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Fili M, Mohammadiarvejeh P, Hu G, Willette AA. Decoding cognitive aging: how white matter tracts and demographics distinguish potential Super-Agers. GeroScience 2025:10.1007/s11357-025-01566-0. [PMID: 40009298 DOI: 10.1007/s11357-025-01566-0] [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: 12/10/2024] [Accepted: 02/10/2025] [Indexed: 02/27/2025] Open
Abstract
Most adults experience age-related cognitive decline. However, "Positive-Agers" exhibit superior cognition compared to their age-matched peers. Distinguishing between those with superior cognitive performance and those with cognitive decline over time could better inform treatment therapies in older adults. We developed an algorithm called Optimal Cognitive Scoring (OptiCS) that accurately differentiates "Positive-Agers" from "Cognitive Decliners." This study draws on a cohort of 5797 participants longitudinally enrolled in the UK Biobank. Using a predictive pipeline, OptiCS could strongly differentiate Positive-Agers versus Cognitive Decliners (area under the curve, or AUC of 83%). The top diffusion MRI attributes highlighted tracts implicated in pathological aging, including the fornix from the hippocampus, the tapetum from the splenium of the corpus callosum, and other key tracts. This study provides three key insights: (I) The proposed algorithm offers a robust cognitive scoring system for subtle cognitive changes, (II) OptiCS can use diffusion MRI to accurately gauge cognitive performance, and (III) OptiCS provides a predictive framework for early detection of cognitive decline.
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Affiliation(s)
- Mohammad Fili
- School of Industrial Engineering and Management, Oklahoma State University, Stillwater, OK, 74078, USA.
| | - Parvin Mohammadiarvejeh
- Institute for Health Computing, University of Maryland Medical System, Linthicum, MD, 21090, USA
| | - Guiping Hu
- School of Industrial Engineering and Management, Oklahoma State University, Stillwater, OK, 74078, USA
| | - Auriel A Willette
- Department of Neurology, Rutgers University, New Brunswick, NJ, 08901, USA
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Peterson A, Sathe A, Zaras D, Yang Y, Durant A, Deters KD, Shashikumar N, Pechman KR, Kim ME, Gao C, Mohd Khairi N, Li Z, Yao T, Huo Y, Dumitrescu L, Gifford KA, Wilson JE, Cambronero FE, Risacher SL, Beason‐Held LL, An Y, Arfanakis K, Erus G, Davatzikos C, Tosun D, Toga AW, Thompson PM, Mormino EC, Habes M, Wang D, Zhang P, Schilling K, Albert M, Kukull W, Biber SA, Landman BA, Johnson SC, Schneider J, Barnes LL, Bennett DA, Jefferson AL, Resnick SM, Saykin AJ, Hohman TJ, Archer DB. Sex and APOE ε4 allele differences in longitudinal white matter microstructure in multiple cohorts of aging and Alzheimer's disease. Alzheimers Dement 2025; 21:e14343. [PMID: 39711105 PMCID: PMC11781133 DOI: 10.1002/alz.14343] [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: 06/18/2024] [Revised: 09/08/2024] [Accepted: 09/27/2024] [Indexed: 12/24/2024]
Abstract
INTRODUCTION The effects of sex and apolipoprotein E (APOE)-Alzheimer's disease (AD) risk factors-on white matter microstructure are not well characterized. METHODS Diffusion magnetic resonance imaging data from nine well-established longitudinal cohorts of aging were free water (FW)-corrected and harmonized. This dataset included 4741 participants (age = 73.06 ± 9.75) with 9671 imaging sessions over time. FW and FW-corrected fractional anisotropy (FAFWcorr) were used to assess differences in white matter microstructure by sex and APOE ε4 carrier status. RESULTS Sex differences in FAFWcorr in projection tracts and APOE ε4 differences in FW limbic and occipital transcallosal tracts were most pronounced. DISCUSSION There are prominent differences in white matter microstructure by sex and APOE ε4 carrier status. This work adds to our understanding of disparities in AD. Additional work to understand the etiology of these differences is warranted. HIGHLIGHTS Sex and apolipoprotein E (APOE) ε4 carrier status relate to white matter microstructural integrity. Females generally have lower free water-corrected fractional anisotropy compared to males. APOE ε4 carriers tended to have higher free water than non-carriers.
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Affiliation(s)
- Amalia Peterson
- Vanderbilt Memory and Alzheimer's CenterVanderbilt University School of MedicineNashvilleTennesseeUSA
- Department of NeurologyVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - Aditi Sathe
- Vanderbilt Memory and Alzheimer's CenterVanderbilt University School of MedicineNashvilleTennesseeUSA
| | - Dimitrios Zaras
- Vanderbilt Memory and Alzheimer's CenterVanderbilt University School of MedicineNashvilleTennesseeUSA
| | - Yisu Yang
- Vanderbilt Memory and Alzheimer's CenterVanderbilt University School of MedicineNashvilleTennesseeUSA
| | - Alaina Durant
- Vanderbilt Memory and Alzheimer's CenterVanderbilt University School of MedicineNashvilleTennesseeUSA
| | - Kacie D. Deters
- Department of Integrative Biology and PhysiologyUniversity of CaliforniaLos AngelesCaliforniaUSA
| | - Niranjana Shashikumar
- Vanderbilt Memory and Alzheimer's CenterVanderbilt University School of MedicineNashvilleTennesseeUSA
| | - Kimberly R. Pechman
- Vanderbilt Memory and Alzheimer's CenterVanderbilt University School of MedicineNashvilleTennesseeUSA
| | - Michael E. Kim
- Department of Computer ScienceVanderbilt UniversityNashvilleTennesseeUSA
| | - Chenyu Gao
- Department of Electrical and Computer EngineeringVanderbilt UniversityNashvilleTennesseeUSA
| | - Nazirah Mohd Khairi
- Department of Electrical and Computer EngineeringVanderbilt UniversityNashvilleTennesseeUSA
| | - Zhiyuan Li
- Department of Electrical and Computer EngineeringVanderbilt UniversityNashvilleTennesseeUSA
| | - Tianyuan Yao
- Department of Computer ScienceVanderbilt UniversityNashvilleTennesseeUSA
| | - Yuankai Huo
- Department of Computer ScienceVanderbilt UniversityNashvilleTennesseeUSA
- Department of Electrical and Computer EngineeringVanderbilt UniversityNashvilleTennesseeUSA
| | - Logan Dumitrescu
- Vanderbilt Memory and Alzheimer's CenterVanderbilt University School of MedicineNashvilleTennesseeUSA
- Department of NeurologyVanderbilt University Medical CenterNashvilleTennesseeUSA
- Vanderbilt Genetics InstituteVanderbilt University Medical CenterNashvilleTennesseeUSA
- Vanderbilt Brain InstituteVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - Katherine A. Gifford
- Vanderbilt Memory and Alzheimer's CenterVanderbilt University School of MedicineNashvilleTennesseeUSA
- Department of NeurologyVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - Jo Ellen Wilson
- Vanderbilt Memory and Alzheimer's CenterVanderbilt University School of MedicineNashvilleTennesseeUSA
- Department of Psychiatry and Behavioral SciencesCenter for Cognitive Medicine, Vanderbilt University Medical CenterNashvilleTennesseeUSA
- Veteran's Affairs, Geriatric Research, Education and Clinical CenterTennessee Valley Healthcare SystemNashvilleTennesseeUSA
| | - Francis E. Cambronero
- Vanderbilt Memory and Alzheimer's CenterVanderbilt University School of MedicineNashvilleTennesseeUSA
