| | - Renaud La Joie
- Department of Neurology, University of California – San Francisco, San Francisco, California, USA, 94143
| | - Nidhi S Mundada
- Department of Neurology, University of California – San Francisco, San Francisco, California, USA, 94143
| | - Melissa E. Murray
- Department of Neuroscience, Mayo Clinic, Jacksonville, Florida, USA, 32224
| | - Kelly Nudelman
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA, 46202
| | - Malia Rumbaugh
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA, 46202
| | | | - Arthur Toga
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Los Angeles, California, USA, 90033
| | - Alexandra Touroutoglou
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA, 02114
| | - Alireza Atri
- Banner Sun Health Research Institute, Sun City, Arizona, USA, 85351
| | - Gregory S. Day
- Department of Neurology, Mayo Clinic, Jacksonville, Florida, USA, 32224
| | - Ranjan Duara
- Wien Center for Alzheimer’s Disease and Memory Disorders, Mount Sinai Medical Center, Miami, Florida, USA, 33140
| | | | - Lawrence S. Honig
- Taub Institute and Department of Neurology, Columbia University Irving Medical Center, New York, New York, USA,10032
| | - David T. Jones
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA, 55905
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA, 55905
| | - Joseph Masdeu
- Nantz National Alzheimer Center, Houston Methodist and Weill Cornell Medicine, Houston, Texas, USA, 77030
| | - Mario F. Mendez
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, California, USA, 90095
| | - Erik Musiek
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA, 63108
| | - Chiadi U. Onyike
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA, 21205
| | - Emily Rogalski
- Department of Psychiatry and Behavioral Sciences, Mesulam Center for Cognitive Neurology and Alzheimer’s Disease, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA, 60611
| | - Stephen Salloway
- Department of Neurology, Alpert Medical School, Brown University, Providence, Rhode Island, USA, 02912
| | - Sharon Sha
- Department of Neurology & Neurological Sciences, Stanford University, Palo Alto, California, USA, 94304
| | - Raymond S. Turner
- Department of Neurology, Georgetown University, Washington D.C., USA, 20007
| | - Thomas S. Wingo
- Department of Neurology and Human Genetics, Emory University School of Medicine, Atlanta, Georgia, USA, 30322
| | - David A. Wolk
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA, 19104
| | - Kyle Womack
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA, 63108
| | - Laurel Beckett
- Department of Public Health Sciences, University of California – Davis, Davis, California, USA, 95616
| | - Sujuan Gao
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, Indiana, USA, 46202
| | - Maria C. Carrillo
- Medical & Scientific Relations Division, Alzheimer’s Association, Chicago, Illinois, USA, 60603
| | - Gil Rabinovici
- Department of Neurology, University of California – San Francisco, San Francisco, California, USA, 94143
| | - Liana G. Apostolova
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA, 46202
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA, 46202
- Department of Radiology and Imaging Sciences, Center for Neuroimaging, Indiana University School of Medicine Indianapolis, Indianapolis, Indiana, USA, 46202
| | - Brad Dickerson
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA, 02114
| | - Prashanthi Vemuri
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA, 55905
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Pieciak T, París G, Beck D, Maximov II, Tristán-Vega A, de Luis-García R, Westlye LT, Aja-Fernández S. Spherical means-based free-water volume fraction from diffusion MRI increases non-linearly with age in the white matter of the healthy human brain. Neuroimage 2023; 279:120324. [PMID: 37574122 DOI: 10.1016/j.neuroimage.2023.120324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Revised: 08/08/2023] [Accepted: 08/10/2023] [Indexed: 08/15/2023] Open
Abstract
The term free-water volume fraction (FWVF) refers to the signal fraction that could be found as the cerebrospinal fluid of the brain, which has been demonstrated as a sensitive measure that correlates with cognitive performance and various neuropathological processes. It can be quantified by properly fitting the isotropic component of the magnetic resonance (MR) signal in diffusion-sensitized sequences. Using N=287 healthy subjects (178F/109M) aged 25-94, this study examines in detail the evolution of the FWVF obtained with the spherical means technique from multi-shell acquisitions in the human brain white matter across the adult lifespan, which has been previously reported to exhibit a positive trend when estimated from single-shell data using the bi-tensor signal representation. We found evidence of a noticeably non-linear gain after the sixth decade of life, with a region-specific variate and varying change rate of the spherical means-based multi-shell FWVF parameter with age, at the same time, a heteroskedastic pattern across the adult lifespan is suggested. On the other hand, the FW corrected diffusion tensor imaging (DTI) leads to a region-dependent flattened age-related evolution of the mean diffusivity (MD) and fractional anisotropy (FA), along with a considerable reduction in their variability, as compared to the studies conducted over the standard (single-component) DTI. This way, our study provides a new perspective on the trajectory-based assessment of the brain and explains the conceivable reason for the variations observed in FA and MD parameters across the lifespan with previous studies under the standard diffusion tensor imaging.
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Affiliation(s)
- Tomasz Pieciak
- Laboratorio de Procesado de Imagen (LPI), ETSI Telecomunicación, Universidad de Valladolid, Valladolid, Spain.
| | - Guillem París
- Laboratorio de Procesado de Imagen (LPI), ETSI Telecomunicación, Universidad de Valladolid, Valladolid, Spain
| | - Dani Beck
- Department of Psychology, University of Oslo, Oslo, Norway; NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway. https://twitter.com/_DaniBeck
| | - Ivan I Maximov
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway
| | - Antonio Tristán-Vega
- Laboratorio de Procesado de Imagen (LPI), ETSI Telecomunicación, Universidad de Valladolid, Valladolid, Spain
| | - Rodrigo de Luis-García
- Laboratorio de Procesado de Imagen (LPI), ETSI Telecomunicación, Universidad de Valladolid, Valladolid, Spain
| | - Lars T Westlye
- Department of Psychology, University of Oslo, Oslo, Norway; NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway. https://twitter.com/larswestlye
| | - Santiago Aja-Fernández
- Laboratorio de Procesado de Imagen (LPI), ETSI Telecomunicación, Universidad de Valladolid, Valladolid, Spain. https://twitter.com/SantiagoAjaFer1
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Kang DW, Wang SM, Um YH, Kim S, Kim T, Kim D, Lee CU, Lim HK. Impact of transcranial direct current stimulation on white matter microstructure integrity in mild cognitive impairment patients according to effect modifiers as risk factors for Alzheimer's disease. Front Aging Neurosci 2023; 15:1234086. [PMID: 37744398 PMCID: PMC10517264 DOI: 10.3389/fnagi.2023.1234086] [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: 06/03/2023] [Accepted: 08/21/2023] [Indexed: 09/26/2023] Open
Abstract
Background Little research exists on how individual risk factors for Alzheimer's disease (AD) affect the intermediate phenotype after transcranial direct current stimulation (tDCS), despite the importance of precision medicine-based therapeutic approaches. Objective To determine how an application of sequential tDCS (2 mA/day, left dorsolateral prefrontal cortex, 10 sessions) affects changes in white matter (WM) microstructure integrity in 63 mild cognitive impairment (MCI) patients with effect modifiers such as Aβ deposition, APOE ε4 carrier status, BDNF Val66Met polymorphism status, and sex. Methods We examined individual effect modifier-by-tDCS interactions and multiple effect modifiers-by-tDCS interactions for diffusion metrics. We also evaluated the association between baseline Aβ deposition and changes in WM microstructure integrity following tDCS. Results We found that APOE ε4 carrier status and sex had a significant interaction with tDCS, resulting in increased fractional anisotropy (FA) in the right uncinate fasciculus (UF) after stimulation. Additionally, we observed multiple effect modifiers-by-tDCS interactions on WM integrity of the right UF, leading to a more pronounced increase in FA values in APOE ε4 carriers and females with Val66 homozygotes. Finally, baseline Aβ deposition was positively associated with a difference in FA of the left cingulum in the hippocampal area, which showed a positive association with the changes in the score for delayed memory. Conclusion Our study shows the differential impact of individual AD risk factors on changes in the early intermediate phenotype after sequential tDCS in MCI patients. This research emphasizes the importance of precision medicine approaches in tDCS for the prodromal stages of AD.
