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Rangus I, Teghipco A, Newman‐Norlund S, Newman‐Norlund R, Rorden C, Riccardi N, Wilson S, Busby N, Wilmskoetter J, Nemati S, Bakos L, Fridriksson J, Bonilha L. The Influence of Structural Brain Changes on Cognition in the Context of Healthy Aging: Exploring Mediation Effects Through gBAT-The Graphical Brain Association Tool. Hum Brain Mapp 2024; 45:e70038. [PMID: 39382372 PMCID: PMC11462644 DOI: 10.1002/hbm.70038] [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: 05/31/2024] [Revised: 09/05/2024] [Accepted: 09/22/2024] [Indexed: 10/10/2024] Open
Abstract
The contribution of age-related structural brain changes to the well-established link between aging and cognitive decline is not fully defined. While both age-related regional brain atrophy and cognitive decline have been extensively studied, the specific mediating role of age-related regional brain atrophy on cognitive functions is unclear. This study introduces an open-source software tool with a graphical user interface that streamlines advanced whole-brain mediation analyses, enabling researchers to systematically explore how the brain acts as a mediator in relationships between various behavioral and health outcomes. The tool is showcased by investigating regional brain volume as a mediator to determine the contribution of age-related brain volume loss toward cognition in healthy aging. We analyzed regional brain volumes and cognitive testing data (Montreal Cognitive Assessment [MoCA]) from a cohort of 131 neurologically healthy adult participants (mean age 50 ± 20.8 years, range 20-79, 73% females) drawn from the Aging Brain Cohort Study at the University of South Carolina. Using our open-source tool developed for evaluating brain-behavior associations across the brain and optimized for exploring mediation effects, we conducted a series of mediation analyses using participant age as the predictor variable, total MoCA and MoCA subtest scores as the outcome variables, and regional brain volume as potential mediators. Age-related atrophy within specific anatomical networks was found to mediate the relationship between age and cognition across multiple cognitive domains. Specifically, atrophy in bilateral frontal, parietal, and occipital areas, along with widespread subcortical regions mediated the effect of age on total MoCA scores. Various MoCA subscores were influenced by age through atrophy in distinct brain regions. These involved prefrontal regions, sensorimotor cortex, and parieto-occipital areas for executive function subscores, prefrontal and temporo-occipital regions, along with the caudate nucleus for attention and concentration subscores, frontal and parieto-occipital areas, alongside connecting subcortical areas such as the optic tract for visuospatial subscores and frontoparietal areas for language subscores. Brain-based mediation analysis offers a causal framework for evaluating the mediating role of brain structure on the relationship between age and cognition and provides a more nuanced understanding of cognitive aging than previously possible. By validating the applicability and effectiveness of this approach and making it openly available to the scientific community, we facilitate the exploration of causal mechanisms between variables mediated by the brain.
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Affiliation(s)
- Ida Rangus
- Communication Sciences and DisordersUniversity of South CarolinaColumbiaSouth CarolinaUSA
- Center for Stroke Research BerlinCharité‐Universitätsmedizin BerlinBerlinGermany
| | - Alex Teghipco
- Communication Sciences and DisordersUniversity of South CarolinaColumbiaSouth CarolinaUSA
| | - Sarah Newman‐Norlund
- Communication Sciences and DisordersUniversity of South CarolinaColumbiaSouth CarolinaUSA
| | | | - Chris Rorden
- Department of PsychologyUniversity of South CarolinaColumbiaSouth CarolinaUSA
| | - Nicholas Riccardi
- Communication Sciences and DisordersUniversity of South CarolinaColumbiaSouth CarolinaUSA
| | - Sarah Wilson
- Communication Sciences and DisordersUniversity of South CarolinaColumbiaSouth CarolinaUSA
- Linguistics ProgramUniversity of South CarolinaColumbiaSouth CarolinaUSA
| | - Natalie Busby
- Communication Sciences and DisordersUniversity of South CarolinaColumbiaSouth CarolinaUSA
| | - Janina Wilmskoetter
- Department of Rehabilitation Sciences, College of Health ProfessionsMedical University of South CarolinaCharlestonSouth CarolinaUSA
| | - Samaneh Nemati
- Communication Sciences and DisordersUniversity of South CarolinaColumbiaSouth CarolinaUSA
| | - Lumi Bakos
- Arnold School of Public HealthUniversity of South CarolinaColumbiaSouth CarolinaUSA
| | - Julius Fridriksson
- Communication Sciences and DisordersUniversity of South CarolinaColumbiaSouth CarolinaUSA
| | - Leonardo Bonilha
- School of Medicine ColumbiaUniversity of South CarolinaColumbiaSouth CarolinaUSA
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Aleksic S, Fleysher R, Weiss EF, Tal N, Darby T, Blumen HM, Vazquez J, Ye KQ, Gao T, Siegel SM, Barzilai N, Lipton ML, Milman S. Hypothalamic MRI-derived microstructure is associated with neurocognitive aging in humans. Neurobiol Aging 2024; 141:102-112. [PMID: 38850591 PMCID: PMC11295133 DOI: 10.1016/j.neurobiolaging.2024.05.018] [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: 08/08/2023] [Revised: 05/17/2024] [Accepted: 05/31/2024] [Indexed: 06/10/2024]
Abstract
The hypothalamus regulates homeostasis across the lifespan and is emerging as a regulator of aging. In murine models, aging-related changes in the hypothalamus, including microinflammation and gliosis, promote accelerated neurocognitive decline. We investigated relationships between hypothalamic microstructure and features of neurocognitive aging, including cortical thickness and cognition, in a cohort of community-dwelling older adults (age range 65-97 years, n=124). Hypothalamic microstructure was evaluated with two magnetic resonance imaging diffusion metrics: mean diffusivity (MD) and fractional anisotropy (FA), using a novel image processing pipeline. Hypothalamic MD was cross-sectionally positively associated with age and it was negatively associated with cortical thickness. Hypothalamic FA, independent of cortical thickness, was cross-sectionally positively associated with neurocognitive scores. An exploratory analysis of longitudinal neurocognitive performance suggested that lower hypothalamic FA may predict cognitive decline. No associations between hypothalamic MD, age, and cortical thickness were identified in a younger control cohort (age range 18-63 years, n=99). To our knowledge, this is the first study to demonstrate that hypothalamic microstructure is associated with features of neurocognitive aging in humans.
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Affiliation(s)
- Sandra Aleksic
- Department of Medicine, Institute for Aging Research, Albert Einstein College of Medicine, Bronx, NY, United States.
| | - Roman Fleysher
- Department of Radiology, Columbia University Irving Medical Center, New York, NY, United States; Department of Radiology, Albert Einstein College of Medicine, Gruss Magnetic Resonance Research Center, Bronx, NY, United States
| | - Erica F Weiss
- Department of Neurology, Albert Einstein College of Medicine, Bronx, NY, United States
| | - Noa Tal
- Department of Medicine, Cedars-Sinai, Los Angeles, CA, United States
| | - Timothy Darby
- Albert Einstein College of Medicine, Bronx, NY, United States
| | - Helena M Blumen
- Department of Neurology, Albert Einstein College of Medicine, Bronx, NY, United States; Department of Medicine, Albert Einstein College of Medicine, Bronx, NY, United States
| | - Juan Vazquez
- Department of Internal Medicine, John Hopkins University, Baltimore, MD, United States
| | - Kenny Q Ye
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, United States; Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, NY, United States
| | - Tina Gao
- Department of Medicine, Institute for Aging Research, Albert Einstein College of Medicine, Bronx, NY, United States
| | - Shira M Siegel
- Department of Radiology, Columbia University Irving Medical Center, New York, NY, United States
| | - Nir Barzilai
- Department of Medicine, Institute for Aging Research, Albert Einstein College of Medicine, Bronx, NY, United States; Department of Genetics, Albert Einstein College of Medicine, Bronx, NY, United States
| | - Michael L Lipton
- Department of Radiology, Columbia University Irving Medical Center, New York, NY, United States; Department of Biomedical Engineering, Columbia University, New York, NY, United States
| | - Sofiya Milman
- Department of Medicine, Institute for Aging Research, Albert Einstein College of Medicine, Bronx, NY, United States; Department of Genetics, Albert Einstein College of Medicine, Bronx, NY, United States
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Lee HK, Basak C, Grant SJ, Ray NR, Skolasinska PA, Oehler C, Qin S, Sun A, Smith ET, Sherard GH, Rivera-Dompenciel A, Merzenich M, Voss MW. The Effects of Computerized Cognitive Training in Older Adults' Cognitive Performance and Biomarkers of Structural Brain Aging. J Gerontol B Psychol Sci Soc Sci 2024; 79:gbae075. [PMID: 38686621 PMCID: PMC11165429 DOI: 10.1093/geronb/gbae075] [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: 07/24/2023] [Indexed: 05/02/2024] Open
Abstract
OBJECTIVES Cognitive training (CT) has been investigated as a means of delaying age-related cognitive decline in older adults. However, its impact on biomarkers of age-related structural brain atrophy has rarely been investigated, leading to a gap in our understanding of the linkage between improvements in cognition and brain plasticity. This study aimed to explore the impact of CT on cognitive performance and brain structure in older adults. METHODS One hundred twenty-four cognitively normal older adults recruited from 2 study sites were randomly assigned to either an adaptive CT (n = 60) or a casual game training (active control, AC, n = 64). RESULTS After 10 weeks of training, CT participants showed greater improvements in the overall cognitive composite score (Cohen's d = 0.66, p < .01) with nonsignificant benefits after 6 months from the completion of training (Cohen's d = 0.36, p = .094). The CT group showed significant maintenance of the caudate volume as well as significant maintained fractional anisotropy in the left internal capsule and in left superior longitudinal fasciculus compared to the AC group. The AC group displayed an age-related decrease in these metrics of brain structure. DISCUSSION Results from this multisite clinical trial demonstrate that the CT intervention improves cognitive performance and helps maintain caudate volume and integrity of white matter regions that are associated with cognitive control, adding to our understanding of the changes in brain structure contributing to changes in cognitive performance from adaptive CT. CLINICAL TRIAL REGISTRATION NCT03197454.