| | - Shannon L. Risacher
- Department of Radiology and Imaging SciencesIndiana University School of MedicineIndianapolisIndianaUSA
- Indiana Alzheimer's Disease Research CenterIndiana University School of MedicineIndianapolisIndianaUSA
| | - Lori L. Beason‐Held
- Laboratory for Behavioral NeuroscienceNational Institute on Aging, National Institutes of HealthBaltimoreMarylandUSA
| | - Yang An
- Laboratory for Behavioral NeuroscienceNational Institute on Aging, National Institutes of HealthBaltimoreMarylandUSA
| | - Konstantinos Arfanakis
- Department of Biomedical EngineeringIllinois Institute of TechnologyChicagoIllinoisUSA
- Rush Alzheimer's Disease CenterRush University Medical CenterChicagoIllinoisUSA
- Department of Diagnostic RadiologyRush University Medical CenterChicagoIllinoisUSA
| | - Guray Erus
- Department of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Christos Davatzikos
- Department of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Duygu Tosun
- Department of Radiology and Biomedical ImagingUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | - Arthur W. Toga
- Laboratory of Neuroimaging, USC Stevens Institute of Neuroimaging and InformaticsKeck School of Medicine, University of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Paul M. Thompson
- Imaging Genetics Center, Mark and Mary Stevens Institute for Neuroimaging and InformaticsKeck School of Medicine, University of Southern CaliforniaMarina del ReyCaliforniaUSA
| | - Elizabeth C. Mormino
- Department of Neurology and Neurological SciencesStanford University School of MedicineStanfordCaliforniaUSA
| | - Mohamad Habes
- Neuroimage Analytics Laboratory and Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Neurodegenerative DisordersUniversity of Texas Health Science Center at San AntonioSan AntonioTexasUSA
| | - Di Wang
- Neuroimage Analytics Laboratory and Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Neurodegenerative DisordersUniversity of Texas Health Science Center at San AntonioSan AntonioTexasUSA
| | - Panpan Zhang
- Vanderbilt Memory and Alzheimer's CenterVanderbilt University School of MedicineNashvilleTennesseeUSA
- Department of BiostatisticsVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - Kurt Schilling
- Department of Radiology & Radiological SciencesVanderbilt University Medical CenterNashvilleTennesseeUSA
- Vanderbilt University Institute of Imaging ScienceVanderbilt University Medical CenterNashvilleTennesseeUSA
| | | | | | | | - Marilyn Albert
- Department of NeurologyJohns Hopkins School of MedicineBaltimoreMarylandUSA
| | - Walter Kukull
- National Alzheimer's Coordinating CenterUniversity of WashingtonSeattleWashingtonUSA
| | - Sarah A. Biber
- National Alzheimer's Coordinating CenterUniversity of WashingtonSeattleWashingtonUSA
| | - Bennett A. Landman
- Department of NeurologyVanderbilt University Medical CenterNashvilleTennesseeUSA
- Department of Computer ScienceVanderbilt UniversityNashvilleTennesseeUSA
- Department of Electrical and Computer EngineeringVanderbilt UniversityNashvilleTennesseeUSA
- Vanderbilt Brain InstituteVanderbilt University Medical CenterNashvilleTennesseeUSA
- Department of Radiology & Radiological SciencesVanderbilt University Medical CenterNashvilleTennesseeUSA
- Vanderbilt University Institute of Imaging ScienceVanderbilt University Medical CenterNashvilleTennesseeUSA
- Department of Biomedical EngineeringVanderbilt UniversityNashvilleTennesseeUSA
| | - Sterling C. Johnson
- Wisconsin Alzheimer's Disease Research CenterUniversity of Wisconsin School of Medicine and Public HealthMadisonWisconsinUSA
- Wisconsin Alzheimer's InstituteUniversity of Wisconsin School of Medicine and Public HealthMadisonWisconsinUSA
| | - Julie Schneider
- Rush Alzheimer's Disease CenterRush University Medical CenterChicagoIllinoisUSA
| | - Lisa L. Barnes
- Rush Alzheimer's Disease CenterRush University Medical CenterChicagoIllinoisUSA
| | - David A. Bennett
- Rush Alzheimer's Disease CenterRush University Medical CenterChicagoIllinoisUSA
| | - Angela L. Jefferson
- Vanderbilt Memory and Alzheimer's CenterVanderbilt University School of MedicineNashvilleTennesseeUSA
- Department of NeurologyVanderbilt University Medical CenterNashvilleTennesseeUSA
- Department of Computer ScienceVanderbilt UniversityNashvilleTennesseeUSA
| | - Susan M. Resnick
- Laboratory for Behavioral NeuroscienceNational Institute on Aging, National Institutes of HealthBaltimoreMarylandUSA
| | - Andrew J. Saykin
- Department of Radiology and Imaging SciencesIndiana University School of MedicineIndianapolisIndianaUSA
- Indiana Alzheimer's Disease Research CenterIndiana University School of MedicineIndianapolisIndianaUSA
| | - Timothy J. Hohman
- Vanderbilt Memory and Alzheimer's CenterVanderbilt University School of MedicineNashvilleTennesseeUSA
- Department of NeurologyVanderbilt University Medical CenterNashvilleTennesseeUSA
- Vanderbilt Genetics InstituteVanderbilt University Medical CenterNashvilleTennesseeUSA
- Vanderbilt Brain InstituteVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - Derek B. Archer
- Vanderbilt Memory and Alzheimer's CenterVanderbilt University School of MedicineNashvilleTennesseeUSA
- Department of NeurologyVanderbilt University Medical CenterNashvilleTennesseeUSA
- Vanderbilt Genetics InstituteVanderbilt University Medical CenterNashvilleTennesseeUSA
- Vanderbilt Brain InstituteVanderbilt University Medical CenterNashvilleTennesseeUSA
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3
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Lissaman R, Rajagopal S, Kearley J, Pasvanis S, Rajah MN. Menopause status- and sex-related differences in age associations with spatial context memory and white matter microstructure at midlife. Neurobiol Aging 2024; 141:151-159. [PMID: 38954878 DOI: 10.1016/j.neurobiolaging.2024.05.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Revised: 05/28/2024] [Accepted: 05/31/2024] [Indexed: 07/04/2024]
Abstract
Decline in spatial context memory emerges in midlife, the time when most females transition from pre- to post-menopause. Recent evidence suggests that, among post-menopausal females, advanced age is associated with functional brain alterations and lower spatial context memory. However, it is unknown whether similar effects are evident for white matter (WM) and, moreover, whether such effects contribute to sex differences at midlife. To address this, we conducted a study on 96 cognitively unimpaired middle-aged adults (30 males, 32 pre-menopausal females, 34 post-menopausal females). Spatial context memory was assessed using a face-location memory paradigm, while WM microstructure was assessed using diffusion tensor imaging. Behaviorally, advanced age was associated with lower spatial context memory in post-menopausal females but not pre-menopausal females or males. Additionally, advanced age was associated with microstructural variability in predominantly frontal WM (e.g., anterior corona radiata, genu of corpus callosum), which was related to lower spatial context memory among post-menopausal females. Our findings suggest that post-menopausal status enhances vulnerability to age effects on the brain's WM and episodic memory.