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Affiliation(s)
- Dong Woo Kang
- Department of Psychiatry, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, 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
| | - Yoo Hyun Um
- Department of Psychiatry, St. Vincent’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Sunghwan Kim
- Department of Psychiatry, Yeouido St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - TaeYeong Kim
- Research Institute, NEUROPHET Inc., Seoul, Republic of Korea
| | - Donghyeon Kim
- 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
| | - 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
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19
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Alsameen MH, Gong Z, Qian W, Kiely M, Triebswetter C, Bergeron CM, Cortina LE, Faulkner ME, Laporte JP, Bouhrara M. C-NODDI: a constrained NODDI model for axonal density and orientation determinations in cerebral white matter. Front Neurol 2023; 14:1205426. [PMID: 37602266 PMCID: PMC10435293 DOI: 10.3389/fneur.2023.1205426] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 07/14/2023] [Indexed: 08/22/2023] Open
Abstract
Purpose Neurite orientation dispersion and density imaging (NODDI) provides measures of neurite density and dispersion through computation of the neurite density index (NDI) and the orientation dispersion index (ODI). However, NODDI overestimates the cerebrospinal fluid water fraction in white matter (WM) and provides physiologically unrealistic high NDI values. Furthermore, derived NDI values are echo-time (TE)-dependent. In this work, we propose a modification of NODDI, named constrained NODDI (C-NODDI), for NDI and ODI mapping in WM. Methods Using NODDI and C-NODDI, we investigated age-related alterations in WM in a cohort of 58 cognitively unimpaired adults. Further, NDI values derived using NODDI or C-NODDI were correlated with the neurofilament light chain (NfL) concentration levels, a plasma biomarker of axonal degeneration. Finally, we investigated the TE dependence of NODDI or C-NODDI derived NDI and ODI. Results ODI derived values using both approaches were virtually identical, exhibiting constant trends with age. Further, our results indicated a quadratic relationship between NDI and age suggesting that axonal maturation continues until middle age followed by a decrease. This quadratic association was notably significant in several WM regions using C-NODDI, while limited to a few regions using NODDI. Further, C-NODDI-NDI values exhibited a stronger correlation with NfL concentration levels as compared to NODDI-NDI, with lower NDI values corresponding to higher levels of NfL. Finally, we confirmed the previous finding that NDI estimation using NODDI was dependent on TE, while NDI derived values using C-NODDI exhibited lower sensitivity to TE in WM. Conclusion C-NODDI provides a complementary method to NODDI for determination of NDI in white matter.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | - Mustapha Bouhrara
- Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, MD, United States
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20
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Carvalho DZ, McCarter SJ, St Louis EK, Przybelski SA, Johnson Sparrman KL, Somers VK, Boeve BF, Petersen RC, Jack CR, Graff-Radford J, Vemuri P. Association of Polysomnographic Sleep Parameters With Neuroimaging Biomarkers of Cerebrovascular Disease in Older Adults With Sleep Apnea. Neurology 2023; 101:e125-e136. [PMID: 37164654 PMCID: PMC10351545 DOI: 10.1212/wnl.0000000000207392] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2022] [Accepted: 03/23/2023] [Indexed: 05/12/2023] Open
Abstract
BACKGROUND AND OBJECTIVES Our objective was to determine whether polysomnographic (PSG) sleep parameters are associated with neuroimaging biomarkers of cerebrovascular disease (CVD) related to white matter (WM) integrity in older adults with obstructive sleep apnea (OSA). METHODS From the population-based Mayo Clinic Study of Aging, we identified participants without dementia who underwent at least 1 brain MRI and PSG. We quantified 2 CVD biomarkers: WM hyperintensities (WMHs) from fluid-attenuated inversion recovery (FLAIR)-MRI, and fractional anisotropy of the genu of the corpus callosum (genu FA) from diffusion MRI. For this cross-sectional analysis, we fit linear models to assess associations between PSG parameters (NREM stage 1 percentage, NREM stage 3 percentage [slow-wave sleep], mean oxyhemoglobin saturation, and log of apnea-hypopnea index [AHI]) and CVD biomarkers (log of WMH and log of genu FA), respectively, while adjusting for age (at MRI), sex, APOE ε4 status, composite cardiovascular and metabolic conditions (CMC) score, REM stage percentage, sleep duration, and interval between MRI and PSG. RESULTS We included 140 participants with FLAIR-MRI (of which 103 had additional diffusion MRI). The mean ± SD age was 72.7 ± 9.6 years at MRI with nearly 60% being men. The absolute median (interquartile range [IQR]) interval between MRI and PSG was 1.74 (0.9-3.2) years. 90.7% were cognitively unimpaired (CU) during both assessments. For every 10-point decrease in N3%, there was a 0.058 (95% CI 0.006-0.111, p = 0.030) increase in the log of WMH and 0.006 decrease (95% CI -0.012 to -0.0002, p = 0.042) in the log of genu FA. After matching for age, sex, and N3%, participants with severe OSA had higher WMH (median [IQR] 0.007 [0.005-0.015] vs 0.006 [0.003-0.009], p = 0.042) and lower genu FA (median [IQR] 0.57 [0.55-0.63] vs 0.63 [0.58-0.65], p = 0.007), when compared with those with mild/moderate OSA. DISCUSSION We found that reduced slow-wave sleep and severe OSA were associated with higher burden of WM abnormalities in predominantly CU older adults, which may contribute to greater risk of cognitive impairment, dementia, and stroke. Our study supports the association between sleep depth/fragmentation and intermittent hypoxia and CVD biomarkers. Longitudinal studies are required to assess causation.
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Affiliation(s)
- Diego Z Carvalho
- From the Department of Neurology (D.Z.C., S.J.M., E.K.S.L., B.F.B., R.C.P., J.G.-R.), Center for Sleep Medicine (D.Z.C., S.J.M., E.K.S.L., B.F.B.), Division of Pulmonary and Critical Care, Department of Internal Medicine, Department of Quantitative Health Sciences (S.A.P., R.C.P.), Department of Radiology (K.L.J.S., C.R.J., P.V.), and Department of Cardiovascular Medicine (V.K.S.), Mayo Clinic, Rochester, MN.
| | - Stuart J McCarter
- From the Department of Neurology (D.Z.C., S.J.M., E.K.S.L., B.F.B., R.C.P., J.G.-R.), Center for Sleep Medicine (D.Z.C., S.J.M., E.K.S.L., B.F.B.), Division of Pulmonary and Critical Care, Department of Internal Medicine, Department of Quantitative Health Sciences (S.A.P., R.C.P.), Department of Radiology (K.L.J.S., C.R.J., P.V.), and Department of Cardiovascular Medicine (V.K.S.), Mayo Clinic, Rochester, MN
| | - Erik K St Louis
- From the Department of Neurology (D.Z.C., S.J.M., E.K.S.L., B.F.B., R.C.P., J.G.-R.), Center for Sleep Medicine (D.Z.C., S.J.M., E.K.S.L., B.F.B.), Division of Pulmonary and Critical Care, Department of Internal Medicine, Department of Quantitative Health Sciences (S.A.P., R.C.P.), Department of Radiology (K.L.J.S., C.R.J., P.V.), and Department of Cardiovascular Medicine (V.K.S.), Mayo Clinic, Rochester, MN
| | - Scott A Przybelski
- From the Department of Neurology (D.Z.C., S.J.M., E.K.S.L., B.F.B., R.C.P., J.G.-R.), Center for Sleep Medicine (D.Z.C., S.J.M., E.K.S.L., B.F.B.), Division of Pulmonary and Critical Care, Department of Internal Medicine, Department of Quantitative Health Sciences (S.A.P., R.C.P.), Department of Radiology (K.L.J.S., C.R.J., P.V.), and Department of Cardiovascular Medicine (V.K.S.), Mayo Clinic, Rochester, MN
| | - Kohl L Johnson Sparrman
- From the Department of Neurology (D.Z.C., S.J.M., E.K.S.L., B.F.B., R.C.P., J.G.-R.), Center for Sleep Medicine (D.Z.C., S.J.M., E.K.S.L., B.F.B.), Division of Pulmonary and Critical Care, Department of Internal Medicine, Department of Quantitative Health Sciences (S.A.P., R.C.P.), Department of Radiology (K.L.J.S., C.R.J., P.V.), and Department of Cardiovascular Medicine (V.K.S.), Mayo Clinic, Rochester, MN
| | - Virend K Somers
- From the Department of Neurology (D.Z.C., S.J.M., E.K.S.L., B.F.B., R.C.P., J.G.-R.), Center for Sleep Medicine (D.Z.C., S.J.M., E.K.S.L., B.F.B.), Division of Pulmonary and Critical Care, Department of Internal Medicine, Department of Quantitative Health Sciences (S.A.P., R.C.P.), Department of Radiology (K.L.J.S., C.R.J., P.V.), and Department of Cardiovascular Medicine (V.K.S.), Mayo Clinic, Rochester, MN
| | - Bradley F Boeve
- From the Department of Neurology (D.Z.C., S.J.M., E.K.S.L., B.F.B., R.C.P., J.G.-R.), Center for Sleep Medicine (D.Z.C., S.J.M., E.K.S.L., B.F.B.), Division of Pulmonary and Critical Care, Department of Internal Medicine, Department of Quantitative Health Sciences (S.A.P., R.C.P.), Department of Radiology (K.L.J.S., C.R.J., P.V.), and Department of Cardiovascular Medicine (V.K.S.), Mayo Clinic, Rochester, MN
| | - Ronald C Petersen
- From the Department of Neurology (D.Z.C., S.J.M., E.K.S.L., B.F.B., R.C.P., J.G.-R.), Center for Sleep Medicine (D.Z.C., S.J.M., E.K.S.L., B.F.B.), Division of Pulmonary and Critical Care, Department of Internal Medicine, Department of Quantitative Health Sciences (S.A.P., R.C.P.), Department of Radiology (K.L.J.S., C.R.J., P.V.), and Department of Cardiovascular Medicine (V.K.S.), Mayo Clinic, Rochester, MN
| | - Clifford R Jack
- From the Department of Neurology (D.Z.C., S.J.M., E.K.S.L., B.F.B., R.C.P., J.G.-R.), Center for Sleep Medicine (D.Z.C., S.J.M., E.K.S.L., B.F.B.), Division of Pulmonary and Critical Care, Department of Internal Medicine, Department of Quantitative Health Sciences (S.A.P., R.C.P.), Department of Radiology (K.L.J.S., C.R.J., P.V.), and Department of Cardiovascular Medicine (V.K.S.), Mayo Clinic, Rochester, MN
| | - Jonathan Graff-Radford
- From the Department of Neurology (D.Z.C., S.J.M., E.K.S.L., B.F.B., R.C.P., J.G.-R.), Center for Sleep Medicine (D.Z.C., S.J.M., E.K.S.L., B.F.B.), Division of Pulmonary and Critical Care, Department of Internal Medicine, Department of Quantitative Health Sciences (S.A.P., R.C.P.), Department of Radiology (K.L.J.S., C.R.J., P.V.), and Department of Cardiovascular Medicine (V.K.S.), Mayo Clinic, Rochester, MN
| | - Prashanthi Vemuri
- From the Department of Neurology (D.Z.C., S.J.M., E.K.S.L., B.F.B., R.C.P., J.G.-R.), Center for Sleep Medicine (D.Z.C., S.J.M., E.K.S.L., B.F.B.), Division of Pulmonary and Critical Care, Department of Internal Medicine, Department of Quantitative Health Sciences (S.A.P., R.C.P.), Department of Radiology (K.L.J.S., C.R.J., P.V.), and Department of Cardiovascular Medicine (V.K.S.), Mayo Clinic, Rochester, MN
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21
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Korbmacher M, de Lange AM, van der Meer D, Beck D, Eikefjord E, Lundervold A, Andreassen OA, Westlye LT, Maximov II. Brain-wide associations between white matter and age highlight the role of fornix microstructure in brain ageing. Hum Brain Mapp 2023; 44:4101-4119. [PMID: 37195079 PMCID: PMC10258541 DOI: 10.1002/hbm.26333] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 04/16/2023] [Accepted: 04/26/2023] [Indexed: 05/18/2023] Open
Abstract
Unveiling the details of white matter (WM) maturation throughout ageing is a fundamental question for understanding the ageing brain. In an extensive comparison of brain age predictions and age-associations of WM features from different diffusion approaches, we analyzed UK Biobank diffusion magnetic resonance imaging (dMRI) data across midlife and older age (N = 35,749, 44.6-82.8 years of age). Conventional and advanced dMRI approaches were consistent in predicting brain age. WM-age associations indicate a steady microstructure degeneration with increasing age from midlife to older ages. Brain age was estimated best when combining diffusion approaches, showing different aspects of WM contributing to brain age. Fornix was found as the central region for brain age predictions across diffusion approaches in complement to forceps minor as another important region. These regions exhibited a general pattern of positive associations with age for intra axonal water fractions, axial, radial diffusivities, and negative relationships with age for mean diffusivities, fractional anisotropy, kurtosis. We encourage the application of multiple dMRI approaches for detailed insights into WM, and the further investigation of fornix and forceps as potential biomarkers of brain age and ageing.