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Affiliation(s)
- Hyun Kyu Lee
- Department of Research and Development, Posit Science Corporation, San Francisco, California, USA
| | | | - Sarah-Jane Grant
- Department of Research and Development, Posit Science Corporation, San Francisco, California, USA
| | - Nicholas R Ray
- Department of Psychology, University of Texas at Dallas, Dallas, Texas, USA
| | | | - Chris Oehler
- Department of Psychological and Brain Sciences, University of Iowa, Iowa City, Iowa, USA
| | - Shuo Qin
- Department of Psychology, University of Texas at Dallas, Dallas, Texas, USA
| | - Andrew Sun
- Department of Psychology, University of Texas at Dallas, Dallas, Texas, USA
| | - Evan T Smith
- Department of Psychology, University of Texas at Dallas, Dallas, Texas, USA
| | - G Hulon Sherard
- Department of Psychology, University of Texas at Dallas, Dallas, Texas, USA
| | | | - Mike Merzenich
- Department of Research and Development, Posit Science Corporation, San Francisco, California, USA
| | - Michelle W Voss
- Department of Psychological and Brain Sciences, University of Iowa, Iowa City, Iowa, USA
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Hu M, Xu F, Liu S, Yao Y, Xia Q, Zhu C, Zhang X, Tang H, Qaiser Z, Liu S, Tang Y. Aging pattern of the brainstem based on volumetric measurement and optimized surface shape analysis. Brain Imaging Behav 2024; 18:396-411. [PMID: 38155336 DOI: 10.1007/s11682-023-00840-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] [Accepted: 12/11/2023] [Indexed: 12/30/2023]
Abstract
The brainstem, a small and crucial structure, is connected to the cerebrum, spinal cord, and cerebellum, playing a vital role in regulating autonomic functions, transmitting motor and sensory information, and modulating cognitive processes, emotions, and consciousness. While previous research has indicated that changes in brainstem anatomy can serve as a biomarker for aging and neurodegenerative diseases, the structural changes that occur in the brainstem during normal aging remain unclear. This study aimed to examine the age- and sex-related differences in the global and local structural measures of the brainstem in 187 healthy adults (ranging in age from 18 to 70 years) using structural magnetic resonance imaging. The findings showed a significant negative age effect on the volume of the two major components of the brainstem: the medulla oblongata and midbrain. The shape analysis revealed that atrophy primarily occurs in specific structures, such as the pyramid, cerebral peduncle, superior and inferior colliculi. Surface area and shape analysis showed a trend of flattening in the aging brainstem. There were no significant differences between the sexes or sex-by-age interactions in brainstem structural measures. These findings provide a systematic description of age associations with brainstem structures in healthy adults and may provide a reference for future research on brain aging and neurodegenerative diseases.
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Affiliation(s)
- Minqi Hu
- Department of Anatomy and Neurobiology, Research Center for Sectional and Imaging Anatomy, Shandong Provincial Key Laboratory of Mental Disorder, Shandong Key Laboratory of Digital Human and Clinical Anatomy, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China
- Institute of Brain and Brain-Inspired Science, Shandong University, Jinan, 250012, Shandong, China
| | - Feifei Xu
- Department of Anatomy and Neurobiology, Research Center for Sectional and Imaging Anatomy, Shandong Provincial Key Laboratory of Mental Disorder, Shandong Key Laboratory of Digital Human and Clinical Anatomy, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China
- Institute of Brain and Brain-Inspired Science, Shandong University, Jinan, 250012, Shandong, China
| | - Shizhou Liu
- Department of Anatomy and Neurobiology, Research Center for Sectional and Imaging Anatomy, Shandong Provincial Key Laboratory of Mental Disorder, Shandong Key Laboratory of Digital Human and Clinical Anatomy, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China
- Institute of Brain and Brain-Inspired Science, Shandong University, Jinan, 250012, Shandong, China
| | - Yuan Yao
- Department of Anatomy and Neurobiology, Research Center for Sectional and Imaging Anatomy, Shandong Provincial Key Laboratory of Mental Disorder, Shandong Key Laboratory of Digital Human and Clinical Anatomy, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China
- Institute of Brain and Brain-Inspired Science, Shandong University, Jinan, 250012, Shandong, China
| | - Qing Xia
- Department of Anatomy and Neurobiology, Research Center for Sectional and Imaging Anatomy, Shandong Provincial Key Laboratory of Mental Disorder, Shandong Key Laboratory of Digital Human and Clinical Anatomy, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China
- Institute of Brain and Brain-Inspired Science, Shandong University, Jinan, 250012, Shandong, China
| | - Caiting Zhu
- Department of Anatomy and Neurobiology, Research Center for Sectional and Imaging Anatomy, Shandong Provincial Key Laboratory of Mental Disorder, Shandong Key Laboratory of Digital Human and Clinical Anatomy, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China
- Institute of Brain and Brain-Inspired Science, Shandong University, Jinan, 250012, Shandong, China
| | - Xinwen Zhang
- Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China
| | - Haiyan Tang
- Department of Anatomy and Neurobiology, Research Center for Sectional and Imaging Anatomy, Shandong Provincial Key Laboratory of Mental Disorder, Shandong Key Laboratory of Digital Human and Clinical Anatomy, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China
- Institute of Brain and Brain-Inspired Science, Shandong University, Jinan, 250012, Shandong, China
| | - Zubair Qaiser
- Department of Anatomy and Neurobiology, Research Center for Sectional and Imaging Anatomy, Shandong Provincial Key Laboratory of Mental Disorder, Shandong Key Laboratory of Digital Human and Clinical Anatomy, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China
- Institute of Brain and Brain-Inspired Science, Shandong University, Jinan, 250012, Shandong, China
| | - Shuwei Liu
- Department of Anatomy and Neurobiology, Research Center for Sectional and Imaging Anatomy, Shandong Provincial Key Laboratory of Mental Disorder, Shandong Key Laboratory of Digital Human and Clinical Anatomy, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China
- Institute of Brain and Brain-Inspired Science, Shandong University, Jinan, 250012, Shandong, China
| | - Yuchun Tang
- Department of Anatomy and Neurobiology, Research Center for Sectional and Imaging Anatomy, Shandong Provincial Key Laboratory of Mental Disorder, Shandong Key Laboratory of Digital Human and Clinical Anatomy, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China.
- Institute of Brain and Brain-Inspired Science, Shandong University, Jinan, 250012, Shandong, China.
- Advanced Medical Research Institute, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China.