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Affiliation(s)
- Rikki Lissaman
- Department of Psychiatry, McGill University, Montreal, QC, Canada.
| | | | - Julia Kearley
- Department of Psychology, McGill University, Montreal, QC, Canada
| | | | - Maria Natasha Rajah
- Department of Psychiatry, McGill University, Montreal, QC, Canada; Department of Psychology, Toronto Metropolitan University, Toronto, ON, Canada
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Arenaza‐Urquijo EM, Boyle R, Casaletto K, Anstey KJ, Vila‐Castelar C, Colverson A, Palpatzis E, Eissman JM, Kheng Siang Ng T, Raghavan S, Akinci M, Vonk JMJ, Machado LS, Zanwar PP, Shrestha HL, Wagner M, Tamburin S, Sohrabi HR, Loi S, Bartrés‐Faz D, Dubal DB, Vemuri P, Okonkwo O, Hohman TJ, Ewers M, Buckley RF. Sex and gender differences in cognitive resilience to aging and Alzheimer's disease. Alzheimers Dement 2024; 20:5695-5719. [PMID: 38967222 PMCID: PMC11350140 DOI: 10.1002/alz.13844] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 03/08/2024] [Accepted: 03/21/2024] [Indexed: 07/06/2024]
Abstract
Sex and gender-biological and social constructs-significantly impact the prevalence of protective and risk factors, influencing the burden of Alzheimer's disease (AD; amyloid beta and tau) and other pathologies (e.g., cerebrovascular disease) which ultimately shape cognitive trajectories. Understanding the interplay of these factors is central to understanding resilience and resistance mechanisms explaining maintained cognitive function and reduced pathology accumulation in aging and AD. In this narrative review, the ADDRESS! Special Interest Group (Alzheimer's Association) adopted a multidisciplinary approach to provide the foundations and recommendations for future research into sex- and gender-specific drivers of resilience, including a sex/gender-oriented review of risk factors, genetics, AD and non-AD pathologies, brain structure and function, and animal research. We urge the field to adopt a sex/gender-aware approach to resilience to advance our understanding of the intricate interplay of biological and social determinants and consider sex/gender-specific resilience throughout disease stages. HIGHLIGHTS: Sex differences in resilience to cognitive decline vary by age and cognitive status. Initial evidence supports sex-specific distinctions in brain pathology. Findings suggest sex differences in the impact of pathology on cognition. There is a sex-specific change in resilience in the transition to clinical stages. Gender and sex factors warrant study: modifiable, immune, inflammatory, and vascular.
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Affiliation(s)
- Eider M. Arenaza‐Urquijo
- Environment and Health Over the Life Course Programme, Climate, Air Pollution, Nature and Urban Health ProgrammeBarcelona Institute for Global Health (ISGlobal)BarcelonaSpain
- University of Pompeu FabraBarcelonaBarcelonaSpain
| | - Rory Boyle
- Massachusetts General HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Kaitlin Casaletto
- Department of NeurologyMemory and Aging CenterUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | - Kaarin J. Anstey
- University of New South Wales Ageing Futures InstituteSydneyNew South WalesAustralia
- Neuroscience Research AustraliaSydneyNew South WalesAustralia
- School of Psychology, University of New South WalesSidneyNew South WalesAustralia
| | | | - Aaron Colverson
- University of Florida Center for Arts in Medicine Interdisciplinary Research LabUniversity of Florida, Center of Arts in MedicineGainesvilleFloridaUSA
| | - Eleni Palpatzis
- Environment and Health Over the Life Course Programme, Climate, Air Pollution, Nature and Urban Health ProgrammeBarcelona Institute for Global Health (ISGlobal)BarcelonaSpain
- University of Pompeu FabraBarcelonaBarcelonaSpain
| | - Jaclyn M. Eissman
- Vanderbilt Memory and Alzheimer's Center, Department of NeurologyVanderbilt University Medical CenterNashvilleTennesseeUSA
- Vanderbilt Genetics InstituteVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - Ted Kheng Siang Ng
- Rush Institute for Healthy Aging and Department of Internal MedicineRush University Medical CenterChicagoIllinoisUSA
| | | | - Muge Akinci
- Environment and Health Over the Life Course Programme, Climate, Air Pollution, Nature and Urban Health ProgrammeBarcelona Institute for Global Health (ISGlobal)BarcelonaSpain
- University of Pompeu FabraBarcelonaBarcelonaSpain
| | - Jet M. J. Vonk
- Department of NeurologyMemory and Aging CenterUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | - Luiza S. Machado
- Graduate Program in Biological Sciences: Biochemistry, Universidade Federal Do Rio Grande Do Sul, FarroupilhaPorto AlegreBrazil
| | - Preeti P. Zanwar
- Jefferson College of Population Health, Thomas Jefferson UniversityPhiladelphiaPennsylvaniaUSA
- The Network on Life Course and Health Dynamics and Disparities, University of Southern CaliforniaLos AngelesCaliforniaUSA
| | | | - Maude Wagner
- Rush Alzheimer's Disease Center, Rush University Medical CenterChicagoIllinoisUSA
| | - Stefano Tamburin
- Department of Neurosciences, Biomedicine and Movement SciencesUniversity of VeronaVeronaItaly
| | - Hamid R. Sohrabi
- Centre for Healthy AgeingHealth Future InstituteMurdoch UniversityMurdochWestern AustraliaAustralia
- School of Psychology, Murdoch UniversityMurdochWestern AustraliaAustralia
| | - Samantha Loi
- Neuropsychiatry Centre, Royal Melbourne HospitalParkvilleVictoriaAustralia
- Department of PsychiatryUniversity of MelbourneParkvilleVictoriaAustralia
| | - David Bartrés‐Faz
- Department of MedicineFaculty of Medicine and Health Sciences & Institut de NeurociènciesUniversity of BarcelonaBarcelonaBarcelonaSpain
- Institut d'Investigacions Biomèdiques (IDIBAPS)BarcelonaBarcelonaSpain
- Institut Guttmann, Institut Universitari de Neurorehabilitació adscrit a la Universitat Autónoma de BarcelonaBadalonaBarcelonaSpain
| | - Dena B. Dubal
- Department of Neurology and Weill Institute of NeurosciencesUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
- Biomedical and Neurosciences Graduate ProgramsUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
| | | | - Ozioma Okonkwo
- Alzheimer's Disease Research Center and Department of MedicineUniversity of Wisconsin School of Medicine and Public HealthMadisonWisconsinUSA
| | - Timothy J. Hohman
- Vanderbilt Memory and Alzheimer's Center, Department of NeurologyVanderbilt University Medical CenterNashvilleTennesseeUSA
- Vanderbilt Genetics InstituteVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - Michael Ewers
- Institute for Stroke and Dementia ResearchKlinikum der Universität MünchenLudwig Maximilians Universität (LMU)MunichGermany
- German Center for Neurodegenerative Diseases (DZNE, Munich)MunichGermany
| | - Rachel F. Buckley
- Massachusetts General HospitalHarvard Medical SchoolBostonMassachusettsUSA
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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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Peterson A, Sathe A, Zaras D, Yang Y, Durant A, Deters KD, Shashikumar N, Pechman KR, Kim ME, Gao C, Khairi NM, Li Z, Yao T, Huo Y, Dumitrescu L, Gifford KA, Wilson JE, Cambronero F, Risacher SL, Beason-Held LL, An Y, Arfanakis K, Erus G, Davatzikos C, Tosun D, Toga AW, Thompson PM, Mormino EC, Zhang P, Schilling K, Albert M, Kukull W, Biber SA, Landman BA, Johnson SC, Schneider J, Barnes LL, Bennett DA, Jefferson AL, Resnick SM, Saykin AJ, Hohman TJ, Archer DB. Sex, racial, and APOE-ε4 allele differences in longitudinal white matter microstructure in multiple cohorts of aging and Alzheimer's disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.10.598357. [PMID: 38915636 PMCID: PMC11195046 DOI: 10.1101/2024.06.10.598357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/26/2024]
Abstract
INTRODUCTION The effects of sex, race, and Apolipoprotein E (APOE) - Alzheimer's disease (AD) risk factors - on white matter integrity are not well characterized. METHODS Diffusion MRI data from nine well-established longitudinal cohorts of aging were free-water (FW)-corrected and harmonized. This dataset included 4,702 participants (age=73.06 ± 9.75) with 9,671 imaging sessions over time. FW and FW-corrected fractional anisotropy (FAFWcorr) were used to assess differences in white matter microstructure by sex, race, and APOE-ε4 carrier status. RESULTS Sex differences in FAFWcorr in association and projection tracts, racial differences in FAFWcorr in projection tracts, and APOE-ε4 differences in FW limbic and occipital transcallosal tracts were most pronounced. DISCUSSION There are prominent differences in white matter microstructure by sex, race, and APOE-ε4 carrier status. This work adds to our understanding of disparities in AD. Additional work to understand the etiology of these differences is warranted.