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Affiliation(s)
- Max Korbmacher
- Department of Health and FunctioningWestern Norway University of Applied SciencesBergenNorway
- NORMENT Centre for Psychosis Research, Division of Mental Health and AddictionUniversity of Oslo and Oslo University HospitalOsloNorway
- Mohn Medical Imaging and Visualisation Center (MMIV)BergenNorway
| | - Ann Marie de Lange
- NORMENT Centre for Psychosis Research, Division of Mental Health and AddictionUniversity of Oslo and Oslo University HospitalOsloNorway
- Department of PsychiatryUniversity of OxfordOxfordUK
- LREN, Centre for Research in Neurosciences–Department of Clinical NeurosciencesCHUV and University of LausanneLausanneSwitzerland
| | - Dennis van der Meer
- NORMENT Centre for Psychosis Research, Division of Mental Health and AddictionUniversity of Oslo and Oslo University HospitalOsloNorway
- Faculty of Health, Medicine and Life SciencesMaastricht UniversityMaastrichtNetherlands
| | - Dani Beck
- NORMENT Centre for Psychosis Research, Division of Mental Health and AddictionUniversity of Oslo and Oslo University HospitalOsloNorway
- Department of Psychiatric Research, Diakonhjemmet HospitalOsloNorway
- Department of PsychologyUniversity of OsloOsloNorway
| | - Eli Eikefjord
- Department of Health and FunctioningWestern Norway University of Applied SciencesBergenNorway
- Mohn Medical Imaging and Visualisation Center (MMIV)BergenNorway
| | - Arvid Lundervold
- Department of Health and FunctioningWestern Norway University of Applied SciencesBergenNorway
- Mohn Medical Imaging and Visualisation Center (MMIV)BergenNorway
- Department of RadiologyHaukeland University HospitalBergenNorway
- Department of BiomedicineUniversity of BergenBergenNorway
| | - Ole A. Andreassen
- NORMENT Centre for Psychosis Research, Division of Mental Health and AddictionUniversity of Oslo and Oslo University HospitalOsloNorway
- KG Jebsen Centre for Neurodevelopmental DisordersUniversity of OsloOsloNorway
| | - Lars T. Westlye
- NORMENT Centre for Psychosis Research, Division of Mental Health and AddictionUniversity of Oslo and Oslo University HospitalOsloNorway
- Department of PsychologyUniversity of OsloOsloNorway
- KG Jebsen Centre for Neurodevelopmental DisordersUniversity of OsloOsloNorway
| | - Ivan I. Maximov
- Department of Health and FunctioningWestern Norway University of Applied SciencesBergenNorway
- NORMENT Centre for Psychosis Research, Division of Mental Health and AddictionUniversity of Oslo and Oslo University HospitalOsloNorway
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22
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Tian J, Raghavan S, Reid RI, Przybelski SA, Lesnick TG, Gebre RK, Graff-Radford J, Schwarz CG, Lowe VJ, Kantarci K, Knopman DS, Petersen RC, Jack CR, Vemuri P. White Matter Degeneration Pathways Associated With Tau Deposition in Alzheimer Disease. Neurology 2023; 100:e2269-e2278. [PMID: 37068958 PMCID: PMC10259272 DOI: 10.1212/wnl.0000000000207250] [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: 08/23/2022] [Accepted: 02/16/2023] [Indexed: 04/19/2023] Open
Abstract
BACKGROUND AND OBJECTIVES The dynamics of white matter (WM) changes are understudied in Alzheimer disease (AD). Our goal was to study the association between flortaucipir PET and WM health using neurite orientation dispersion and density imaging (NODDI) and evaluate its association with cognitive performance. Specifically, we focused on NODDI's Neurite Density Index (NDI), which aids in capturing axonal degeneration in WM and has greater specificity than single-shell diffusion MRI methods. METHOD We estimated regional flortaucipir PET standard uptake value ratios (SUVRs) from 3 regions corresponding to Braak stage I, III/IV, and V/VI to capture the spatial distribution pattern of the 3R/4R tau in AD. Then, we evaluated the associations between these measurements and NDIs in 29 candidate WM tracts using Pearson correlation and multiple regression models. RESULTS Based on 223 participants who were amyloid positive (mean age of 78 years and 57.0% male, 119 cognitively unimpaired, 56 mild cognitive impairment, and 48 dementia), the results showed that WM tracts NDI decreased with increasing regional Braak tau SUVRs. Of all the significant WM tracts, the uncinate fasciculus (r = -0.274 for Braak I, -0.311 for Braak III/IV, and -0.292 for Braak V/VI, p < 0.05) and cingulum adjoining hippocampus (r = -0.274, -0.288, -0.233, p < 0.05), both tracts anatomically connected to areas of early tau deposition, were consistently found to be within the top 5 distinguishing WM tracts associated with flortaucipir SUVRs. The increase in tau deposition measurable outside the medial temporal lobes in Braak III-VI was associated with a decrease in NDI in the middle and inferior temporal WM tracts. For cognitive performance, WM NDI had similar coefficients of determination (r 2 = 31%) as regional Braak flortaucipir SUVRs (29%), and together WM NDI and regional Braak flortaucipir SUVRs explained 46% of the variance in cognitive performance. DISCUSSION We found spatially dependent WM degeneration associated with regional flortaucipir SUVRs in Braak stages, suggesting a spatial pattern in WM damage. NDI, a specific marker of axonal density, provides complementary information about disease staging and progression in addition to tau deposition. Measurements of WM changes are important for the mechanistic understanding of multifactorial pathways through which AD causes cognitive dysfunction.