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5
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Arleo A, Bareš M, Bernard JA, Bogoian HR, Bruchhage MMK, Bryant P, Carlson ES, Chan CCH, Chen LK, Chung CP, Dotson VM, Filip P, Guell X, Habas C, Jacobs HIL, Kakei S, Lee TMC, Leggio M, Misiura M, Mitoma H, Olivito G, Ramanoël S, Rezaee Z, Samstag CL, Schmahmann JD, Sekiyama K, Wong CHY, Yamashita M, Manto M. Consensus Paper: Cerebellum and Ageing. CEREBELLUM (LONDON, ENGLAND) 2024; 23:802-832. [PMID: 37428408 PMCID: PMC10776824 DOI: 10.1007/s12311-023-01577-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 06/08/2023] [Indexed: 07/11/2023]
Abstract
Given the key roles of the cerebellum in motor, cognitive, and affective operations and given the decline of brain functions with aging, cerebellar circuitry is attracting the attention of the scientific community. The cerebellum plays a key role in timing aspects of both motor and cognitive operations, including for complex tasks such as spatial navigation. Anatomically, the cerebellum is connected with the basal ganglia via disynaptic loops, and it receives inputs from nearly every region in the cerebral cortex. The current leading hypothesis is that the cerebellum builds internal models and facilitates automatic behaviors through multiple interactions with the cerebral cortex, basal ganglia and spinal cord. The cerebellum undergoes structural and functional changes with aging, being involved in mobility frailty and related cognitive impairment as observed in the physio-cognitive decline syndrome (PCDS) affecting older, functionally-preserved adults who show slowness and/or weakness. Reductions in cerebellar volume accompany aging and are at least correlated with cognitive decline. There is a strongly negative correlation between cerebellar volume and age in cross-sectional studies, often mirrored by a reduced performance in motor tasks. Still, predictive motor timing scores remain stable over various age groups despite marked cerebellar atrophy. The cerebello-frontal network could play a significant role in processing speed and impaired cerebellar function due to aging might be compensated by increasing frontal activity to optimize processing speed in the elderly. For cognitive operations, decreased functional connectivity of the default mode network (DMN) is correlated with lower performances. Neuroimaging studies highlight that the cerebellum might be involved in the cognitive decline occurring in Alzheimer's disease (AD), independently of contributions of the cerebral cortex. Grey matter volume loss in AD is distinct from that seen in normal aging, occurring initially in cerebellar posterior lobe regions, and is associated with neuronal, synaptic and beta-amyloid neuropathology. Regarding depression, structural imaging studies have identified a relationship between depressive symptoms and cerebellar gray matter volume. In particular, major depressive disorder (MDD) and higher depressive symptom burden are associated with smaller gray matter volumes in the total cerebellum as well as the posterior cerebellum, vermis, and posterior Crus I. From the genetic/epigenetic standpoint, prominent DNA methylation changes in the cerebellum with aging are both in the form of hypo- and hyper-methylation, and the presumably increased/decreased expression of certain genes might impact on motor coordination. Training influences motor skills and lifelong practice might contribute to structural maintenance of the cerebellum in old age, reducing loss of grey matter volume and therefore contributing to the maintenance of cerebellar reserve. Non-invasive cerebellar stimulation techniques are increasingly being applied to enhance cerebellar functions related to motor, cognitive, and affective operations. They might enhance cerebellar reserve in the elderly. In conclusion, macroscopic and microscopic changes occur in the cerebellum during the lifespan, with changes in structural and functional connectivity with both the cerebral cortex and basal ganglia. With the aging of the population and the impact of aging on quality of life, the panel of experts considers that there is a huge need to clarify how the effects of aging on the cerebellar circuitry modify specific motor, cognitive, and affective operations both in normal subjects and in brain disorders such as AD or MDD, with the goal of preventing symptoms or improving the motor, cognitive, and affective symptoms.
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Affiliation(s)
- Angelo Arleo
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, 17 rue Moreau, F-75012, Paris, France
| | - Martin Bareš
- First Department of Neurology, Faculty of Medicine, Masaryk University and St. Anne's Teaching Hospital, Brno, Czech Republic
- Department of Neurology, School of Medicine, University of Minnesota, Minneapolis, USA
| | - Jessica A Bernard
- Department of Psychological and Brain Sciences, Texas A&M University, 4235 TAMU, College Station, TX, 77843, USA
- Texas A&M Institute for Neuroscience, Texas A&M University, College Station, TX, USA
| | - Hannah R Bogoian
- Department of Psychology, Georgia State University, Atlanta, GA, USA
| | - Muriel M K Bruchhage
- Department of Psychology, Stavanger University, Institute of Social Sciences, Kjell Arholms Gate 41, 4021, Stavanger, Norway
- King's College London, Institute of Psychiatry, Psychology and Neuroscience, Centre for Neuroimaging Sciences, Box 89, De Crespigny Park, London, PO, SE5 8AF, UK
- Rhode Island Hospital, Department for Diagnostic Imaging, 1 Hoppin St, Providence, RI, 02903, USA
- Department of Paediatrics, Warren Alpert Medical School of Brown University, 222 Richmond St, Providence, RI, 02903, USA
| | - Patrick Bryant
- Freie Universität Berlin, Fachbereich Mathematik und Informatik, Arnimallee 12, 14195, Berlin, Germany
| | - Erik S Carlson
- Department of Psychiatry and Behavioural Sciences, University of Washington, Seattle, WA, USA
- Geriatric Research, Education and Clinical Center, Veteran's Affairs Medical Center, Puget Sound, Seattle, WA, USA
| | - Chetwyn C H Chan
- Department of Psychology, The Education University of Hong Kong, New Territories, Tai Po, Hong Kong, China
| | - Liang-Kung Chen
- Center for Healthy Longevity and Aging Sciences, National Yang Ming Chiao Tung University College of Medicine, Taipei, Taiwan
- Center for Geriatric and Gerontology, Taipei Veterans General Hospital, Taipei, Taiwan
- Taipei Municipal Gan-Dau Hospital (managed by Taipei Veterans General Hospital), Taipei, Taiwan
| | - Chih-Ping Chung
- Center for Healthy Longevity and Aging Sciences, National Yang Ming Chiao Tung University College of Medicine, Taipei, Taiwan
- Department of Neurology, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Vonetta M Dotson
- Department of Psychology, Georgia State University, Atlanta, GA, USA
- Gerontology Institute, Georgia State University, Atlanta, GA, USA
| | - Pavel Filip
- Department of Neurology, Charles University, First Faculty of Medicine and General University Hospital, Prague, Czech Republic
- Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, MN, USA
| | - Xavier Guell
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Laboratory for Neuroanatomy and Cerebellar Neurobiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Christophe Habas
- CHNO Des Quinze-Vingts, INSERM-DGOS CIC 1423, 28 rue de Charenton, 75012, Paris, France
- Université Versailles St Quentin en Yvelines, Paris, France
| | - Heidi I L Jacobs
- School for Mental Health and Neuroscience, Alzheimer Centre Limburg, Maastricht University, PO BOX 616, 6200, MD, Maastricht, The Netherlands
- Faculty of Psychology and Neuroscience, Department of Cognitive Neuroscience, Maastricht University, PO BOX 616, 6200, MD, Maastricht, The Netherlands
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | | | - Tatia M C Lee
- State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong, China
- Laboratory of Neuropsychology and Human Neuroscience, Department of Psychology, The University of Hong Kong, Hong Kong, China
| | - Maria Leggio
- Department of Psychology, Sapienza University of Rome, Rome, Italy
- Ataxia Laboratory, I.R.C.C.S. Santa Lucia Foundation, Rome, Italy
| | - Maria Misiura
- Department of Psychology, Georgia State University, Atlanta, GA, USA
| | - Hiroshi Mitoma
- Department of Medical Education, Tokyo Medical University, Tokyo, Japan
| | - Giusy Olivito
- Department of Psychology, Sapienza University of Rome, Rome, Italy
- Ataxia Laboratory, I.R.C.C.S. Santa Lucia Foundation, Rome, Italy
| | - Stephen Ramanoël
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, 17 rue Moreau, F-75012, Paris, France
- Université Côte d'Azur, LAMHESS, Nice, France
| | - Zeynab Rezaee
- Noninvasive Neuromodulation Unit, Experimental Therapeutics & Pathophysiology Branch, National Institute of Mental Health, NIH, Bethesda, USA
| | - Colby L Samstag
- Department of Psychiatry and Behavioural Sciences, University of Washington, Seattle, WA, USA
- Geriatric Research, Education and Clinical Center, Veteran's Affairs Medical Center, Puget Sound, Seattle, WA, USA
| | - Jeremy D Schmahmann
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Laboratory for Neuroanatomy and Cerebellar Neurobiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Ataxia Center, Cognitive Behavioural neurology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Kaoru Sekiyama
- Graduate School of Advanced Integrated Studies in Human Survivability, Kyoto University, Kyoto, Japan
| | - Clive H Y Wong
- Department of Psychology, The Education University of Hong Kong, New Territories, Tai Po, Hong Kong, China
| | - Masatoshi Yamashita
- Research Center for Child Mental Development, University of Fukui, Fukui, Japan
- United Graduate School of Child Development, Osaka University, Kanazawa University, Hamamatsu University School of Medicine, Chiba University and University of Fukui, Osaka, Japan
| | - Mario Manto
- Service de Neurologie, Médiathèque Jean Jacquy, CHU-Charleroi, Charleroi, Belgium.
- Service des Neurosciences, University of Mons, Mons, Belgium.