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Affiliation(s)
- Amalia Peterson
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University School of Medicine, Nashville, TN
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN
| | - Aditi Sathe
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University School of Medicine, Nashville, TN
| | - Dimitrios Zaras
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University School of Medicine, Nashville, TN
| | - Yisu Yang
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University School of Medicine, Nashville, TN
| | - Alaina Durant
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University School of Medicine, Nashville, TN
| | - Kacie D. Deters
- Department of Integrative Biology and Physiology, University of California, Los Angeles
| | - Niranjana Shashikumar
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University School of Medicine, Nashville, TN
| | - Kimberly R. Pechman
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University School of Medicine, Nashville, TN
| | - Michael E. Kim
- Department of Computer Science, Vanderbilt University, Nashville, TN
| | - Chenyu Gao
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN
| | - Nazirah Mohd Khairi
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN
| | - Zhiyuan Li
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN
| | - Tianyuan Yao
- Department of Computer Science, Vanderbilt University, Nashville, TN
| | - Yuankai Huo
- Department of Computer Science, Vanderbilt University, Nashville, TN
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN
| | - Logan Dumitrescu
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University School of Medicine, Nashville, TN
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN
- Vanderbilt Brain Institute, Vanderbilt University Medical Center, Nashville, TN
| | - Katherine A. Gifford
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University School of Medicine, Nashville, TN
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN
| | - Jo Ellen Wilson
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University School of Medicine, Nashville, TN
- Center for Cognitive Medicine, Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN
- Veteran‘s Affairs, Geriatric Research, Education and Clinical Center, Tennessee Valley Healthcare System
| | - Francis Cambronero
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University School of Medicine, Nashville, TN
| | - Shannon L. Risacher
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN
- Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN
| | - Lori L. Beason-Held
- Laboratory for Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD
| | - Yang An
- Laboratory for Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD
| | - Konstantinos Arfanakis
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL
- Department of Diagnostic Radiology, Rush University Medical Center, Chicago, IL
| | - Guray Erus
- Department of Radiology, University of Pennsylvania, Philadelphia, PA
| | | | - Duygu Tosun
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA
| | - Arthur W. Toga
- Laboratory of Neuroimaging, USC Stevens Institute of Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA
| | - Paul M. Thompson
- Imaging Genetics Center, Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Marina del Rey, CA
| | - Elizabeth C. Mormino
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA
| | - Panpan Zhang
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University School of Medicine, Nashville, TN
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN
| | - Kurt Schilling
- Department of Radiology & Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN2
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN
| | | | | | | | - Marilyn Albert
- Department of Neurology, Johns Hopkins School of Medicine Baltimore, MD
| | - Walter Kukull
- National Alzheimer’s Coordinating Center, University of Washington, Seattle, WA
| | - Sarah A. Biber
- National Alzheimer’s Coordinating Center, University of Washington, Seattle, WA
| | - Bennett A. Landman
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN
- Department of Computer Science, Vanderbilt University, Nashville, TN
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN
- Vanderbilt Brain Institute, Vanderbilt University Medical Center, Nashville, TN
- Department of Radiology & Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN2
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN
| | - Sterling C. Johnson
- Wisconsin Alzheimer’s Disease Research Center, School of Medicine and Public Health, University of Wisconsin, Madison, WI
- Wisconsin Alzheimer’s Institute, School of Medicine and Public Health, University of Wisconsin, Madison, WI
| | - Julie Schneider
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL
| | - Lisa L. Barnes
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL
| | - David A. Bennett
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL
| | - Angela L. Jefferson
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University School of Medicine, Nashville, TN
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN
- Department of Computer Science, Vanderbilt University, Nashville, TN
| | - Susan M. Resnick
- Laboratory for Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD
| | - Andrew J. Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN
- Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN
| | - Timothy J. Hohman
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University School of Medicine, Nashville, TN
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN
- Vanderbilt Brain Institute, Vanderbilt University Medical Center, Nashville, TN
| | - Derek B. Archer
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University School of Medicine, Nashville, TN
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN
- Vanderbilt Brain Institute, Vanderbilt University Medical Center, Nashville, TN
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7
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Schilling KG, Chad JA, Chamberland M, Nozais V, Rheault F, Archer D, Li M, Gao Y, Cai L, Del'Acqua F, Newton A, Moyer D, Gore JC, Lebel C, Landman BA. White matter tract microstructure, macrostructure, and associated cortical gray matter morphology across the lifespan. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.25.559330. [PMID: 37808645 PMCID: PMC10557619 DOI: 10.1101/2023.09.25.559330] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
Characterizing how, when and where the human brain changes across the lifespan is fundamental to our understanding of developmental processes of childhood and adolescence, degenerative processes of aging, and divergence from normal patterns in disease and disorders. We aimed to provide detailed descriptions of white matter pathways across the lifespan by thoroughly characterizing white matter microstructure, white matter macrostructure, and morphology of the cortex associated with white matter pathways. We analyzed 4 large, high-quality, publicly-available datasets comprising 2789 total imaging sessions, and participants ranging from 0 to 100 years old, using advanced tractography and diffusion modeling. We first find that all microstructural, macrostructural, and cortical features of white matter bundles show unique lifespan trajectories, with rates and timing of development and degradation that vary across pathways - describing differences between types of pathways and locations in the brain, and developmental milestones of maturation of each feature. Second, we show cross-sectional relationships between different features that may help elucidate biological changes occurring during different stages of the lifespan. Third, we show unique trajectories of age-associations across features. Finally, we find that age associations during development are strongly related to those during aging. Overall, this study reports normative data for several features of white matter pathways of the human brain that will be useful for studying normal and abnormal white matter development and degeneration.