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Affiliation(s)
- Jianqiao Tian
- From the Department of Radiology (J.T., S.R., R.K.G., C.G.S., V.J.L., K.K., C.R.J., P.V.), Mayo Clinic; Mayo Clinic Graduate School of Biomedical Sciences (J.T.); and Department of Information Technology (R.I.R.), Department of Quantitative Health Sciences (S.A.P., T.G.L.), and Department of Neurology (J.G.-R., D.S.K., R.C.P.), Mayo Clinic, Rochester, MN
| | - Sheelakumari Raghavan
- From the Department of Radiology (J.T., S.R., R.K.G., C.G.S., V.J.L., K.K., C.R.J., P.V.), Mayo Clinic; Mayo Clinic Graduate School of Biomedical Sciences (J.T.); and Department of Information Technology (R.I.R.), Department of Quantitative Health Sciences (S.A.P., T.G.L.), and Department of Neurology (J.G.-R., D.S.K., R.C.P.), Mayo Clinic, Rochester, MN
| | - Robert I Reid
- From the Department of Radiology (J.T., S.R., R.K.G., C.G.S., V.J.L., K.K., C.R.J., P.V.), Mayo Clinic; Mayo Clinic Graduate School of Biomedical Sciences (J.T.); and Department of Information Technology (R.I.R.), Department of Quantitative Health Sciences (S.A.P., T.G.L.), and Department of Neurology (J.G.-R., D.S.K., R.C.P.), Mayo Clinic, Rochester, MN
| | - Scott A Przybelski
- From the Department of Radiology (J.T., S.R., R.K.G., C.G.S., V.J.L., K.K., C.R.J., P.V.), Mayo Clinic; Mayo Clinic Graduate School of Biomedical Sciences (J.T.); and Department of Information Technology (R.I.R.), Department of Quantitative Health Sciences (S.A.P., T.G.L.), and Department of Neurology (J.G.-R., D.S.K., R.C.P.), Mayo Clinic, Rochester, MN
| | - Timothy G Lesnick
- From the Department of Radiology (J.T., S.R., R.K.G., C.G.S., V.J.L., K.K., C.R.J., P.V.), Mayo Clinic; Mayo Clinic Graduate School of Biomedical Sciences (J.T.); and Department of Information Technology (R.I.R.), Department of Quantitative Health Sciences (S.A.P., T.G.L.), and Department of Neurology (J.G.-R., D.S.K., R.C.P.), Mayo Clinic, Rochester, MN
| | - Robel K Gebre
- From the Department of Radiology (J.T., S.R., R.K.G., C.G.S., V.J.L., K.K., C.R.J., P.V.), Mayo Clinic; Mayo Clinic Graduate School of Biomedical Sciences (J.T.); and Department of Information Technology (R.I.R.), Department of Quantitative Health Sciences (S.A.P., T.G.L.), and Department of Neurology (J.G.-R., D.S.K., R.C.P.), Mayo Clinic, Rochester, MN
| | - Jonathan Graff-Radford
- From the Department of Radiology (J.T., S.R., R.K.G., C.G.S., V.J.L., K.K., C.R.J., P.V.), Mayo Clinic; Mayo Clinic Graduate School of Biomedical Sciences (J.T.); and Department of Information Technology (R.I.R.), Department of Quantitative Health Sciences (S.A.P., T.G.L.), and Department of Neurology (J.G.-R., D.S.K., R.C.P.), Mayo Clinic, Rochester, MN
| | - Christopher G Schwarz
- From the Department of Radiology (J.T., S.R., R.K.G., C.G.S., V.J.L., K.K., C.R.J., P.V.), Mayo Clinic; Mayo Clinic Graduate School of Biomedical Sciences (J.T.); and Department of Information Technology (R.I.R.), Department of Quantitative Health Sciences (S.A.P., T.G.L.), and Department of Neurology (J.G.-R., D.S.K., R.C.P.), Mayo Clinic, Rochester, MN
| | - Val J Lowe
- From the Department of Radiology (J.T., S.R., R.K.G., C.G.S., V.J.L., K.K., C.R.J., P.V.), Mayo Clinic; Mayo Clinic Graduate School of Biomedical Sciences (J.T.); and Department of Information Technology (R.I.R.), Department of Quantitative Health Sciences (S.A.P., T.G.L.), and Department of Neurology (J.G.-R., D.S.K., R.C.P.), Mayo Clinic, Rochester, MN
| | - Kejal Kantarci
- From the Department of Radiology (J.T., S.R., R.K.G., C.G.S., V.J.L., K.K., C.R.J., P.V.), Mayo Clinic; Mayo Clinic Graduate School of Biomedical Sciences (J.T.); and Department of Information Technology (R.I.R.), Department of Quantitative Health Sciences (S.A.P., T.G.L.), and Department of Neurology (J.G.-R., D.S.K., R.C.P.), Mayo Clinic, Rochester, MN
| | - David S Knopman
- From the Department of Radiology (J.T., S.R., R.K.G., C.G.S., V.J.L., K.K., C.R.J., P.V.), Mayo Clinic; Mayo Clinic Graduate School of Biomedical Sciences (J.T.); and Department of Information Technology (R.I.R.), Department of Quantitative Health Sciences (S.A.P., T.G.L.), and Department of Neurology (J.G.-R., D.S.K., R.C.P.), Mayo Clinic, Rochester, MN
| | - Ronald C Petersen
- From the Department of Radiology (J.T., S.R., R.K.G., C.G.S., V.J.L., K.K., C.R.J., P.V.), Mayo Clinic; Mayo Clinic Graduate School of Biomedical Sciences (J.T.); and Department of Information Technology (R.I.R.), Department of Quantitative Health Sciences (S.A.P., T.G.L.), and Department of Neurology (J.G.-R., D.S.K., R.C.P.), Mayo Clinic, Rochester, MN
| | - Clifford R Jack
- From the Department of Radiology (J.T., S.R., R.K.G., C.G.S., V.J.L., K.K., C.R.J., P.V.), Mayo Clinic; Mayo Clinic Graduate School of Biomedical Sciences (J.T.); and Department of Information Technology (R.I.R.), Department of Quantitative Health Sciences (S.A.P., T.G.L.), and Department of Neurology (J.G.-R., D.S.K., R.C.P.), Mayo Clinic, Rochester, MN
| | - Prashanthi Vemuri
- From the Department of Radiology (J.T., S.R., R.K.G., C.G.S., V.J.L., K.K., C.R.J., P.V.), Mayo Clinic; Mayo Clinic Graduate School of Biomedical Sciences (J.T.); and Department of Information Technology (R.I.R.), Department of Quantitative Health Sciences (S.A.P., T.G.L.), and Department of Neurology (J.G.-R., D.S.K., R.C.P.), Mayo Clinic, Rochester, MN.
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23
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Cogswell PM, Lundt ES, Therneau TM, Mester CT, Wiste HJ, Graff-Radford J, Schwarz CG, Senjem ML, Gunter JL, Reid RI, Przybelski SA, Knopman DS, Vemuri P, Petersen RC, Jack CR. Evidence against a temporal association between cerebrovascular disease and Alzheimer's disease imaging biomarkers. Nat Commun 2023; 14:3097. [PMID: 37248223 PMCID: PMC10226977 DOI: 10.1038/s41467-023-38878-8] [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: 08/30/2022] [Accepted: 05/15/2023] [Indexed: 05/31/2023] Open
Abstract
Whether a relationship exists between cerebrovascular disease and Alzheimer's disease has been a source of controversy. Evaluation of the temporal progression of imaging biomarkers of these disease processes may inform mechanistic associations. We investigate the relationship of disease trajectories of cerebrovascular disease (white matter hyperintensity, WMH, and fractional anisotropy, FA) and Alzheimer's disease (amyloid and tau PET) biomarkers in 2406 Mayo Clinic Study of Aging and Mayo Alzheimer's Disease Research Center participants using accelerated failure time models. The model assumes a common pattern of progression for each biomarker that is shifted earlier or later in time for each individual and represented by a per participant age adjustment. An individual's amyloid and tau PET adjustments show very weak temporal association with WMH and FA adjustments (R = -0.07 to 0.07); early/late amyloid or tau timing explains <1% of the variation in WMH and FA adjustment. Earlier onset of amyloid is associated with earlier onset of tau (R = 0.57, R2 = 32%). These findings support a strong mechanistic relationship between amyloid and tau aggregation, but not between WMH or FA and amyloid or tau PET.
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Affiliation(s)
- Petrice M Cogswell
- Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA.
| | - Emily S Lundt
- Department of Quantitative Health Sciences, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA
| | - Terry M Therneau
- Department of Quantitative Health Sciences, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA
| | - Carly T Mester
- Department of Quantitative Health Sciences, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA
| | - Heather J Wiste
- Department of Quantitative Health Sciences, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA
| | | | | | - Matthew L Senjem
- Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA
- Department of Information Technology, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA
| | - Jeffrey L Gunter
- Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA
| | - Robert I Reid
- Department of Information Technology, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA
| | - Scott A Przybelski
- Department of Quantitative Health Sciences, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA
| | - David S Knopman
- Department of Neurology, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA
| | - Prashanthi Vemuri
- Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA
| | - Ronald C Petersen
- Department of Quantitative Health Sciences, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA
- Department of Neurology, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA
| | - Clifford R Jack
- Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA
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24
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Taraku B, Woods RP, Boucher M, Espinoza R, Jog M, Al-Sharif N, Narr KL, Zavaliangos-Petropulu A. Changes in white matter microstructure following serial ketamine infusions in treatment resistant depression. Hum Brain Mapp 2023; 44:2395-2406. [PMID: 36715291 PMCID: PMC10028677 DOI: 10.1002/hbm.26217] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Revised: 12/30/2022] [Accepted: 01/12/2023] [Indexed: 01/31/2023] Open
Abstract
Ketamine produces fast-acting antidepressant effects in treatment resistant depression (TRD). Though prior studies report ketamine-related changes in brain activity in TRD, understanding of ketamine's effect on white matter (WM) microstructure remains limited. We thus sought to examine WM neuroplasticity and associated clinical improvements following serial ketamine infusion (SKI) in TRD. TRD patients (N = 57, 49.12% female, mean age: 39.9) received four intravenous ketamine infusions (0.5 mg/kg) 2-3 days apart. Diffusion-weighted scans and clinical assessments (Hamilton Depression Rating Scale [HDRS-17]; Snaith Hamilton Pleasure Scale [SHAPS]) were collected at baseline and 24-h after SKI. WM measures including the neurite density index (NDI) and orientation dispersion index (ODI) from the neurite orientation dispersion and density imaging (NODDI) model, and fractional anisotropy (FA) from the diffusion tensor model were compared voxelwise pre- to post-SKI after using Tract-Based Spatial Statistics workflows to align WM tracts across subjects/time. Correlations between change in WM metrics and clinical measures were subsequently assessed. Following SKI, patients showed significant improvements in HDRS-17 (p-value = 1.8 E-17) and SHAPS (p-value = 1.97 E-10). NDI significantly decreased in occipitotemporal WM pathways (p < .05, FWER/TFCE corrected). ΔSHAPS significantly correlated with ΔNDI in the left internal capsule and left superior longitudinal fasciculus (r = -0.614, p-value = 6.24E-09). No significant changes in ODI or FA were observed. SKI leads to significant changes in the microstructural features of neurites within occipitotemporal tracts, and changes in neurite density within tracts connecting the basal ganglia, thalamus, and cortex relate to improvements in anhedonia. NODDI may be more sensitive for detecting ketamine-induced WM changes than DTI.