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6
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Badji A, Cedres N, Muehlboeck JS, Khan W, Dhollander T, Barroso J, Ferreira D, Westman E. In vivo microstructural heterogeneity of white matter and cognitive correlates in aging using tissue compositional analysis of diffusion magnetic resonance imaging. Hum Brain Mapp 2024; 45:e26618. [PMID: 38414286 PMCID: PMC10899800 DOI: 10.1002/hbm.26618] [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: 08/09/2023] [Revised: 12/03/2023] [Accepted: 12/24/2023] [Indexed: 02/29/2024] Open
Abstract
BACKGROUND Age-related cognitive decline is linked to changes in the brain, particularly the deterioration of white matter (WM) microstructure that accelerates after the age of 60. WM deterioration is associated with mild cognitive impairment and dementia, but the origin and role of white matter signal abnormalities (WMSA) seen in standard MRI remain debated due to their heterogeneity. This study explores the potential of single-shell 3-tissue constrained spherical deconvolution (SS3T-CSD), a novel technique that models diffusion data in terms of gray matter (TG ), white matter (Tw ), and cerebrospinal fluid (TC ), to differentiate WMSA from normal-appearing white matter and better understand the interplay between changes in WM microstructure and decline in cognition. METHODS A total of 189 individuals from the GENIC cohort were included. MRI data, including T1-weighted and diffusion images, were obtained. Preprocessing steps were performed on the diffusion MRI data, followed by the SS3T-CSD. WMSA were segmented using FreeSurfer. Statistical analyses were conducted to assess the association between age, WMSA volume, 3-tissue signal fractions (Tw , TG , and TC ), and neuropsychological variables. RESULTS Participants above 60 years old showed worse cognitive performance and processing speed compared to those below 60 (p < .001). Age was negatively associated with Tw in normal-appearing white matter (p < .001) and positively associated with TG in both WMSA (p < .01) and normal-appearing white matter (p < .001). Age was also significantly associated with WMSA volume (p < .001). Higher processing speed was associated with lower Tw and higher TG , in normal-appearing white matter (p < .01 and p < .001, respectively), as well as increased WMSA volume (p < .001). Similarly, lower MMSE scores correlated with lower Tw and higher TG in normal-appearing white matter (p < .05). High cholesterol and hypertension were associated with higher WMSA volume (p < .05). CONCLUSION The microstructural heterogeneity within normal-appearing white matter and WMSA is associated with increasing age and cognitive variation, in cognitively unimpaired individuals. Furthermore, the 3-tissue signal fractions are more specific to potential white matter alterations than conventional MRI measures such as WMSA volume. These findings also support the view that the WMSA volumes may be more influenced by vascular risk factors than the 3-tissue metrics. Finally, the 3-tissue metrics were able to capture associations with cognitive tests and therefore capable of capturing subtle pathological changes in the brain in individuals who are still within the normal range of cognitive performance.
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Affiliation(s)
- Atef Badji
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
- Theme Inflammation and Aging, Karolinska University Hospital, Stockholm, Sweden
| | - Nira Cedres
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
- Facultad de Ciencias de la Salud, Universidad Fernando Pessoa Canarias, Las Palmas de Gran Canaria, España
| | - J-Sebastian Muehlboeck
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Wasim Khan
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Thijs Dhollander
- Murdoch Children's Research Institute, Melbourne, Victoria, Australia
| | - Jose Barroso
- Facultad de Ciencias de la Salud, Universidad Fernando Pessoa Canarias, Las Palmas de Gran Canaria, España
| | - Daniel Ferreira
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
- Facultad de Ciencias de la Salud, Universidad Fernando Pessoa Canarias, Las Palmas de Gran Canaria, España
| | - Eric Westman
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
- Department of Neuroimaging, Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
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7
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Aghamohammadi-Sereshki A, Pietrasik W, Malykhin NV. Aging, cingulate cortex, and cognition: insights from structural MRI, emotional recognition, and theory of mind. Brain Struct Funct 2024:10.1007/s00429-023-02753-5. [PMID: 38305874 DOI: 10.1007/s00429-023-02753-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 12/12/2023] [Indexed: 02/03/2024]
Abstract
The cingulate cortex is a limbic structure involved in multiple functions, including emotional processing, pain, cognition, memory, and spatial orientation. The main goal of this structural Magnetic Resonance Imaging (MRI) study was to investigate whether age affects the cingulate cortex uniformly across its anteroposterior dimensions and determine if the effects of age differ based on sex, hemisphere, and regional cingulate anatomy, in a large cohort of healthy individuals across the adult lifespan. The second objective aimed to explore whether the decline in emotional recognition accuracy and Theory of Mind (ToM) is linked to the potential age-related reductions in the pregenual anterior cingulate (ACC) and anterior midcingulate (MCC) cortices. We recruited 126 healthy participants (18-85 years) for this study. MRI datasets were acquired on a 4.7 T system. The cingulate cortex was manually segmented into the pregenual ACC, anterior MCC, posterior MCC, and posterior cingulate cortex (PCC). We observed negative relationships between the presence and length of the superior cingulate gyrus and bilateral volumes of pregenual ACC and anterior MCC. Age showed negative effects on the volume of all cingulate cortical subregions bilaterally except for the right anterior MCC. Most of the associations between age and the cingulate subregional volumes were linear. We did not find a significant effect of sex on cingulate cortical volumes. However, stronger effects of age were observed in men compared to women. This study also demonstrated that performance on an emotional recognition task was linked to pregenual ACC volume, whist the ToM capabilities were related to the size of pregenual ACC and anterior MCC. These results suggest that the cingulate cortex contributes to emotional recognition ability and ToM across the adult lifespan.
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Affiliation(s)
| | - Wojciech Pietrasik
- Department of Psychiatry, Faculty of Medicine & Dentistry, University of Alberta, Edmonton, AB, T6G 2V2, Canada
| | - Nikolai V Malykhin
- Neuroscience and Mental Health Institute, University of Alberta, Edmonton, AB, Canada.
- Department of Psychiatry, Faculty of Medicine & Dentistry, University of Alberta, Edmonton, AB, T6G 2V2, Canada.
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8
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Guan S, Jiang R, Meng C, Biswal B. Brain age prediction across the human lifespan using multimodal MRI data. GeroScience 2024; 46:1-20. [PMID: 37733220 PMCID: PMC10828281 DOI: 10.1007/s11357-023-00924-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 08/22/2023] [Indexed: 09/22/2023] Open
Abstract
Measuring differences between an individual's age and biological age with biological information from the brain have the potential to provide biomarkers of clinically relevant neurological syndromes that arise later in human life. To explore the effect of multimodal brain magnetic resonance imaging (MRI) features on the prediction of brain age, we investigated how multimodal brain imaging data improved age prediction from more imaging features of structural or functional MRI data by using partial least squares regression (PLSR) and longevity data sets (age 6-85 years). First, we found that the age-predicted values for each of these ten features ranged from high to low: cortical thickness (R = 0.866, MAE = 7.904), all seven MRI features (R = 0.8594, MAE = 8.24), four features in structural MRI (R = 0.8591, MAE = 8.24), fALFF (R = 0.853, MAE = 8.1918), gray matter volume (R = 0.8324, MAE = 8.931), three rs-fMRI feature (R = 0.7959, MAE = 9.744), mean curvature (R = 0.7784, MAE = 10.232), ReHo (R = 0.7833, MAE = 10.122), ALFF (R = 0.7517, MAE = 10.844), and surface area (R = 0.719, MAE = 11.33). In addition, the significance of the volume and size of brain MRI data in predicting age was also studied. Second, our results suggest that all multimodal imaging features, except cortical thickness, improve brain-based age prediction. Third, we found that the left hemisphere contributed more to the age prediction, that is, the left hemisphere showed a greater weight in the age prediction than the right hemisphere. Finally, we found a nonlinear relationship between the predicted age and the amount of MRI data. Combined with multimodal and lifespan brain data, our approach provides a new perspective for chronological age prediction and contributes to a better understanding of the relationship between brain disorders and aging.
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Affiliation(s)
- Sihai Guan
- College of Electronic and Information, Southwest Minzu University, Chengdu, 610041, China.
- Key Laboratory of Electronic and Information Engineering, State Ethnic Affairs Commission, Chengdu, 610041, China.
| | - Runzhou Jiang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China
- Medical Equipment Department, Xiangyang No. 1 People's Hospital, Xiangyang, 441000, China
| | - Chun Meng
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Bharat Biswal
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China.
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, 07102, USA.