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Affiliation(s)
- Kurt G Schilling
- Department of Radiology & Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jordan A Chad
- Rotman Research Institute, Baycrest Academy for Research and Education, Toronto, ON, Canada
- Department of Radiology, University of Calgary, Calgary, AB, Canada
| | - Maxime Chamberland
- Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands
| | | | - Francois Rheault
- Medical Imaging and Neuroinformatic (MINi) Lab, Department of Computer Science, University of Sherbrooke, Canada
| | - Derek Archer
- Vanderbilt Memory & Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, 37212, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Muwei Li
- Department of Radiology & Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Yurui Gao
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Leon Cai
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Flavio Del'Acqua
- NatbrainLab, Department of Forensics and Neurodevelopmental Sciences, King's College London, London UK
| | - Allen Newton
- Department of Radiology & Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Daniel Moyer
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - John C Gore
- Department of Radiology & Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Catherine Lebel
- Alberta Children's Hospital Research Institute (ACHRI), Calgary, AB, Canada
- Department of Radiology, University of Calgary, Calgary, AB, Canada
| | - Bennett A Landman
- Department of Radiology & Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
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8
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Trofimova O, Latypova A, DiDomenicantonio G, Lutti A, de Lange AMG, Kliegel M, Stringhini S, Marques-Vidal P, Vaucher J, Vollenweider P, Strippoli MPF, Preisig M, Kherif F, Draganski B. Topography of associations between cardiovascular risk factors and myelin loss in the ageing human brain. Commun Biol 2023; 6:392. [PMID: 37037939 PMCID: PMC10086032 DOI: 10.1038/s42003-023-04741-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 03/21/2023] [Indexed: 04/12/2023] Open
Abstract
Our knowledge of the mechanisms underlying the vulnerability of the brain's white matter microstructure to cardiovascular risk factors (CVRFs) is still limited. We used a quantitative magnetic resonance imaging (MRI) protocol in a single centre setting to investigate the cross-sectional association between CVRFs and brain tissue properties of white matter tracts in a large community-dwelling cohort (n = 1104, age range 46-87 years). Arterial hypertension was associated with lower myelin and axonal density MRI indices, paralleled by higher extracellular water content. Obesity showed similar associations, though with myelin difference only in male participants. Associations between CVRFs and white matter microstructure were observed predominantly in limbic and prefrontal tracts. Additional genetic, lifestyle and psychiatric factors did not modulate these results, but moderate-to-vigorous physical activity was linked to higher myelin content independently of CVRFs. Our findings complement previously described CVRF-related changes in brain water diffusion properties pointing towards myelin loss and neuroinflammation rather than neurodegeneration.
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Affiliation(s)
- Olga Trofimova
- Laboratory for Research in Neuroimaging LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Adeliya Latypova
- Laboratory for Research in Neuroimaging LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Giulia DiDomenicantonio
- Laboratory for Research in Neuroimaging LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Antoine Lutti
- Laboratory for Research in Neuroimaging LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Ann-Marie G de Lange
- Laboratory for Research in Neuroimaging LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- Department of Psychology, University of Oslo, Oslo, Norway
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Matthias Kliegel
- Department of Psychology, University of Geneva, Geneva, Switzerland
| | - Silvia Stringhini
- Center for Primary Care and Public Health (Unisanté), University of Lausanne, Lausanne, Switzerland
- Institute of Social and Preventive Medicine, Lausanne University Hospital, Lausanne, Switzerland
- Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals and Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Pedro Marques-Vidal
- Department of Medicine, Internal Medicine, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Julien Vaucher
- Department of Medicine, Internal Medicine, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Peter Vollenweider
- Department of Medicine, Internal Medicine, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Marie-Pierre F Strippoli
- Center for Research in Psychiatric Epidemiology and Psychopathology, Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Martin Preisig
- Center for Research in Psychiatric Epidemiology and Psychopathology, Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Ferath Kherif
- Laboratory for Research in Neuroimaging LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Bogdan Draganski
- Laboratory for Research in Neuroimaging LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
- Neurology Department, Max-Planck-Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
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9
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Bouhrara M, Avram AV, Kiely M, Trivedi A, Benjamini D. Adult lifespan maturation and degeneration patterns in gray and white matter: A mean apparent propagator (MAP) MRI study. Neurobiol Aging 2023; 124:104-116. [PMID: 36641369 PMCID: PMC9985137 DOI: 10.1016/j.neurobiolaging.2022.12.016] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 12/24/2022] [Accepted: 12/27/2022] [Indexed: 01/02/2023]
Abstract
The relationship between brain microstructure and aging has been the subject of intense study, with diffusion MRI perhaps the most effective modality for elucidating these associations. Here, we used the mean apparent propagator (MAP)-MRI framework, which is suitable to characterize complex microstructure, to investigate age-related cerebral differences in a cohort of cognitively unimpaired participants and compared the results to those derived using diffusion tensor imaging. We studied MAP-MRI metrics, among them the non-Gaussianity (NG) and propagator anisotropy (PA), and established an opposing pattern in white matter of higher NG alongside lower PA among older adults, likely indicative of axonal degradation. In gray matter, however, these two indices were consistent with one another, and exhibited regional pattern heterogeneity compared to other microstructural parameters, which could indicate fewer neuronal projections across cortical layers along with an increased glial concentration. In addition, we report regional variations in the magnitude of age-related microstructural differences consistent with the posterior-anterior shift in aging paradigm. These results encourage further investigations in cognitive impairments and neurodegeneration.
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Affiliation(s)
- Mustapha Bouhrara
- Magnetic Resonance Physics of Aging and Dementia Unit, National Institute on Aging, NIH, Baltimore, MD 21224, USA.
| | - Alexandru V. Avram
- Section on Quantitative Imaging and Tissue Sciences,Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, MD, USA,Center for Neuroscience and Regenerative Medicine, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
| | - Matthew Kiely
- Magnetic Resonance Physics of Aging and Dementia Unit, National Institute on Aging, NIH, Baltimore, MD 21224, USA
| | - Aparna Trivedi
- Multiscale Imaging and Integrative Biophysics Unit, National Institute on Aging, NIH, Baltimore, MD 21224, USA
| | - Dan Benjamini
- Multiscale Imaging and Integrative Biophysics Unit, National Institute on Aging, NIH, Baltimore, MD 21224, USA.
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10
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Schaechter JD, Kim M, Hightower BG, Ragas T, Loggia ML. Disruptions in Structural and Functional Connectivity Relate to Poststroke Fatigue. Brain Connect 2023; 13:15-27. [PMID: 35570655 PMCID: PMC9942175 DOI: 10.1089/brain.2022.0021] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Introduction: Poststroke fatigue (PSF) is a disabling condition with unclear etiology. The brain lesion is thought to be an important causal factor in PSF, although focal lesion characteristics such as size and location have not proven to be predictive. Given that the stroke lesion results not only in focal tissue death but also in widespread changes in brain networks that are structurally and functionally connected to damaged tissue, we hypothesized that PSF relates to disruptions in structural and functional connectivity. Materials and Methods: Twelve patients who incurred an ischemic stroke in the middle cerebral artery (MCA) territory 1-3 years prior, and currently experiencing a range of fatigue severity, were enrolled. The patients underwent structural and resting-state functional magnetic resonance imaging (MRI). The structural MRI data were used to measure structural disconnection of gray matter resulting from lesion to white matter pathways. The functional MRI data were used to measure network functional connectivity. Results: The patients showed structural disconnection in varying cortical and subcortical regions. Fatigue severity correlated significantly with structural disconnection of several frontal cortex regions in the ipsilesional (IL) and contralesional hemispheres. Fatigue-related structural disconnection was most severe in the IL rostral middle frontal cortex. Greater structural disconnection of a subset of fatigue-related frontal cortex regions, including the IL rostral middle frontal cortex, trended toward correlating significantly with greater loss in functional connectivity. Among identified fatigue-related frontal cortex regions, only the IL rostral middle frontal cortex showed loss in functional connectivity correlating significantly with fatigue severity. Conclusion: Our results provide evidence that loss in structural and functional connectivity of bihemispheric frontal cortex regions plays a role in PSF after MCA stroke, with connectivity disruptions of the IL rostral middle frontal cortex having a central role. Impact statement Poststroke fatigue (PSF) is a common disabling condition with unclear etiology. We hypothesized that PSF relates to disruptions in structural and functional connectivity secondary to the focal lesion. Using structural and resting-state functional connectivity magnetic resonance imaging (MRI) in patients with chronic middle cerebral artery (MCA) stroke, we found frontal cortex regions in the ipsilesional (IL) and contralesional hemispheres with greater structural disconnection correlating with greater fatigue. Among these fatigue-related cortices, the IL rostral middle frontal cortex showed loss in functional connectivity correlating with fatigue. These findings suggest that disruptions in structural and functional connectivity play a role in PSF after MCA stroke.