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Affiliation(s)
- Brandon Taraku
- Department of Neurology, University of California Los Angeles, Los Angeles, California, USA
| | - Roger P Woods
- Department of Neurology, University of California Los Angeles, Los Angeles, California, USA
- Department of Psychiatry and Behavioral Sciences, University of California Los Angeles, Los Angeles, California, USA
| | - Michael Boucher
- Department of Psychiatry and Behavioral Sciences, University of California Los Angeles, Los Angeles, California, USA
| | - Randall Espinoza
- Department of Psychiatry and Behavioral Sciences, University of California Los Angeles, Los Angeles, California, USA
| | - Mayank Jog
- Department of Neurology, University of California Los Angeles, Los Angeles, California, USA
| | - Noor Al-Sharif
- Department of Neurology, University of California Los Angeles, Los Angeles, California, USA
| | - Katherine L Narr
- Department of Neurology, University of California Los Angeles, Los Angeles, California, USA
- Department of Psychiatry and Behavioral Sciences, University of California Los Angeles, Los Angeles, California, USA
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25
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Murdy TJ, Dunn AR, Singh S, Telpoukhovskaia MA, Zhang S, White JK, Kahn I, Febo M, Kaczorowski CC. Leveraging genetic diversity in mice to inform individual differences in brain microstructure and memory. Front Behav Neurosci 2023; 16:1033975. [PMID: 36703722 PMCID: PMC9871587 DOI: 10.3389/fnbeh.2022.1033975] [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: 09/01/2022] [Accepted: 12/08/2022] [Indexed: 01/11/2023] Open
Abstract
In human Alzheimer's disease (AD) patients and AD mouse models, both differential pre-disease brain features and differential disease-associated memory decline are observed, suggesting that certain neurological features may protect against AD-related cognitive decline. The combination of these features is known as brain reserve, and understanding the genetic underpinnings of brain reserve may advance AD treatment in genetically diverse human populations. One potential source of brain reserve is brain microstructure, which is genetically influenced and can be measured with diffusion MRI (dMRI). To investigate variation of dMRI metrics in pre-disease-onset, genetically diverse AD mouse models, we utilized a population of genetically distinct AD mice produced by crossing the 5XFAD transgenic mouse model of AD to 3 inbred strains (C57BL/6J, DBA/2J, FVB/NJ) and two wild-derived strains (CAST/EiJ, WSB/EiJ). At 3 months of age, these mice underwent diffusion magnetic resonance imaging (dMRI) to probe neural microanatomy in 83 regions of interest (ROIs). At 5 months of age, these mice underwent contextual fear conditioning (CFC). Strain had a significant effect on dMRI measures in most ROIs tested, while far fewer effects of sex, sex*strain interactions, or strain*sex*5XFAD genotype interactions were observed. A main effect of 5XFAD genotype was observed in only 1 ROI, suggesting that the 5XFAD transgene does not strongly disrupt neural development or microstructure of mice in early adulthood. Strain also explained the most variance in mouse baseline motor activity and long-term fear memory. Additionally, significant effects of sex and strain*sex interaction were observed on baseline motor activity, and significant strain*sex and sex*5XFAD genotype interactions were observed on long-term memory. We are the first to study the genetic influences of brain microanatomy in genetically diverse AD mice. Thus, we demonstrated that strain is the primary factor influencing brain microstructure in young adult AD mice and that neural development and early adult microstructure are not strongly altered by the 5XFAD transgene. We also demonstrated that strain, sex, and 5XFAD genotype interact to influence memory in genetically diverse adult mice. Our results support the usefulness of the 5XFAD mouse model and convey strong relationships between natural genetic variation, brain microstructure, and memory.
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Affiliation(s)
| | - Amy R. Dunn
- The Jackson Laboratory, Bar Harbor, ME, United States
| | - Surjeet Singh
- The Jackson Laboratory, Bar Harbor, ME, United States
| | | | | | | | - Itamar Kahn
- Department of Neuroscience, Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, United States
| | - Marcelo Febo
- Department of Neuroscience, University of Florida College of Medicine, Gainesville, FL, United States
| | - Catherine C. Kaczorowski
- The Jackson Laboratory, Bar Harbor, ME, United States,*Correspondence: Catherine C. Kaczorowski,
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Beba A, Peterson SM, Brennan PC, O’Byrne J, Machulda MM, Jannetto PI, Vemuri P, Lewallen DG, Kremers HM, Vassilaki M. Correlation of Blood Metal Concentrations with Cognitive Scores and Neuroimaging Findings in Patients with Total Joint Arthroplasty. J Alzheimers Dis 2023; 94:1335-1342. [PMID: 37393495 PMCID: PMC10481381 DOI: 10.3233/jad-221182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/03/2023]
Abstract
Total joint arthroplasty (TJA) implants are composed of metals, ceramics, and/or polyethylene. Studies suggest that the debris released from metal implants may possess neurotoxic properties with reports of neuropsychiatric symptoms and memory deficits, which could be relevant to Alzheimer's disease and related dementias. This exploratory study examined the cross-sectional correlation of blood metal concentrations with cognitive performance and neuroimaging findings in a convenience sample of 113 TJA patients with history of elevated blood metal concentrations of either titanium, cobalt and/or chromium. Associations with neuroimaging measures were observed but not with cognitive scores. Larger studies with longitudinal follow-up are warranted.
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Affiliation(s)
- Alican Beba
- George Washington University, Columbian College of Arts and Sciences, Washington, DC, USA
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | | | - Peter C. Brennan
- Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN, USA
| | - Jamie O’Byrne
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Mary M. Machulda
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
| | - Paul I. Jannetto
- Department of Laboratory Medicine & Pathology, Mayo Clinic, Rochester, MN, USA
| | | | | | - Hilal Maradit Kremers
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
- Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN, USA
| | - Maria Vassilaki
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
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Moody JF, Dean DC, Kecskemeti SR, Blennow K, Zetterberg H, Kollmorgen G, Suridjan I, Wild N, Carlsson CM, Johnson SC, Alexander AL, Bendlin BB. Associations between diffusion MRI microstructure and cerebrospinal fluid markers of Alzheimer's disease pathology and neurodegeneration along the Alzheimer's disease continuum. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2022; 14:e12381. [PMID: 36479018 PMCID: PMC9720004 DOI: 10.1002/dad2.12381] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 09/14/2022] [Accepted: 10/29/2022] [Indexed: 12/07/2022]
Abstract
Introduction White matter (WM) degeneration is a critical component of early Alzheimer's disease (AD) pathophysiology. Diffusion-weighted imaging (DWI) models, including diffusion tensor imaging (DTI), neurite orientation dispersion and density imaging (NODDI), and mean apparent propagator MRI (MAP-MRI), have the potential to identify early neurodegenerative WM changes associated with AD. Methods We imaged 213 (198 cognitively unimpaired) aging adults with DWI and used tract-based spatial statistics to compare 15 DWI metrics of WM microstructure to 9 cerebrospinal fluid (CSF) markers of AD pathology and neurodegeneration treated as continuous variables. Results We found widespread WM injury in AD, as indexed by robust associations between DWI metrics and CSF biomarkers. MAP-MRI had more spatially diffuse relationships with Aβ42/40 and pTau, compared with NODDI and DTI. Discussion Our results suggest that WM degeneration may be more pervasive in AD than is commonly appreciated and that innovative DWI models such as MAP-MRI may provide clinically viable biomarkers of AD-related neurodegeneration in the earliest stages of AD progression.