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9
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Köhncke Y, Kühn S, Düzel S, Sander MC, Brandmaier AM, Lindenberger U. Grey-matter structure in cortical and limbic regions correlates with general cognitive ability in old age. AGING BRAIN 2023; 5:100103. [PMID: 38186748 PMCID: PMC10770753 DOI: 10.1016/j.nbas.2023.100103] [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: 06/15/2023] [Revised: 11/21/2023] [Accepted: 11/22/2023] [Indexed: 01/09/2024] Open
Abstract
According to the maintenance hypothesis (Nyberg et al., 2012), structural integrity of the brain's grey matter helps to preserve cognitive functioning into old age. A corollary of this hypothesis that can be tested in cross-sectional data is that grey-matter structural integrity and general cognitive ability are positively associated in old age. Building on Köhncke et al. (2021), who found that region-specific latent factors of grey-matter integrity are positively associated with episodic memory ability among older adults, we examine associations between general factors of grey-matter integrity and a general factor of cognitive ability in a cross-sectional sample of 1466 participants aged 60-88 years, 319 of whom contributed imaging data. Indicator variables based on T1-weighted images (voxel-based morphometry, VBM), magnetization-transfer imaging (MT), and diffusion tensor imaging-derived mean diffusivity (MD) had sufficient portions of variance in common to establish latent factors of grey-matter structure for a comprehensive set of regions of interest (ROI). Individual differences in grey-matter factors were positively correlated across neocortical and limbic areas, allowing for the definition of second-order, general factors for neocortical and limbic ROI, respectively. Both general grey-matter factors were positively correlated with general cognitive ability. For the basal ganglia, the three modality-specific indicators showed heterogenous loading patterns, and no reliable associations of the general grey-matter factor to general cognitive ability were found. To provide more direct tests of the maintenance hypothesis, we recommend applying the present structural modeling approach to longitudinal data, thereby enhancing the physiological validity of latent constructs of brain structure.
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Affiliation(s)
- Ylva Köhncke
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany
| | - Simone Kühn
- Lise Meitner Group for Environmental Neuroscience, Max Planck Institute for Human Development, Berlin, Germany
- Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Germany
| | - Sandra Düzel
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany
| | - Myriam C. Sander
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany
| | - Andreas M. Brandmaier
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, London, UK, & Berlin, Germany
- Department of Psychology, MSB Medical School Berlin, Berlin, Germany
| | - Ulman Lindenberger
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, London, UK, & Berlin, Germany
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10
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Paitel ER, Nielson KA. Cerebellar EEG source localization reveals age-related compensatory activity moderated by genetic risk for Alzheimer's disease. Psychophysiology 2023; 60:e14395. [PMID: 37493042 DOI: 10.1111/psyp.14395] [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: 11/21/2022] [Revised: 06/24/2023] [Accepted: 07/03/2023] [Indexed: 07/27/2023]
Abstract
The apolipoprotein-E (APOE) ε4 allele is the greatest genetic risk factor for late-onset Alzheimer's disease (AD), but alone it is not sufficiently predictive. Because neuropathological changes associated with AD begin decades before cognitive symptoms, neuroimaging of healthy, cognitively intact ε4 carriers (ε4+) may enable early characterization of patterns associated with risk for future decline. Research in the cerebral cortex highlights a period of compensatory recruitment in elders and ε4+, which serves to maintain cognitive functioning. Yet, AD-related changes may occur even earlier in the cerebellum. Advances in electroencephalography (EEG) source localization now allow effective modeling of cerebellar activity. Importantly, healthy aging and AD are associated with declines in both cerebellar functions and executive functioning (EF). However, it is not known whether cerebellar activity can detect pre-symptomatic AD risk. Thus, the current study analyzed cerebellar EEG source localization during an EF-dependent stop-signal task (i.e., inhibitory control) in healthy, intact older adults (Mage = 80 years; 20 ε4+, 25 ε4-). Task performance was comparable between groups. Older age predicted greater activity in left crus II and lobule VIIb during the P300 window (i.e., performance evaluation), consistent with age-related compensation. Age*ε4 moderations specifically showed that compensatory patterns were evident only in ε4-, suggesting that cerebellar compensatory resources may already be depleted in healthy ε4+ elders. Thus, the posterolateral cerebellum is sensitive to AD-related neural deficits in healthy elders. Characterization of these patterns may be essential for the earliest possible detection of AD risk, which would enable critical early intervention prior to symptom onset.
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Affiliation(s)
- Elizabeth R Paitel
- Department of Psychology, Marquette University, Milwaukee, Wisconsin, USA
| | - Kristy A Nielson
- Department of Psychology, Marquette University, Milwaukee, Wisconsin, USA
- Department of Neurology, Center for Imaging Research, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
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11
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Merenstein JL, Zhao J, Mullin HA, Rudolph MD, Song AW, Madden DJ. High-resolution multi-shot diffusion imaging of structural networks in healthy neurocognitive aging. Neuroimage 2023; 275:120191. [PMID: 37244322 PMCID: PMC10482115 DOI: 10.1016/j.neuroimage.2023.120191] [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: 03/17/2023] [Revised: 05/10/2023] [Accepted: 05/24/2023] [Indexed: 05/29/2023] Open
Abstract
Healthy neurocognitive aging has been associated with the microstructural degradation of white matter pathways that connect distributed gray matter regions, assessed by diffusion-weighted imaging (DWI). However, the relatively low spatial resolution of standard DWI has limited the examination of age-related differences in the properties of smaller, tightly curved white matter fibers, as well as the relatively more complex microstructure of gray matter. Here, we capitalize on high-resolution multi-shot DWI, which allows spatial resolutions < 1 mm3 to be achieved on clinical 3T MRI scanners. We assessed whether traditional diffusion tensor-based measures of gray matter microstructure and graph theoretical measures of white matter structural connectivity assessed by standard (1.5 mm3 voxels, 3.375 μl volume) and high-resolution (1 mm3 voxels, 1μl volume) DWI were differentially related to age and cognitive performance in 61 healthy adults 18-78 years of age. Cognitive performance was assessed using an extensive battery comprising 12 separate tests of fluid (speed-dependent) cognition. Results indicated that the high-resolution data had larger correlations between age and gray matter mean diffusivity, but smaller correlations between age and structural connectivity. Moreover, parallel mediation models including both standard and high-resolution measures revealed that only the high-resolution measures mediated age-related differences in fluid cognition. These results lay the groundwork for future studies planning to apply high-resolution DWI methodology to further assess the mechanisms of both healthy aging and cognitive impairment.
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Affiliation(s)
- Jenna L Merenstein
- Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC, 27710, USA.
| | - Jiayi Zhao
- Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC, 27710, USA
| | - Hollie A Mullin
- Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC, 27710, USA
| | - Marc D Rudolph
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27514, USA
| | - Allen W Song
- Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC, 27710, USA
| | - David J Madden
- Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC, 27710, USA; Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, 27710, USA; Center for Cognitive Neuroscience, Duke University, Durham, NC, 27708, USA
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12
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Mulholland MM, Meguerditchian A, Hopkins WD. Age- and sex-related differences in baboon (Papio anubis) gray matter covariation. Neurobiol Aging 2023; 125:41-48. [PMID: 36827943 PMCID: PMC10308318 DOI: 10.1016/j.neurobiolaging.2023.01.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 01/05/2023] [Accepted: 01/09/2023] [Indexed: 01/30/2023]
Abstract
Age-related changes in cognition, brain morphology, and behavior are exhibited in several primate species. Baboons, like humans, naturally develop Alzheimer's disease-like pathology and cognitive declines with age and are an underutilized model for studies of aging. To determine age-related differences in gray matter covariation of 89 olive baboons (Papio anubis), we used source-based morphometry (SBM) to analyze data from magnetic resonance images. We hypothesized that we would find significant age effects in one or more SBM components, particularly those which include regions influenced by age in humans and other nonhuman primates (NHPs). A multivariate analysis of variance revealed that individual weighted gray matter covariation scores differed across the age classes. Elderly baboons contributed significantly less to gray matter covariation components including the brainstem, superior parietal cortex, thalamus, and pallidum compared to juveniles, and middle and superior frontal cortex compared to juveniles and young adults (p < 0.05). Future studies should examine the relationship between the changes in gray matter covariation reported here and age-related cognitive decline.
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Affiliation(s)
- M M Mulholland
- The University of Texas MD Anderson Cancer Center, Bastrop, TX.
| | - A Meguerditchian
- Laboratoire de Psychologie Cognitive UMR7290, LPC, CNRS, Aix-Marseille University, Institute of Language, Communication and the Brain, Marseille, France; Station de Primatologie-Celphedia, UAR846, Rousset, France
| | - W D Hopkins
- The University of Texas MD Anderson Cancer Center, Bastrop, TX
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13
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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: 11.0] [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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14
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Kanishka, Jha SK. Compensatory cognition in neurological diseases and aging: A review of animal and human studies. AGING BRAIN 2023; 3:100061. [PMID: 36911258 PMCID: PMC9997140 DOI: 10.1016/j.nbas.2022.100061] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 12/06/2022] [Accepted: 12/12/2022] [Indexed: 12/27/2022] Open
Abstract
Specialized individual circuits in the brain are recruited for specific functions. Interestingly, multiple neural circuitries continuously compete with each other to acquire the specialized function. However, the dominant among them compete and become the central neural network for that particular function. For example, the hippocampal principal neural circuitries are the dominant networks among many which are involved in learning processes. But, in the event of damage to the principal circuitry, many times, less dominant networks compensate for the primary network. This review highlights the psychopathologies of functional loss and the aspects of functional recuperation in the absence of the hippocampus.