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Affiliation(s)
- Judith D. Schaechter
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA
| | - Minhae Kim
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA
| | - Baileigh G. Hightower
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA
| | - Trevor Ragas
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA
| | - Marco L. Loggia
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA
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11
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Marvel CL, Alm KH, Bhattacharya D, Rebman AW, Bakker A, Morgan OP, Creighton JA, Kozero EA, Venkatesan A, Nadkarni PA, Aucott JN. A multimodal neuroimaging study of brain abnormalities and clinical correlates in post treatment Lyme disease. PLoS One 2022; 17:e0271425. [PMID: 36288329 PMCID: PMC9604010 DOI: 10.1371/journal.pone.0271425] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 09/15/2022] [Indexed: 01/24/2023] Open
Abstract
Lyme disease is the most common vector-borne infectious disease in the United States. Post-treatment Lyme disease (PTLD) is a condition affecting 10-20% of patients in which symptoms persist despite antibiotic treatment. Cognitive complaints are common among those with PTLD, suggesting that brain changes are associated with the course of the illness. However, there has been a paucity of evidence to explain the cognitive difficulties expressed by patients with PTLD. This study administered a working memory task to a carefully screened group of 12 patients with well-characterized PTLD and 18 healthy controls while undergoing functional MRI (fMRI). A subset of 12 controls and all 12 PTLD participants also received diffusion tensor imaging (DTI) to measure white matter integrity. Clinical variables were also assessed and correlated with these multimodal MRI findings. On the working memory task, the patients with PTLD responded more slowly, but no less accurately, than did controls. FMRI activations were observed in expected regions by the controls, and to a lesser extent, by the PTLD participants. The PTLD group also hypoactivated several regions relevant to the task. Conversely, novel regions were activated by the PTLD group that were not observed in controls, suggesting a compensatory mechanism. Notably, three activations were located in white matter of the frontal lobe. DTI measures applied to these three regions of interest revealed that higher axial diffusivity correlated with fewer cognitive and neurological symptoms. Whole-brain DTI analyses revealed several frontal lobe regions in which higher axial diffusivity in the patients with PTLD correlated with longer duration of illness. Together, these results show that the brain is altered by PTLD, involving changes to white matter within the frontal lobe. Higher axial diffusivity may reflect white matter repair and healing over time, rather than pathology, and cognition appears to be dynamically affected throughout this repair process.
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Affiliation(s)
- Cherie L. Marvel
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, United States of America
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, United States of America
- * E-mail:
| | - Kylie H. Alm
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, United States of America
| | - Deeya Bhattacharya
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, United States of America
| | - Alison W. Rebman
- Division of Rheumatology, Department of Medicine, Lyme Disease Research Center, Johns Hopkins University School of Medicine, Baltimore, MD, United States of America
| | - Arnold Bakker
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, United States of America
| | - Owen P. Morgan
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, United States of America
| | - Jason A. Creighton
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, United States of America
| | - Erica A. Kozero
- Division of Rheumatology, Department of Medicine, Lyme Disease Research Center, Johns Hopkins University School of Medicine, Baltimore, MD, United States of America
| | - Arun Venkatesan
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, United States of America
| | - Prianca A. Nadkarni
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, United States of America
| | - John N. Aucott
- Division of Rheumatology, Department of Medicine, Lyme Disease Research Center, Johns Hopkins University School of Medicine, Baltimore, MD, United States of America
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12
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Aging and white matter microstructure and macrostructure: a longitudinal multi-site diffusion MRI study of 1218 participants. Brain Struct Funct 2022; 227:2111-2125. [PMID: 35604444 DOI: 10.1007/s00429-022-02503-z] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 04/22/2022] [Indexed: 11/02/2022]
Abstract
Quantifying the microstructural and macrostructural geometrical features of the human brain's connections is necessary for understanding normal aging and disease. Here, we examine brain white matter diffusion magnetic resonance imaging data from one cross-sectional and two longitudinal data sets totaling in 1218 subjects and 2459 sessions of people aged 50-97 years. Data was drawn from well-established cohorts, including the Baltimore Longitudinal Study of Aging data set, Cambridge Centre for Ageing Neuroscience data set, and the Vanderbilt Memory & Aging Project. Quantifying 4 microstructural features and, for the first time, 11 macrostructure-based features of volume, area, and length across 120 white matter pathways, we apply linear mixed effect modeling to investigate changes in pathway-specific features over time, and document large age associations within white matter. Conventional diffusion tensor microstructure indices are the most age-sensitive measures, with positive age associations for diffusivities and negative age associations with anisotropies, with similar patterns observed across all pathways. Similarly, pathway shape measures also change with age, with negative age associations for most length, surface area, and volume-based features. A particularly novel finding of this study is that while trends were homogeneous throughout the brain for microstructure features, macrostructural features demonstrated heterogeneity across pathways, whereby several projection, thalamic, and commissural tracts exhibited more decline with age compared to association and limbic tracts. The findings from this large-scale study provide a comprehensive overview of the age-related decline in white matter and demonstrate that macrostructural features may be more sensitive to heterogeneous white matter decline. Therefore, leveraging macrostructural features may be useful for studying aging and could facilitate comparisons in a variety of diseases or abnormal conditions.
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13
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Neef NE, Korzeczek A, Primaßin A, Wolff von Gudenberg A, Dechent P, Riedel CH, Paulus W, Sommer M. White matter tract strength correlates with therapy outcome in persistent developmental stuttering. Hum Brain Mapp 2022; 43:3357-3374. [PMID: 35415866 PMCID: PMC9248304 DOI: 10.1002/hbm.25853] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 03/22/2022] [Accepted: 03/22/2022] [Indexed: 11/11/2022] Open
Abstract
Persistent stuttering is a prevalent neurodevelopmental speech disorder, which presents with involuntary speech blocks, sound and syllable repetitions, and sound prolongations. Affected individuals often struggle with negative feelings, elevated anxiety, and low self-esteem. Neuroimaging studies frequently link persistent stuttering with cortical alterations and dysfunctional cortico-basal ganglia-thalamocortical loops; dMRI data also point toward connectivity changes of the superior longitudinal fasciculus (SLF) and the frontal aslant tract (FAT). Both tracts are involved in speech and language functions, and the FAT also supports inhibitory control and conflict monitoring. Whether the two tracts are involved in therapy-associated improvements and how they relate to therapeutic outcomes is currently unknown. Here, we analyzed dMRI data of 22 patients who participated in a fluency-shaping program, 18 patients not participating in therapy, and 27 fluent control participants, measured 1 year apart. We used diffusion tractography to segment the SLF and FAT bilaterally and to quantify their microstructural properties before and after a fluency-shaping program. Participants learned to speak with soft articulation, pitch, and voicing during a 2-week on-site boot camp and computer-assisted biofeedback-based daily training for 1 year. Therapy had no impact on the microstructural properties of the two tracts. Yet, after therapy, stuttering severity correlated positively with left SLF fractional anisotropy, whereas relief from the social-emotional burden to stutter correlated negatively with right FAT fractional anisotropy. Thus, posttreatment, speech motor performance relates to the left dorsal stream, while the experience of the adverse impact of stuttering relates to the structure recently associated with conflict monitoring and action inhibition.