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Affiliation(s)
- Jason F. Moody
- Wisconsin Alzheimer's Disease Research CenterUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Douglas C. Dean
- Waisman CenterUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
- Department of Medical PhysicsUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
- Department of PediatricsUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | | | - Kaj Blennow
- Department of Psychiatry and NeurochemistryInstitute of Neuroscience and PhysiologySahlgrenska AcademyUniversity of GothenburgMölndalSweden
- Clinical Neurochemistry LaboratorySahlgrenska University HospitalMölndalSweden
| | - Henrik Zetterberg
- Department of Psychiatry and NeurochemistryInstitute of Neuroscience and PhysiologySahlgrenska AcademyUniversity of GothenburgMölndalSweden
- Clinical Neurochemistry LaboratorySahlgrenska University HospitalMölndalSweden
- Department of Neurodegenerative DiseaseUCL Institute of NeurologyLondonUK
- UK Dementia Research InstituteUCLLondonUK
- Hong Kong Center for Neurodegenerative DiseasesHong KongChina
| | | | | | | | - Cynthia M. Carlsson
- Wisconsin Alzheimer's Disease Research CenterUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Sterling C. Johnson
- Wisconsin Alzheimer's Disease Research CenterUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
- Geriatric Research Education and Clinical CenterMiddleton Memorial VA HospitalMadisonWisconsinUSA
| | - Andrew L. Alexander
- Waisman CenterUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
- Department of Medical PhysicsUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
- Department of PsychiatryUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Barbara B. Bendlin
- Wisconsin Alzheimer's Disease Research CenterUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
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28
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Nguyen AT, Kouri N, Labuzan SA, Przybelski SA, Lesnick TG, Raghavan S, Reid RI, Reichard RR, Knopman DS, Petersen RC, Jack CR, Mielke MM, Dickson DW, Graff-Radford J, Murray ME, Vemuri P. Neuropathologic scales of cerebrovascular disease associated with diffusion changes on MRI. Acta Neuropathol 2022; 144:1117-1125. [PMID: 35841412 PMCID: PMC9637622 DOI: 10.1007/s00401-022-02465-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Revised: 06/29/2022] [Accepted: 06/29/2022] [Indexed: 01/26/2023]
Abstract
Summarizing the multiplicity and heterogeneity of cerebrovascular disease (CVD) features into a single measure has been difficult in both neuropathology and imaging studies. The objective of this work was to evaluate the association between neuroimaging surrogates of CVD and two available neuropathologic CVD scales in those with both antemortem imaging CVD measures and postmortem CVD evaluation. Individuals in the Mayo Clinic Study of Aging with MRI scans within 5 years of death (N = 51) were included. Antemortem CVD measures were computed from diffusion MRI (dMRI), FLAIR, and T2* GRE imaging modalities and compared with postmortem neuropathologic findings using Kalaria and Strozyk Scales. Of all the neuroimaging measures, both regional and global dMRI measures were associated with Kalaria and Strozyk Scales (p < 0.05) and modestly correlated with global cognitive performance. The major conclusions from this study were: (i) microstructural white matter injury measurements using dMRI may be meaningful surrogates of neuropathologic CVD scales, because they aid in capturing diffuse (and early) changes to white matter and secondary neurodegeneration due to lesions; (ii) vacuolation in the corpus callosum may be associated with white matter changes measured on antemortem dMRI imaging; (iii) Alzheimer's disease neuropathologic change did not associate with neuropathologic CVD scales; and (iv) future work should be focused on developing better quantitative measures utilizing dMRI to optimally assess CVD-related neuropathologic changes.
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Affiliation(s)
- Aivi T Nguyen
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
| | - Naomi Kouri
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL, 32224, USA
| | - Sydney A Labuzan
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL, 32224, USA
| | - Scott A Przybelski
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Timothy G Lesnick
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Sheelakumari Raghavan
- Department of Radiology, Mayo Clinic and Foundation, 200 First Street SW, Rochester, MN, 55905, USA
| | - Robert I Reid
- Department of Information Technology, Mayo Clinic, Rochester, MN, USA
| | - R Ross Reichard
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
| | | | | | - Clifford R Jack
- Department of Radiology, Mayo Clinic and Foundation, 200 First Street SW, Rochester, MN, 55905, USA
| | - Michelle M Mielke
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Dennis W Dickson
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL, 32224, USA
| | | | - Melissa E Murray
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL, 32224, USA.
| | - Prashanthi Vemuri
- Department of Radiology, Mayo Clinic and Foundation, 200 First Street SW, Rochester, MN, 55905, USA.
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Neumane S, Gondova A, Leprince Y, Hertz-Pannier L, Arichi T, Dubois J. Early structural connectivity within the sensorimotor network: Deviations related to prematurity and association to neurodevelopmental outcome. Front Neurosci 2022; 16:932386. [PMID: 36507362 PMCID: PMC9732267 DOI: 10.3389/fnins.2022.932386] [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: 04/29/2022] [Accepted: 11/10/2022] [Indexed: 11/27/2022] Open
Abstract
Consisting of distributed and interconnected structures that interact through cortico-cortical connections and cortico-subcortical loops, the sensorimotor (SM) network undergoes rapid maturation during the perinatal period and is thus particularly vulnerable to preterm birth. However, the impact of prematurity on the development and integrity of the emerging SM connections and their relationship to later motor and global impairments are still poorly understood. In this study we aimed to explore to which extent the early microstructural maturation of SM white matter (WM) connections at term-equivalent age (TEA) is modulated by prematurity and related with neurodevelopmental outcome at 18 months corrected age. We analyzed 118 diffusion MRI datasets from the developing Human Connectome Project (dHCP) database: 59 preterm (PT) low-risk infants scanned near TEA and a control group of full-term (FT) neonates paired for age at MRI and sex. We delineated WM connections between the primary SM cortices (S1, M1 and paracentral region) and subcortical structures using probabilistic tractography, and evaluated their microstructure with diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) models. To go beyond tract-specific univariate analyses, we computed a maturational distance related to prematurity based on the multi-parametric Mahalanobis distance of each PT infant relative to the FT group. Our results confirmed the presence of microstructural differences in SM tracts between PT and FT infants, with effects increasing with lower gestational age at birth. Maturational distance analyses highlighted that prematurity has a differential effect on SM tracts with higher distances and thus impact on (i) cortico-cortical than cortico-subcortical connections; (ii) projections involving S1 than M1 and paracentral region; and (iii) the most rostral cortico-subcortical tracts, involving the lenticular nucleus. These different alterations at TEA suggested that vulnerability follows a specific pattern coherent with the established WM caudo-rostral progression of maturation. Finally, we highlighted some relationships between NODDI-derived maturational distances of specific tracts and fine motor and cognitive outcomes at 18 months. As a whole, our results expand understanding of the significant impact of premature birth and early alterations on the emerging SM network even in low-risk infants, with possible relationship with neurodevelopmental outcomes. This encourages further exploration of these potential neuroimaging markers for prediction of neurodevelopmental disorders, with special interest for subtle neuromotor impairments frequently observed in preterm-born children.
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Affiliation(s)
- Sara Neumane
- Inserm, NeuroDiderot, Université Paris Cité, Paris, France
- CEA, NeuroSpin UNIACT, Université Paris-Saclay, Paris, France
- School of Biomedical Engineering and Imaging Sciences, Centre for the Developing Brain, King’s College London, London, United Kingdom
| | - Andrea Gondova
- Inserm, NeuroDiderot, Université Paris Cité, Paris, France
- CEA, NeuroSpin UNIACT, Université Paris-Saclay, Paris, France
| | - Yann Leprince
- CEA, NeuroSpin UNIACT, Université Paris-Saclay, Paris, France
| | - Lucie Hertz-Pannier
- Inserm, NeuroDiderot, Université Paris Cité, Paris, France
- CEA, NeuroSpin UNIACT, Université Paris-Saclay, Paris, France
| | - Tomoki Arichi
- School of Biomedical Engineering and Imaging Sciences, Centre for the Developing Brain, King’s College London, London, United Kingdom
- Paediatric Neurosciences, Evelina London Children’s Hospital, Guy’s and St Thomas’ NHS Foundation Trust, London, United Kingdom
| | - Jessica Dubois
- Inserm, NeuroDiderot, Université Paris Cité, Paris, France
- CEA, NeuroSpin UNIACT, Université Paris-Saclay, Paris, France
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30
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Chen J, Ge A, Zhou Y, Ma Y, Zhong S, Chen C, Shi W, Ding J, Wang X. White matter integrity mediates the associations between white matter hyperintensities and cognitive function in patients with silent cerebrovascular diseases. CNS Neurosci Ther 2022; 29:412-428. [PMID: 36415139 PMCID: PMC9804066 DOI: 10.1111/cns.14015] [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: 03/21/2022] [Revised: 10/18/2022] [Accepted: 10/19/2022] [Indexed: 11/24/2022] Open
Abstract
OBJECTIVE To evaluate the relationships between cognitive function and white matter hyperintensity volume (WMHV) in patients with silent cerebrovascular disease and to investigate whether white matter integrity or brain atrophy play a role in this association. METHODS Automated Fiber Quantification and Voxel- based morphometry were used to track and identify the integrity of 20 well-defined white matter tracts and to measure the gray matter volume (GMV). A linear regression model was applied for examining the associations between cognitive function and WMHV and mediation analysis was used to identify the roles of white matter integrity or GMV in the influence of WMHV on cognitive function. RESULTS Two hundred and thirty-six individuals were included for analysis. Executive function was linearly associated with fractional anisotropy (FA) of the right interior frontal occipital fasciculus (IFOF) (β = 0.193; 95% CI, 0.126 to 1.218) and with WMHV (β = -0.188; 95% CI, -0.372 to -0.037). Information processing speed was linearly associated with WMHV (β = -0.357; 95% CI, -0.643 to -0.245), FA of the right anterior thalamic radiation (ATR) (β = 0.207; 95% CI, 0.116 to 0.920), and FA of the left superior longitudinal fasciculus (SLF) (β = 0.177; 95% CI, 0.103 to 1.315). The relationship between WMHV and executive function was mediated by FA of the right IFOF (effect size = -0.045, 95% CI, -0.015 to -0.092). Parallel mediation analysis showed that the association between WMHV and information processing speed was mediated by FA of the right ATR (effect size = -0.099, 95% CI, -0.198 to -0.038) and FA of the left SLF (effect size = -0.038, 95% CI, -0.080 to -0.003). CONCLUSION These findings suggest a mechanism by which WMH affects executive function and information processing speed by impairing white matter integrity. This may be helpful in providing a theoretical basis for rehabilitation strategies of cognitive function in patients with silent cerebrovascular diseases.