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Affiliation(s)
- Kanishka
- School of Life Sciences, Jawaharlal Nehru University, New Delhi 110067, India
| | - Sushil K Jha
- School of Life Sciences, Jawaharlal Nehru University, New Delhi 110067, India
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15
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Schilling KG, Archer D, Yeh FC, Rheault F, Cai LY, Shafer A, Resnick SM, Hohman T, Jefferson A, Anderson AW, Kang H, Landman BA. Short superficial white matter and aging: a longitudinal multi-site study of 1293 subjects and 2711 sessions. AGING BRAIN 2023; 3:100067. [PMID: 36817413 PMCID: PMC9937516 DOI: 10.1016/j.nbas.2023.100067] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
It is estimated that short association fibers running immediately beneath the cortex may make up as much as 60% of the total white matter volume. However, these have been understudied relative to the long-range association, projection, and commissural fibers of the brain. This is largely because of limitations of diffusion MRI fiber tractography, which is the primary methodology used to non-invasively study the white matter connections. Inspired by recent anatomical considerations and methodological improvements in superficial white matter (SWM) tractography, we aim to characterize changes in these fiber systems in cognitively normal aging, which provide insight into the biological foundation of age-related cognitive changes, and a better understanding of how age-related pathology differs from healthy aging. To do this, we used three large, longitudinal and cross-sectional datasets (N = 1293 subjects, 2711 sessions) to quantify microstructural features and length/volume features of several SWM systems. We find that axial, radial, and mean diffusivities show positive associations with age, while fractional anisotropy has negative associations with age in SWM throughout the entire brain. These associations were most pronounced in the frontal, temporal, and temporoparietal regions. Moreover, measures of SWM volume and length decrease with age in a heterogenous manner across the brain, with different rates of change in inter-gyri and intra-gyri SWM, and at slower rates than well-studied long-range white matter pathways. These features, and their variations with age, provide the background for characterizing normal aging, and, in combination with larger association pathways and gray matter microstructural features, may provide insight into fundamental mechanisms associated with aging and cognition.
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Affiliation(s)
- Kurt G Schilling
- Department of Radiology & Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN
| | - Derek Archer
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University Medical Center, Nashville, TN, USA,Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA,Vanderbilt Genetics Institute, Vanderbilt University School of Medicine, Nashville, TN
| | - Fang-Cheng Yeh
- Department of Neurological Surgery, University of Pittsburgh Medical Center, Pittsburgh, PA, USA; Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Francois Rheault
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, United States
| | - Leon Y Cai
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, United States
| | - Andrea Shafer
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, United States of America
| | - Susan M. Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, United States of America
| | - Timothy Hohman
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University Medical Center, Nashville, TN, USA,Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA,Vanderbilt Genetics Institute, Vanderbilt University School of Medicine, Nashville, TN
| | - Angela Jefferson
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University Medical Center, Nashville, TN, USA,Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA,Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Adam W Anderson
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United States
| | - Hakmook Kang
- Department of Biostatistics, Vanderbilt University, Nashville, TN, United States
| | - Bennett A Landman
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, United States
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16
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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: 9.5] [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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17
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Hsieh S, Yang MH. Potential Diffusion Tensor Imaging Biomarkers for Elucidating Intra-Individual Age-Related Changes in Cognitive Control and Processing Speed. Front Aging Neurosci 2022; 14:850655. [PMID: 35557836 PMCID: PMC9087335 DOI: 10.3389/fnagi.2022.850655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Accepted: 03/24/2022] [Indexed: 11/24/2022] Open
Abstract
Cognitive aging, especially cognitive control, and processing speed aging have been well-documented in the literature. Most of the evidence was reported based on cross-sectional data, in which inter-individual age effects were shown. However, there have been some studies pointing out the possibility of overlooking intra-individual changes in cognitive aging. To systematically examine whether age-related differences and age-related changes might yield distinctive patterns, this study directly compared cognitive control function and processing speed between different cohorts versus follow-up changes across the adult lifespan. Moreover, considering that cognitive aging has been attributed to brain disconnection in white matter (WM) integrity, this study focused on WM integrity via acquiring diffusion-weighted imaging data with an MRI instrument that are further fitted to a diffusion tensor model (i.e., DTI) to detect water diffusion directionality (i.e., fractional anisotropy, FA; mean diffusivity, MD; radial diffusivity, RD; axial diffusivity, AxD). Following data preprocessing, 114 participants remained for further analyses in which they completed the two follow-up sessions (with a range of 1-2 years) containing a series of neuropsychology instruments and computerized cognitive control tasks. The results show that many significant correlations between age and cognitive control functions originally shown on cross-sectional data no longer exist on the longitudinal data. The current longitudinal data show that MD, RD, and AxD (especially in the association fibers of anterior thalamic radiation) are more strongly correlated to follow-up aging processes, suggesting that axonal/myelin damage is a more robust phenomenon for observing intra-individual aging processes. Moreover, processing speed appears to be the most prominent cognitive function to reflect DTI-related age (cross-sectional) and aging (longitudinal) effects. Finally, converging the results from regression analyses and mediation models, MD, RD, and AxD appear to be the representative DTI measures to reveal age-related changes in processing speed. To conclude, the current results provide new insights to which indicator of WM integrity and which type of cognitive changes are most representative (i.e., potentially to be neuroimaging biomarkers) to reflect intra-individual cognitive aging processes.
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Affiliation(s)
- Shulan Hsieh
- Cognitive Electrophysiology Laboratory: Control, Aging, Sleep, and Emotion, Department of Psychology, National Cheng Kung University, Tainan, Taiwan
- Institute of Allied Health Sciences, National Cheng Kung University, Tainan, Taiwan
- Department of Public Health, National Cheng Kung University, Tainan, Taiwan
| | - Meng-Heng Yang
- Cognitive Electrophysiology Laboratory: Control, Aging, Sleep, and Emotion, Department of Psychology, National Cheng Kung University, Tainan, Taiwan
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18
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Gust CJ, Moe EN, Seals DR, Banich MT, Andrews-Hanna JR, Hutchison KE, Bryan AD. Associations Between Age and Resting State Connectivity Are Partially Dependent Upon Cardiovascular Fitness. Front Aging Neurosci 2022; 14:858405. [PMID: 35527739 PMCID: PMC9067399 DOI: 10.3389/fnagi.2022.858405] [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: 01/19/2022] [Accepted: 03/17/2022] [Indexed: 11/13/2022] Open
Abstract
Previous research suggests a marked impact of aging on structural and functional connectivity within the frontoparietal control network (FPCN) and default mode network (DMN). As aging is also associated with reductions in cardiovascular fitness, age-related network connectivity differences reported by past studies could be partially due to age-related declines in fitness. Here, we use data collected as part of a 16-week exercise intervention to explore relationships between fitness and functional connectivity. Young and older adults completed baseline assessments including cardiovascular fitness, health and functioning measures, and an fMRI session. Scan data were acquired on a Siemens 3T MRI scanner with a 32-channel head coil. Results from regression analyses indicated that average connectivity did not differ between young and older adults. However, individual ROI-to-ROI connectivity analyses indicated weaker functional correlations for older adults between specific regions in the FPCN and DMN and, critically, many of these differences were attenuated when fitness was accounted for. Taken together, findings suggest that fitness exerts regional rather than global effects on network connectivity.