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Affiliation(s)
- Nicole E Neef
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Göttingen, Göttingen, Germany.,Department of Clinical Neurophysiology, University Medical Center Göttingen, Göttingen, Germany
| | - Alexandra Korzeczek
- Department of Clinical Neurophysiology, University Medical Center Göttingen, Göttingen, Germany
| | - Annika Primaßin
- Department of Clinical Neurophysiology, University Medical Center Göttingen, Göttingen, Germany.,Fachbereich Gesundheit, FH Münster University of Applied Sciences, Münster, Germany
| | | | - Peter Dechent
- Department of Cognitive Neurology, MR Research in Neurosciences, University Medical Center Göttingen, Göttingen, Germany
| | - Christian Heiner Riedel
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Göttingen, Göttingen, Germany
| | - Walter Paulus
- Department of Clinical Neurophysiology, University Medical Center Göttingen, Göttingen, Germany
| | - Martin Sommer
- Department of Clinical Neurophysiology, University Medical Center Göttingen, Göttingen, Germany.,Department of Neurology, University Medical Center Göttingen, Göttingen, Germany.,Department of Geriatrics, University Medical Center Göttingen, Göttingen, Germany
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14
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Zhang F, Daducci A, He Y, Schiavi S, Seguin C, Smith RE, Yeh CH, Zhao T, O'Donnell LJ. Quantitative mapping of the brain's structural connectivity using diffusion MRI tractography: A review. Neuroimage 2022; 249:118870. [PMID: 34979249 PMCID: PMC9257891 DOI: 10.1016/j.neuroimage.2021.118870] [Citation(s) in RCA: 124] [Impact Index Per Article: 41.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 12/03/2021] [Accepted: 12/31/2021] [Indexed: 12/13/2022] Open
Abstract
Diffusion magnetic resonance imaging (dMRI) tractography is an advanced imaging technique that enables in vivo reconstruction of the brain's white matter connections at macro scale. It provides an important tool for quantitative mapping of the brain's structural connectivity using measures of connectivity or tissue microstructure. Over the last two decades, the study of brain connectivity using dMRI tractography has played a prominent role in the neuroimaging research landscape. In this paper, we provide a high-level overview of how tractography is used to enable quantitative analysis of the brain's structural connectivity in health and disease. We focus on two types of quantitative analyses of tractography, including: 1) tract-specific analysis that refers to research that is typically hypothesis-driven and studies particular anatomical fiber tracts, and 2) connectome-based analysis that refers to research that is more data-driven and generally studies the structural connectivity of the entire brain. We first provide a review of methodology involved in three main processing steps that are common across most approaches for quantitative analysis of tractography, including methods for tractography correction, segmentation and quantification. For each step, we aim to describe methodological choices, their popularity, and potential pros and cons. We then review studies that have used quantitative tractography approaches to study the brain's white matter, focusing on applications in neurodevelopment, aging, neurological disorders, mental disorders, and neurosurgery. We conclude that, while there have been considerable advancements in methodological technologies and breadth of applications, there nevertheless remains no consensus about the "best" methodology in quantitative analysis of tractography, and researchers should remain cautious when interpreting results in research and clinical applications.
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Affiliation(s)
- Fan Zhang
- Brigham and Women's Hospital, Harvard Medical School, Boston, USA.
| | | | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China; Chinese Institute for Brain Research, Beijing, China
| | - Simona Schiavi
- Department of Computer Science, University of Verona, Verona, Italy
| | - Caio Seguin
- Melbourne Neuropsychiatry Centre, University of Melbourne and Melbourne Health, Melbourne, Australia; The University of Sydney, School of Biomedical Engineering, Sydney, Australia
| | - Robert E Smith
- The Florey Institute of Neuroscience and Mental Health, Melbourne, Australia; Florey Department of Neuroscience and Mental Health, The University of Melbourne, Melbourne, Australia
| | - Chun-Hung Yeh
- Institute for Radiological Research, Chang Gung University, Taoyuan, Taiwan; Department of Psychiatry, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
| | - Tengda Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
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15
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Chiang HL, Yang LK, Chen YJ, Hsu YC, Lo YC, Isaac Tseng WY, Shur-Fen Gau S. Altered White-matter Tract Property in Adults with Attention-Deficit Hyperactivity Disorder. Neuroscience 2022; 487:78-87. [DOI: 10.1016/j.neuroscience.2022.01.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Revised: 01/23/2022] [Accepted: 01/31/2022] [Indexed: 10/19/2022]
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16
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Lawrence KE, Nabulsi L, Santhalingam V, Abaryan Z, Villalon-Reina JE, Nir TM, Ba Gari I, Zhu AH, Haddad E, Muir AM, Laltoo E, Jahanshad N, Thompson PM. Age and sex effects on advanced white matter microstructure measures in 15,628 older adults: A UK biobank study. Brain Imaging Behav 2021; 15:2813-2823. [PMID: 34537917 PMCID: PMC8761720 DOI: 10.1007/s11682-021-00548-y] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/23/2021] [Indexed: 12/19/2022]
Abstract
A comprehensive characterization of the brain's white matter is critical for improving our understanding of healthy and diseased aging. Here we used diffusion-weighted magnetic resonance imaging (dMRI) to estimate age and sex effects on white matter microstructure in a cross-sectional sample of 15,628 adults aged 45-80 years old (47.6% male, 52.4% female). Microstructure was assessed using the following four models: a conventional single-shell model, diffusion tensor imaging (DTI); a more advanced single-shell model, the tensor distribution function (TDF); an advanced multi-shell model, neurite orientation dispersion and density imaging (NODDI); and another advanced multi-shell model, mean apparent propagator MRI (MAPMRI). Age was modeled using a data-driven statistical approach, and normative centile curves were created to provide sex-stratified white matter reference charts. Participant age and sex substantially impacted many aspects of white matter microstructure across the brain, with the advanced dMRI models TDF and NODDI detecting such effects the most sensitively. These findings and the normative reference curves provide an important foundation for the study of healthy and diseased brain aging.
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Affiliation(s)
- Katherine E Lawrence
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, University of Southern California, Marina del Rey, CA, USA
| | - Leila Nabulsi
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, University of Southern California, Marina del Rey, CA, USA
| | - Vigneshwaran Santhalingam
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, University of Southern California, Marina del Rey, CA, USA
| | - Zvart Abaryan
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, University of Southern California, Marina del Rey, CA, USA
| | - Julio E Villalon-Reina
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, University of Southern California, Marina del Rey, CA, USA
| | - Talia M Nir
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, University of Southern California, Marina del Rey, CA, USA
| | - Iyad Ba Gari
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, University of Southern California, Marina del Rey, CA, USA
| | - Alyssa H Zhu
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, University of Southern California, Marina del Rey, CA, USA
| | - Elizabeth Haddad
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, University of Southern California, Marina del Rey, CA, USA
| | - Alexandra M Muir
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, University of Southern California, Marina del Rey, CA, USA
| | - Emily Laltoo
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, University of Southern California, Marina del Rey, CA, USA
| | - Neda Jahanshad
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, University of Southern California, Marina del Rey, CA, USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, University of Southern California, Marina del Rey, CA, USA.