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Affiliation(s)
- Jing Chen
- Department of Neurology, Zhongshan HospitalFudan UniversityShanghaiChina
| | - Anyan Ge
- Department of Neurology, Zhongshan HospitalFudan UniversityShanghaiChina
| | - Ying Zhou
- Department of Neurology, XiaMen Branch, Zhongshan HospitalFudan UniversityShanghaiChina
| | - Yuanyuan Ma
- Department of Neurology, Zhongshan HospitalFudan UniversityShanghaiChina
| | - Shaoping Zhong
- Department of Neurology, Zhongshan HospitalFudan UniversityShanghaiChina
| | - Caizhong Chen
- Department of Radiology, Zhongshan HospitalFudan UniversityShanghaiChina
| | - Weibin Shi
- Health Examination Center, Zhongshan HospitalFudan UniversityShanghaiChina
| | - Jing Ding
- Department of Neurology, Zhongshan HospitalFudan UniversityShanghaiChina
| | - Xin Wang
- Department of Neurology, Zhongshan HospitalFudan UniversityShanghaiChina,Department of the State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science,Institutes of Brain ScienceFudan UniversityShanghaiChina
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31
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Ruiz-Rizzo AL, Viviano RP, Daugherty AM, Finke K, Müller HJ, Damoiseaux JS. Subjective cognitive decline predicts lower cingulo-opercular network functional connectivity in individuals with lower neurite density in the forceps minor: Cingulo-opercular network in SCD. Neuroimage 2022; 263:119662. [PMID: 36198354 DOI: 10.1016/j.neuroimage.2022.119662] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 09/19/2022] [Accepted: 10/01/2022] [Indexed: 11/18/2022] Open
Abstract
Cognitive complaints of attention/concentration problems are highly frequent in older adults with subjective cognitive decline (SCD). Functional connectivity in the cingulo-opercular network (CON-FC) supports cognitive control, tonic alertness, and visual processing speed. Thus, those complaints in SCD may reflect a decrease in CON-FC. Frontal white-matter tracts such as the forceps minor exhibit age- and SCD-related alterations and, therefore, might influence the CON-FC decrease in SCD. Here, we aimed to determine whether SCD predicts an impairment in CON-FC and whether neurite density in the forceps minor modulates that effect. To do so, we integrated cross-sectional and longitudinal analyses of multimodal data in a latent growth curve modeling approach. Sixty-nine healthy older adults (13 males; 68.33 ± 7.95 years old) underwent resting-state functional and diffusion-weighted magnetic resonance imaging, and the degree of SCD was assessed at baseline with the memory functioning questionnaire (greater score indicating more SCD). Forty-nine of the participants were further enrolled in two follow-ups, each about 18 months apart. Baseline SCD did not predict CON-FC after three years or its rate of change (p-values > 0.092). Notably, however, the forceps minor neurite density did modulate the relation between SCD and CON-FC (intercept; b = 0.21, 95% confidence interval, CI, [0.03, 0.39], p = 0.021), so that SCD predicted a greater CON-FC decrease in older adults with relatively lower neurite density in the forceps minor. The neurite density of the forceps minor, in turn, negatively correlated with age. These results suggest that CON-FC alterations in SCD are dependent upon the forceps minor neurite density. Accordingly, these results imply modifiable age-related factors that could help delay or mitigate both age and SCD-related effects on brain connectivity.
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Affiliation(s)
- Adriana L Ruiz-Rizzo
- Department of Psychology, General and Experimental Psychology Unit, LMU Munich, Munich 80802, Germany; Department of Neurology, Jena University Hospital, Am Klinikum 1, Jena 07747, Germany.
| | - Raymond P Viviano
- Department of Psychology and Institute of Gerontology, Wayne State University, Detroit, MI 48202, USA
| | - Ana M Daugherty
- Department of Psychology and Institute of Gerontology, Wayne State University, Detroit, MI 48202, USA
| | - Kathrin Finke
- Department of Psychology, General and Experimental Psychology Unit, LMU Munich, Munich 80802, Germany; Department of Neurology, Jena University Hospital, Am Klinikum 1, Jena 07747, Germany
| | - Hermann J Müller
- Department of Psychology, General and Experimental Psychology Unit, LMU Munich, Munich 80802, Germany
| | - Jessica S Damoiseaux
- Department of Psychology and Institute of Gerontology, Wayne State University, Detroit, MI 48202, USA
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32
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Sun Y, Hu Y, Qiu Y, Zhang Y, Jiang C, Lu P, Xu Q, Shi Y, Wei H, Zhou Y. Characterization of white matter over 1–2 years in small vessel disease using MR-based quantitative susceptibility mapping and free-water mapping. Front Aging Neurosci 2022; 14:998051. [PMID: 36247993 PMCID: PMC9562046 DOI: 10.3389/fnagi.2022.998051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 09/07/2022] [Indexed: 11/13/2022] Open
Abstract
PurposeThe aim of this study was to investigate alterations in white matter lesions (WMLs) and normal-appearing white matter (NAWM) with small vessel disease (SVD) over 1–2 years using quantitative susceptibility mapping (QSM) and free-water (FW) mapping.MethodsFifty-one SVD patients underwent MRI brain scans and neuropsychological testing both at baseline and follow-up. The main approach for treating these patients is the management of risk factors. Quantitative susceptibility (QS), fractional anisotropy (FA), mean diffusivity (MD), FW, FW-corrected FA (FAT), and FW-corrected MD (MDT) maps within WMLs and NAWM were generated. Furthermore, the JHU-ICBM-DTI label atlas was used as an anatomic guide, and the measurements of the segmented NAWMs were calculated. The average regional values were extracted, and a paired t-test was used to analyze the longitudinal change. Partial correlations were used to assess the relationship between the MRI indices changes (e.g., ΔQSfollowup − baseline/QSbaseline) and the cognitive function changes (e.g., ΔMoCAfollowup − baseline/MoCAbaseline).ResultsAfter SVD risk factor control, no gradual cognitive decline occurred during 1–2 years. However, we still found that the QS values (index of demyelination) increased in the NAWM at follow-up, especially in the NAWM part of the left superior frontal blade (SF), left occipital blade, right uncinate fasciculus, and right corticospinal tract (CST). FW (index of neuroinflammation/edema) analysis revealed that the follow-up group differed from the baseline group in the NAWM part of the right CST and inferior frontal blade (IF). Decreased FAT (index of axonal loss) was observed in the NAWM part of the right SF and IF at follow-up. In addition, the FAT changes in the NAWM part of the right IF were associated with overall cognitive performance changes. In contrast, no significant differences were found in the WMLs.ConclusionThe NAWM was still in the progressive injury process over time, while WMLs remained relatively stable, which supports the notion that SVD is a chronic progressive disease. The process of axonal loss in the NAWM part of the prefrontal lobe might be a biomarker of cognitive changes in the evolution of SVD.
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Affiliation(s)
- Yawen Sun
- Department of Radiology, School of Medicine, Ren Ji Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Ying Hu
- Department of Radiology, School of Medicine, Ren Ji Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Yage Qiu
- Department of Radiology, School of Medicine, Ren Ji Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Yuyao Zhang
- School of Information and Science and Technology, ShanghaiTech University, Shanghai, China
| | - Changhao Jiang
- School of Information and Science and Technology, ShanghaiTech University, Shanghai, China
| | - Peiwen Lu
- Department of Neurology, School of Medicine, Ren Ji Hospital, Shanghai Jiao Tong University, Shanghai, China
- Ren Ji-UNSW CHeBA Neurocognitive Center, School of Medicine, Ren Ji Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Qun Xu
- Department of Neurology, School of Medicine, Ren Ji Hospital, Shanghai Jiao Tong University, Shanghai, China
- Ren Ji-UNSW CHeBA Neurocognitive Center, School of Medicine, Ren Ji Hospital, Shanghai Jiao Tong University, Shanghai, China
- Department of Health Manage Center, School of Medicine, Ren Ji Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Yuting Shi
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Hongjiang Wei
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
- *Correspondence: Yan Zhou
| | - Yan Zhou
- Department of Radiology, School of Medicine, Ren Ji Hospital, Shanghai Jiao Tong University, Shanghai, China
- Hongjiang Wei
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Raghavan S, Przybelski SA, Reid RI, Lesnick TG, Ramanan VK, Botha H, Matchett BJ, Murray ME, Reichard RR, Knopman DS, Graff-Radford J, Jones DT, Lowe VJ, Mielke MM, Machulda MM, Petersen RC, Kantarci K, Whitwell JL, Josephs KA, Jack CR, Vemuri P. White matter damage due to vascular, tau, and TDP-43 pathologies and its relevance to cognition. Acta Neuropathol Commun 2022; 10:16. [PMID: 35123591 PMCID: PMC8817561 DOI: 10.1186/s40478-022-01319-6] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 01/19/2022] [Indexed: 12/27/2022] Open
Abstract
Multi-compartment modelling of white matter microstructure using Neurite Orientation Dispersion and Density Imaging (NODDI) can provide information on white matter health through neurite density index and free water measures. We hypothesized that cerebrovascular disease, Alzheimer's disease, and TDP-43 proteinopathy would be associated with distinct NODDI readouts of white matter damage which would be informative for identifying the substrate for cognitive impairment. We identified two independent cohorts with multi-shell diffusion MRI, amyloid and tau PET, and cognitive assessments: specifically, a population-based cohort of 347 elderly randomly sampled from the Olmsted county, Minnesota, population and a clinical research-based cohort of 61 amyloid positive Alzheimer's dementia participants. We observed an increase in free water and decrease in neurite density using NODDI measures in the genu of the corpus callosum associated with vascular risk factors, which we refer to as the vascular white matter component. Tau PET signal reflective of 3R/4R tau deposition was associated with worsening neurite density index in the temporal white matter where we measured parahippocampal cingulum and inferior temporal white matter bundles. Worsening temporal white matter neurite density was associated with (antemortem confirmed) FDG TDP-43 signature. Post-mortem neuropathologic data on a small subset of this sample lend support to our findings. In the community-dwelling cohort where vascular disease was more prevalent, the NODDI vascular white matter component explained variability in global cognition (partial R2 of free water and neurite density = 8.3%) and MMSE performance (8.2%) which was comparable to amyloid PET (7.4% for global cognition and 6.6% for memory). In the AD dementia cohort, tau deposition was the greatest contributor to cognitive performance (9.6%), but there was also a non-trivial contribution of the temporal white matter component (8.5%) to cognitive performance. The differences observed between the two cohorts were reflective of their distinct clinical composition. White matter microstructural damage assessed using advanced diffusion models may add significant value for distinguishing the underlying substrate (whether cerebrovascular disease versus neurodegenerative disease caused by tau deposition or TDP-43 pathology) for cognitive impairment in older adults.