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Affiliation(s)
- Charleen J. Gust
- Department of Psychology and Neuroscience, University of Colorado, Boulder, Boulder, CO, United States
- *Correspondence: Charleen J. Gust,
| | - Erin N. Moe
- Department of Psychiatry, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
| | - Douglas R. Seals
- Department of Integrative Physiology, University of Colorado, Boulder, Boulder, CO, United States
| | - Marie T. Banich
- Department of Psychology and Neuroscience, University of Colorado, Boulder, Boulder, CO, United States
- Institute of Cognitive Science, University of Colorado, Boulder, Boulder, CO, United States
| | - Jessica R. Andrews-Hanna
- Department of Psychology, University of Arizona, Tucson, AZ, United States
- Cognitive Science Program, University of Arizona, Tucson, AZ, United States
| | - Kent E. Hutchison
- Department of Psychiatry, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
| | - Angela D. Bryan
- Department of Psychology and Neuroscience, University of Colorado, Boulder, Boulder, CO, United States
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19
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Frangou S, Modabbernia A, Williams SCR, Papachristou E, Doucet GE, Agartz I, Aghajani M, Akudjedu TN, Albajes‐Eizagirre A, Alnæs D, Alpert KI, Andersson M, Andreasen NC, Andreassen OA, Asherson P, Banaschewski T, Bargallo N, Baumeister S, Baur‐Streubel R, Bertolino A, Bonvino A, Boomsma DI, Borgwardt S, Bourque J, Brandeis D, Breier A, Brodaty H, Brouwer RM, Buitelaar JK, Busatto GF, Buckner RL, Calhoun V, Canales‐Rodríguez EJ, Cannon DM, Caseras X, Castellanos FX, Cervenka S, Chaim‐Avancini TM, Ching CRK, Chubar V, Clark VP, Conrod P, Conzelmann A, Crespo‐Facorro B, Crivello F, Crone EA, Dale AM, Dannlowski U, Davey C, de Geus EJC, de Haan L, de Zubicaray GI, den Braber A, Dickie EW, Di Giorgio A, Doan NT, Dørum ES, Ehrlich S, Erk S, Espeseth T, Fatouros‐Bergman H, Fisher SE, Fouche J, Franke B, Frodl T, Fuentes‐Claramonte P, Glahn DC, Gotlib IH, Grabe H, Grimm O, Groenewold NA, Grotegerd D, Gruber O, Gruner P, Gur RE, Gur RC, Hahn T, Harrison BJ, Hartman CA, Hatton SN, Heinz A, Heslenfeld DJ, Hibar DP, Hickie IB, Ho B, Hoekstra PJ, Hohmann S, Holmes AJ, Hoogman M, Hosten N, Howells FM, Hulshoff Pol HE, Huyser C, Jahanshad N, James A, Jernigan TL, Jiang J, Jönsson EG, Joska JA, Kahn R, Kalnin A, Kanai R, Klein M, Klyushnik TP, Koenders L, Koops S, Krämer B, Kuntsi J, Lagopoulos J, Lázaro L, Lebedeva I, Lee WH, Lesch K, Lochner C, Machielsen MWJ, Maingault S, Martin NG, Martínez‐Zalacaín I, Mataix‐Cols D, Mazoyer B, McDonald C, McDonald BC, McIntosh AM, McMahon KL, McPhilemy G, Meinert S, Menchón JM, Medland SE, Meyer‐Lindenberg A, Naaijen J, Najt P, Nakao T, Nordvik JE, Nyberg L, Oosterlaan J, de la Foz VO, Paloyelis Y, Pauli P, Pergola G, Pomarol‐Clotet E, Portella MJ, Potkin SG, Radua J, Reif A, Rinker DA, Roffman JL, Rosa PGP, Sacchet MD, Sachdev PS, Salvador R, Sánchez‐Juan P, Sarró S, Satterthwaite TD, Saykin AJ, Serpa MH, Schmaal L, Schnell K, Schumann G, Sim K, Smoller JW, Sommer I, Soriano‐Mas C, Stein DJ, Strike LT, Swagerman SC, Tamnes CK, Temmingh HS, Thomopoulos SI, Tomyshev AS, Tordesillas‐Gutiérrez D, Trollor JN, Turner JA, Uhlmann A, van den Heuvel OA, van den Meer D, van der Wee NJA, van Haren NEM, van 't Ent D, van Erp TGM, Veer IM, Veltman DJ, Voineskos A, Völzke H, Walter H, Walton E, Wang L, Wang Y, Wassink TH, Weber B, Wen W, West JD, Westlye LT, Whalley H, Wierenga LM, Wittfeld K, Wolf DH, Worker A, Wright MJ, Yang K, Yoncheva Y, Zanetti MV, Ziegler GC, Thompson PM, Dima D. Cortical thickness across the lifespan: Data from 17,075 healthy individuals aged 3-90 years. Hum Brain Mapp 2022; 43:431-451. [PMID: 33595143 PMCID: PMC8675431 DOI: 10.1002/hbm.25364] [Citation(s) in RCA: 113] [Impact Index Per Article: 56.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Revised: 01/02/2021] [Accepted: 01/21/2021] [Indexed: 12/25/2022] Open
Abstract
Delineating the association of age and cortical thickness in healthy individuals is critical given the association of cortical thickness with cognition and behavior. Previous research has shown that robust estimates of the association between age and brain morphometry require large-scale studies. In response, we used cross-sectional data from 17,075 individuals aged 3-90 years from the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to infer age-related changes in cortical thickness. We used fractional polynomial (FP) regression to quantify the association between age and cortical thickness, and we computed normalized growth centiles using the parametric Lambda, Mu, and Sigma method. Interindividual variability was estimated using meta-analysis and one-way analysis of variance. For most regions, their highest cortical thickness value was observed in childhood. Age and cortical thickness showed a negative association; the slope was steeper up to the third decade of life and more gradual thereafter; notable exceptions to this general pattern were entorhinal, temporopolar, and anterior cingulate cortices. Interindividual variability was largest in temporal and frontal regions across the lifespan. Age and its FP combinations explained up to 59% variance in cortical thickness. These results may form the basis of further investigation on normative deviation in cortical thickness and its significance for behavioral and cognitive outcomes.
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Affiliation(s)
- Sophia Frangou
- Department of PsychiatryIcahn School of Medicine at Mount SinaiNew York CityNew YorkUSA
- Department of Psychiatry, Djavad Mowafaghian Centre for Brain HealthUniversity of British ColumbiaVancouverCanada
| | | | - Steven C. R. Williams
- Department of NeuroimagingInstitute of Psychiatry, Psychology and Neuroscience, King's College LondonLondonUnited Kingdom
| | - Efstathios Papachristou
- Psychology and Human DevelopmentInstitute of Education, University College LondonLondonUnited Kingdom
| | - Gaelle E. Doucet
- Institute for Human NeuroscienceBoys Town National Research HospitalOmahaNebraskaUSA
| | - Ingrid Agartz
- Norwegian Centre for Mental Disorders Research (NORMENT)Institute of Clinical Medicine, University of OsloOsloNorway
- Department of Psychiatric ResearchDiakonhjemmet HospitalOsloNorway
- Centre for Psychiatric Research, Department of Clinical NeuroscienceKarolinska InstitutetSolnaSweden
| | - Moji Aghajani
- Department of PsychiatryAmsterdam University Medical Centre, Vrije UniversiteitAmsterdamNetherlands
- Section Forensic Family & Youth CareInstitute of Education & Child StudiesLeiden UniversityNetherlands
| | - Theophilus N. Akudjedu
- Institute of Medical Imaging and Visualisation, Department of Medical Science and Public Health, Faculty of Health and Social SciencesBournemouth UniversityPooleUnited Kingdom
- Clinical Neuroimaging Laboratory, Centre for Neuroimaging and Cognitive Genomics and NCBES Galway Neuroscience CentreNational University of IrelandGalwayIreland
| | - Anton Albajes‐Eizagirre
- FIDMAG Germanes HospitalàriesBarcelonaSpain
- Mental Health Research Networking Center (CIBERSAM)MadridSpain
| | - Dag Alnæs
- Norwegian Centre for Mental Disorders Research (NORMENT)Institute of Clinical Medicine, University of OsloOsloNorway
- Division of Mental Health and AddictionInstitute of Clinical Medicine, University of OsloOsloNorway
| | | | - Micael Andersson
- Department of Integrative Medical BiologyUmeå UniversityUmeåSweden
| | - Nancy C. Andreasen
- Department of Psychiatry, Carver College of MedicineThe University of IowaIowa CityIowaUSA
| | - Ole A. Andreassen
- Norwegian Centre for Mental Disorders Research (NORMENT)Institute of Clinical Medicine, University of OsloOsloNorway
| | - Philip Asherson
- Social, Genetic and Developmental Psychiatry CentreInstitute of Psychiatry, Psychology and Neuroscience, King's College LondonLondonUnited Kingdom
| | - Tobias Banaschewski
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental HealthHeidelberg UniversityHeidelbergGermany
| | - Nuria Bargallo
- Imaging Diagnostic CentreHospital Clinic, Barcelona University ClinicBarcelonaSpain
- August Pi i Sunyer Biomedical Research Institut (IDIBAPS)BarcelonaSpain