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17
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Yates K, Lång U, Peters EM, Wigman JTW, McNicholas F, Cannon M, DeVylder J, Ramsay H, Oh H, Kelleher I. Hallucinations in the general population across the adult lifespan: prevalence and psychopathologic significance. Br J Psychiatry 2021; 219:652-658. [PMID: 35048871 DOI: 10.1192/bjp.2021.100] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
BACKGROUND Community studies have found a relatively high prevalence of hallucinations, which are associated with a range of (psychotic and non-psychotic) mental disorders, as well as with suicidal ideation and behaviour. The literature on hallucinations in the general population has largely focused on adolescents and young adults. AIMS We aimed to explore the prevalence and psychopathologic significance of hallucinations across the adult lifespan. METHOD Using the 1993, 2000, 2007 and 2014 cross-sectional Adult Psychiatric Morbidity Survey series (N = 33 637), we calculated the prevalence of past-year hallucinations in the general population ages 16 to ≥90 years. We used logistic regression to examine the relationship between hallucinations and a range of mental disorders, suicidal ideation and suicide attempts. RESULTS The prevalence of past-year hallucinations varied across the adult lifespan, from a high of 7% in individuals aged 16-19 years, to a low of 3% in individuals aged ≥70 years. In all age groups, hallucinations were associated with increased risk for mental disorders, suicidal ideation and suicide attempts, but there was also evidence of significant age-related variation. In particular, hallucinations in older adults were less likely to be associated with a cooccurring mental disorder, suicidal ideation or suicide attempt compared with early adulthood and middle age. CONCLUSIONS Our findings highlight important life-course developmental features of hallucinations from early adulthood to old age.
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Affiliation(s)
- Kathryn Yates
- Department of Psychiatry, Royal College of Surgeons in Ireland, Ireland
| | - Ulla Lång
- Department of Psychiatry, Royal College of Surgeons in Ireland, Ireland
| | - Evyn M Peters
- Department of Psychiatry, University of Saskatchewan, Canada
| | - Johanna T W Wigman
- Department of Psychiatry, Interdisciplinary Center Psychopathology and Emotion Regulation (ICPE), University of Groningen, The Netherlands
| | - Fiona McNicholas
- School of Medicine and Medical Sciences, University College Dublin, Ireland; Lucena Clinic Services, Child and Adolescent Mental Health Service of St. John of God Community Services, Ireland; and Department of Child Psychiatry, Our Lady's Hospital for Sick Children, Ireland
| | - Mary Cannon
- Department of Psychiatry, Royal College of Surgeons in Ireland, Ireland
| | - Jordan DeVylder
- Graduate School of Social Service, Fordham University, New York, USA
| | - Hugh Ramsay
- Department of Psychiatry, Research Unit of Clinical Neuroscience, University of Oulu, Finland; and Department of Psychiatry, Trinity College, Ireland
| | - Hans Oh
- Suzanne Dworak Peck School of Social Work, University of Southern California, California, USA
| | - Ian Kelleher
- School of Medicine and Medical Sciences, University College Dublin, Ireland; Department of Psychiatry, Royal College of Surgeons in Ireland, Ireland; and Lucena Clinic Services, Child and Adolescent Mental Health Service of St. John of God Community Services, Ireland
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18
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Shirazi Y, Oghabian MA, Batouli SAH. Along-tract analysis of the white matter is more informative about brain ageing, compared to whole-tract analysis. Clin Neurol Neurosurg 2021; 211:107048. [PMID: 34826755 DOI: 10.1016/j.clineuro.2021.107048] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2021] [Revised: 10/25/2021] [Accepted: 11/14/2021] [Indexed: 11/30/2022]
Abstract
Diffusion Tensor Imaging (DTI) enabled the investigation of brain White Matter (WM), both qualitatively to study the macrostructure, and quantitatively to study the microstructure. The quantitative analyses are mostly performed at the whole-tract level, i.e., providing one measure of interest per tract; however, along-tract approaches may provide finer details of the quality of the WM tracts. In this study, using the DWI data collected from 40 young and 40 old individuals, we compared the DTI measures of FA, MD, AD, and RD, estimated by both whole-tract and along-tract approaches in 18 WM bundles, between the two groups. The results of the whole-tract quantitative analysis showed a statistically significant (p-FWER < 0.05) difference between the old and young groups in 6 tracts for FA, 8 tracts for MD, 1 tract for AD, and 7 tracts for RD. On the contrary, the along-tract approach showed differences between the two groups in 10 tracts for FA, 14 tracts for MD, 8 tracts for AD, and 11 tracts for RD. All the differences between the along-tract measures of the two groups had a large effect size (Cohen'd > 0.80). This study showed that the along-tract approach for the analysis of brain WM reveals changes in some WM tracts which had not shown any changes in the whole-tract approach, and therefore this finding emphasizes the utilization of the along-tract approach along with the whole-tract method for a more accurate study of the brain WM.
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Affiliation(s)
- Yasin Shirazi
- Medical Physics and Biomedical Engineering Department, Tehran University of Medical Sciences, Tehran, Iran
| | - Mohammad Ali Oghabian
- Medical Physics and Biomedical Engineering Department, Tehran University of Medical Sciences, Tehran, Iran; Neuroimaging and Analysis Group, Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran
| | - Seyed Amir Hossein Batouli
- Neuroimaging and Analysis Group, Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran; Department of Neuroscience and addiction Studies, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran.
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19
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White matter network disruption and cognitive correlates underlying impaired memory awareness in mild cognitive impairment. NEUROIMAGE-CLINICAL 2021; 30:102626. [PMID: 33780863 PMCID: PMC8039854 DOI: 10.1016/j.nicl.2021.102626] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Revised: 02/03/2021] [Accepted: 03/05/2021] [Indexed: 11/20/2022]
Abstract
Episodic memory deficits are insufficient for explaining memory anosognosia in MCI. Reasoning ability can be used as a basis for identifying memory anosognosia in MCI. Memory anosognosia in MCI is a white matter disconnection syndrome. Frontal-subcortical and callosal fibers are linked to memory anosognosia in MCI.
Decreased awareness of memory declines in mild cognitive impairment (MCI) has been linked to structural or functional changes in a wide gray matter network; however, the underlying white matter pathway correlations for the memory awareness deficits remain unknown. Moreover, consistent findings have not been obtained regarding the cognitive basis of disturbed awareness of memory declines in MCI. Due to the methodological drawbacks (e.g., correlational analysis without controlling confounders related to clinical status, a problem related to the representativeness of the control group) of previous studies on the aforementioned topic, further investigation is required. To addressed the research gaps, this study investigated white matter microstructural integrity and the cognitive correlates of memory awareness in 87 older adults with or without mild cognitive impairment (MCI). The patients with MCI and healthy controls (HCs) were divided into two subgroups, namely those with normal awareness (NA) and poor awareness (PA) for memory deficit, according to the discrepancy scores calculated from the differences between subjective and objective memory evaluations. Only the results for HCs with NA (HC-NA) were compared with those for the two MCI groups (i.e., MCI-NA and MCI-PA). The three groups were matched on demographic and clinical variables. An advanced diffusion imaging technique—diffusion spectrum imaging—was used to investigate the integrity of the white matter tract. The results revealed that although the HC-NA group outperformed the two MCI groups on several cognitive tests, the two MCI groups exhibited comparable performance across different neuropsychological tests, except for the test on reasoning ability. Compared with the other two groups, the MCI-PA group exhibited lower integrity in bilateral frontal-striatal fibers, left anterior thalamocortical radiations, and callosal fibers connecting bilateral inferior parietal regions. These results could not be explained by gray matter morphometric differences. Overall, the results indicated that mnemonic anosognosia was not sufficient to explain the memory awareness deficits observed in the patients with MCI. Our brain imaging findings also support the concept of anosognosia for memory deficit as a disconnection syndrome in MCI.
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