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Affiliation(s)
| | - Scott A. Przybelski
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN 55905 USA
| | - Robert I. Reid
- Department of Information Technology, Mayo Clinic, Rochester, MN 55905 USA
| | - Timothy G. Lesnick
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN 55905 USA
| | | | - Hugo Botha
- Department of Neurology, Mayo Clinic, Rochester, MN 55905 USA
| | | | | | - R. Ross Reichard
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55905 USA
| | | | | | - David T. Jones
- Department of Neurology, Mayo Clinic, Rochester, MN 55905 USA
| | - Val J. Lowe
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905 USA
| | - Michelle M. Mielke
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN 55905 USA
- Department of Neurology, Mayo Clinic, Rochester, MN 55905 USA
| | - Mary M. Machulda
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN 55905 USA
| | | | - Kejal Kantarci
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905 USA
| | - Jennifer L. Whitwell
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905 USA
| | | | - Clifford R. Jack
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905 USA
| | - Prashanthi Vemuri
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905 USA
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Saboo KV, Hu C, Varatharajah Y, Przybelski SA, Reid RI, Schwarz CG, Graff-Radford J, Knopman DS, Machulda MM, Mielke MM, Petersen RC, Arnold PM, Worrell GA, Jones DT, Jack Jr CR, Iyer RK, Vemuri P. Deep learning identifies brain structures that predict cognition and explain heterogeneity in cognitive aging. Neuroimage 2022; 251:119020. [PMID: 35196565 PMCID: PMC9045384 DOI: 10.1016/j.neuroimage.2022.119020] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Revised: 01/20/2022] [Accepted: 02/17/2022] [Indexed: 12/02/2022] Open
Abstract
Specific brain structures (gray matter regions and white matter tracts) play a dominant role in determining cognitive decline and explain the heterogeneity in cognitive aging. Identification of these structures is crucial for screening of older adults at risk of cognitive decline. Using deep learning models augmented with a model-interpretation technique on data from 1432 Mayo Clinic Study of Aging participants, we identified a subset of brain structures that were most predictive of individualized cognitive trajectories and indicative of cognitively resilient vs. vulnerable individuals. Specifically, these structures explained why some participants were resilient to the deleterious effects of elevated brain amyloid and poor vascular health. Of these, medial temporal lobe and fornix, reflective of age and pathology-related degeneration, and corpus callosum, reflective of inter-hemispheric disconnection, accounted for 60% of the heterogeneity explained by the most predictive structures. Our results are valuable for identifying cognitively vulnerable individuals and for developing interventions for cognitive decline.
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Schindler LS, Subramaniapillai S, Barth C, van der Meer D, Pedersen ML, Kaufmann T, Maximov II, Linge J, Leinhard OD, Beck D, Gurholt TP, Voldsbekk I, Suri S, Ebmeier KP, Draganski B, Andreassen OA, Westlye LT, de Lange AMG. Associations between abdominal adipose tissue, reproductive span, and brain characteristics in post-menopausal women. Neuroimage Clin 2022; 36:103239. [PMID: 36451350 PMCID: PMC9668664 DOI: 10.1016/j.nicl.2022.103239] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 10/06/2022] [Accepted: 10/17/2022] [Indexed: 11/06/2022]
Abstract
The menopause transition involves changes in oestrogens and adipose tissue distribution, which may influence female brain health post-menopause. Although increased central fat accumulation is linked to risk of cardiometabolic diseases, adipose tissue also serves as the primary biosynthesis site of oestrogens post-menopause. It is unclear whether different types of adipose tissue play diverging roles in female brain health post-menopause, and whether this depends on lifetime oestrogen exposure, which can have lasting effects on the brain and body even after menopause. Using the UK Biobank sample, we investigated associations between brain characteristics and visceral adipose tissue (VAT) and abdominal subcutaneous adipose tissue (ASAT) in 10,251 post-menopausal females, and assessed whether the relationships varied depending on length of reproductive span (age at menarche to age at menopause). To parse the effects of common genetic variation, we computed polygenic scores for reproductive span. The results showed that higher VAT and ASAT were both associated with higher grey and white matter brain age, and greater white matter hyperintensity load. The associations varied positively with reproductive span, indicating more prominent associations between adipose tissue and brain measures in females with a longer reproductive span. The effects were in general small, but could not be fully explained by genetic variation or relevant confounders. Our findings indicate that associations between abdominal adipose tissue and brain health post-menopause may partly depend on individual differences in cumulative oestrogen exposure during reproductive years, emphasising the complexity of neural and endocrine ageing processes in females.
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Affiliation(s)
- Louise S Schindler
- LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland; Department of Psychology, University of Oslo, Oslo, Norway.
| | - Sivaniya Subramaniapillai
- LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland; Department of Psychology, University of Oslo, Oslo, Norway
| | - Claudia Barth
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Dennis van der Meer
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; School of Mental Health and Neuroscience, Faculty of Health Medicine and Life Sciences, Maastricht University, The Netherlands
| | - Mads L Pedersen
- Department of Psychology, University of Oslo, Oslo, Norway; NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Tobias Kaufmann
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychiatry and Psychotherapy, University of Tübingen, Germany
| | - Ivan I Maximov
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway
| | - Jennifer Linge
- AMRA Medical AB, Linköping, Sweden; Department of Health, Medicine, and Caring Sciences, Linköping University, Linköping, Sweden
| | - Olof Dahlqvist Leinhard
- AMRA Medical AB, Linköping, Sweden; Department of Health, Medicine, and Caring Sciences, Linköping University, Linköping, Sweden; Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
| | - Dani Beck
- Department of Psychology, University of Oslo, Oslo, Norway; NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Tiril P Gurholt
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Irene Voldsbekk
- Department of Psychology, University of Oslo, Oslo, Norway; NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Sana Suri
- Department of Psychiatry, University of Oxford, Oxford, UK; Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | | | - Bogdan Draganski
- LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland; Dept. of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Ole A Andreassen
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Lars T Westlye
- Department of Psychology, University of Oslo, Oslo, Norway; NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Ann-Marie G de Lange
- LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland; Department of Psychology, University of Oslo, Oslo, Norway; Department of Psychiatry, University of Oxford, Oxford, UK
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Schwarz CG, Knopman DS, Ramanan VK, Lowe VJ, Wiste HJ, Cogswell PM, Utianski RL, Senjem ML, Gunter JR, Vemuri P, Petersen RC, Jack CR. Longitudinally Increasing Elevated Asymmetric Flortaucipir Binding in a Cognitively Unimpaired Amyloid-Negative Older Individual. J Alzheimers Dis 2021; 85:59-64. [PMID: 34776445 PMCID: PMC8842786 DOI: 10.3233/jad-215052] [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] [Indexed: 11/30/2022]
Abstract
We present the case of a cognitively unimpaired 77-year-old man with elevated, asymmetric, and longitudinally increasing Flortaucipir tau PET despite normal (visually negative) amyloid PET. His atypical tau PET signal persisted and globally increased in a follow-up scan five years later. Across eight years of observations, temporoparietal atrophy was observed consistent with tau PET patterns, but he retained the cognitively unimpaired classification. Altogether, his atypical tau PET signal is not explained by any known risk factors or alternative pathologies, and other imaging findings were not remarkable. He remains enrolled for further observation.
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Affiliation(s)
| | | | | | - Val J Lowe
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - Heather J Wiste
- Department of Quantitative Health Sciences, Division of Clinical Trials and Biostatistics, Mayo Clinic, Rochester, MN, USA
| | | | | | - Matthew L Senjem
- Department of Radiology, Mayo Clinic, Rochester, MN, USA.,Department of Information Technology, Mayo Clinic, Rochester, MN, USA
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