| | - Sarah Baumeister
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental HealthHeidelberg UniversityHeidelbergGermany
| | - Ramona Baur‐Streubel
- Department of Psychology, Biological Psychology, Clinical Psychology and PsychotherapyUniversity of WürzburgWürzburgGermany
| | - Alessandro Bertolino
- Department of Basic Medical Science, Neuroscience and Sense OrgansUniversity of Bari Aldo MoroBariItaly
| | - Aurora Bonvino
- Department of Biological PsychologyVrije UniversiteitAmsterdamNetherlands
| | - Dorret I. Boomsma
- Department of Biological PsychologyVrije UniversiteitAmsterdamNetherlands
| | - Stefan Borgwardt
- Department of Psychiatry & PsychotherapyUniversity of LübeckLübeckGermany
| | - Josiane Bourque
- Department of PsychiatryUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Daniel Brandeis
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental HealthHeidelberg UniversityHeidelbergGermany
| | - Alan Breier
- Department of Radiology and Imaging SciencesIndiana University School of MedicineIndianapolisIndianaUSA
| | - Henry Brodaty
- Centre for Healthy Brain Ageing, School of PsychiatryUniversity of New South WalesKensingtonNew South WalesAustralia
| | - Rachel M. Brouwer
- Rudolf Magnus Institute of NeuroscienceUniversity Medical Center UtrechtUtrechtNetherlands
| | - Jan K. Buitelaar
- Donders Center of Medical NeurosciencesRadboud UniversityNijmegenNetherlands
- Donders Centre for Cognitive NeuroimagingRadboud UniversityNijmegenNetherlands
- Donders Institute for Brain, Cognition and BehaviourRadboud UniversityNijmegenNetherlands
| | - Geraldo F. Busatto
- Laboratory of Psychiatric Neuroimaging, Departamento e Instituto de Psiquiatria, Hospital das Clinicas HCFMUSP, Faculdade de MedicinaUniversidade de São PauloSão PauloBrazil
| | - Randy L. Buckner
- Department of Psychology, Center for Brain ScienceHarvard UniversityCambridgeMassachusettsUSA
- Department of PsychiatryMassachusetts General HospitalBostonMassachusettsUSA
| | - Vincent Calhoun
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of TechnologyEmory University, USA Neurology, Radiology, Psychiatry and Biomedical Engineering, Emory UniversityAtlantaGeorgiaUSA
| | - Erick J. Canales‐Rodríguez
- FIDMAG Germanes HospitalàriesBarcelonaSpain
- Mental Health Research Networking Center (CIBERSAM)MadridSpain
| | - Dara M. Cannon
- Clinical Neuroimaging Laboratory, Centre for Neuroimaging and Cognitive Genomics and NCBES Galway Neuroscience CentreNational University of IrelandGalwayIreland
| | - Xavier Caseras
- MRC Centre for Neuropsychiatric Genetics and GenomicsCardiff UniversityCardiffUnited Kingdom
| | | | - Simon Cervenka
- Centre for Psychiatric Research, Department of Clinical NeuroscienceKarolinska InstitutetSolnaSweden
- Stockholm Health Care ServicesStockholmSweden
| | - Tiffany M. Chaim‐Avancini
- Laboratory of Psychiatric Neuroimaging, Departamento e Instituto de Psiquiatria, Hospital das Clinicas HCFMUSP, Faculdade de MedicinaUniversidade de São PauloSão PauloBrazil
| | - Christopher R. K. Ching
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of MedicineUniversity of Southern CaliforniaMarina del ReyCaliforniaUSA
| | - Victoria Chubar
- Mind‐Body Research Group, Department of NeuroscienceKU LeuvenLeuvenBelgium
| | - Vincent P. Clark
- Department of PsychologyUniversity of New MexicoAlbuquerqueNew MexicoUSA
- Mind Research NetworkAlbuquerqueNew MexicoUSA
| | - Patricia Conrod
- Department of PsychiatryUniversité de MontréalMontrealCanada
| | - Annette Conzelmann
- Department of Child and Adolescent Psychiatry, Psychosomatics and PsychotherapyUniversity of TübingenTübingenGermany
| | - Benedicto Crespo‐Facorro
- Mental Health Research Networking Center (CIBERSAM)MadridSpain
- HU Virgen del Rocio, IBiSUniversity of SevillaSevillaSpain
| | - Fabrice Crivello
- Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293Université de BordeauxBordeauxFrance
| | - Eveline A. Crone
- Erasmus School of Social and Behavioural SciencesErasmus University RotterdamRotterdamNetherlands
- Faculteit der Sociale Wetenschappen, Instituut PsychologieUniversiteit LeidenLeidenNetherlands
| | - Anders M. Dale
- Center for Multimodal Imaging and Genetics, Department of NeuroscienceUniversity of California‐San DiegoSan DiegoCaliforniaUSA
- Department of RadiologyUniversity of California‐San DiegoSan DiegoCaliforniaUSA
| | - Udo Dannlowski
- Department of Psychiatry and PsychotherapyUniversity of MünsterGermany
| | | | - Eco J. C. de Geus
- Department of Biological PsychologyVrije UniversiteitAmsterdamNetherlands
| | - Lieuwe de Haan
- Academisch Medisch CentrumUniversiteit van AmsterdamAmsterdamNetherlands
| | - Greig I. de Zubicaray
- Faculty of Health, Institute of Health and Biomedical InnovationQueensland University of TechnologyQueenslandAustralia
| | - Anouk den Braber
- Department of Biological PsychologyVrije UniversiteitAmsterdamNetherlands
| | - Erin W. Dickie
- Kimel Family Translational Imaging Genetics Laboratory, Campbell Family Mental Health Research InstituteCAMHCampbellCanada
- Department of PsychiatryUniversity of TorontoTorontoCanada
| | - Annabella Di Giorgio
- Biological Psychiatry LabFondazione IRCCS Casa Sollievo della SofferenzaSan Giovanni Rotondo (FG)Italy
| | - Nhat Trung Doan
- Norwegian Centre for Mental Disorders Research (NORMENT)Institute of Clinical Medicine, University of OsloOsloNorway
| | - Erlend S. Dørum
- Norwegian Centre for Mental Disorders Research (NORMENT)Institute of Clinical Medicine, University of OsloOsloNorway
- Department of PsychologyUniversity of OsloOsloNorway
- Sunnaas Rehabilitation Hospital HTNesoddenNorway
| | - Stefan Ehrlich
- Division of Psychological and Social Medicine and Developmental NeurosciencesTechnische Universität DresdenDresdenGermany
- Faculty of MedicineUniversitätsklinikum Carl Gustav Carus an der TU DresdenDresdenGermany
| | - Susanne Erk
- Division of Mind and Brain Research, Department of Psychiatry and PsychotherapyCharité‐Universitätsmedizin BerlinBerlinGermany
| | - Thomas Espeseth
- Biological Psychiatry LabFondazione IRCCS Casa Sollievo della SofferenzaSan Giovanni Rotondo (FG)Italy
- Bjørknes CollegeOsloNorway
| | - Helena Fatouros‐Bergman
- Centre for Psychiatric Research, Department of Clinical NeuroscienceKarolinska InstitutetSolnaSweden
- Stockholm Health Care ServicesStockholmSweden
| | - Simon E. Fisher
- Donders Institute for Brain, Cognition and BehaviourRadboud UniversityNijmegenNetherlands
- Language and Genetics DepartmentMax Planck Institute for PsycholinguisticsNijmegenNetherlands
| | - Jean‐Paul Fouche
- Department of Psychiatry and Mental HealthUniversity of Cape TownCape TownSouth Africa
| | - Barbara Franke
- Donders Institute for Brain, Cognition and BehaviourRadboud UniversityNijmegenNetherlands
- Department of Human GeneticsRadboud University Medical CenterNijmegenNetherlands
- Department of PsychiatryRadboud University Medical CenterNijmegenNetherlands
| | - Thomas Frodl
- Department of Psychiatry and PsychotherapyOtto von Guericke University MagdeburgMagdeburgGermany
| | - Paola Fuentes‐Claramonte
- FIDMAG Germanes HospitalàriesBarcelonaSpain
- Mental Health Research Networking Center (CIBERSAM)MadridSpain
| | - David C. Glahn
- Department of PsychiatryTommy Fuss Center for Neuropsychiatric Disease Research Boston Children's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Ian H. Gotlib
- Department of PsychologyStanford UniversityStanfordCaliforniaUSA
| | - Hans‐Jörgen Grabe
- Department of Psychiatry and PsychotherapyUniversity Medicine Greifswald, University of GreifswaldGreifswaldGermany
- German Center for Neurodegenerative Diseases (DZNE)Site Rostock/GreifswaldGreifswaldGermany
| | - Oliver Grimm
- Department for Psychiatry, Psychosomatics and PsychotherapyUniversitätsklinikum Frankfurt, Goethe UniversitatFrankfurtGermany
| | - Nynke A. Groenewold
- Department of Psychiatry and Mental HealthUniversity of Cape TownCape TownSouth Africa
- Neuroscience InstituteUniversity of Cape TownCape TownSouth Africa
| | | | - Oliver Gruber
- Section for Experimental Psychopathology and Neuroimaging, Department of General PsychiatryHeidelberg UniversityHeidelbergGermany
| | - Patricia Gruner
- Department of PsychiatryYale UniversityNew HavenConnecticutUSA
- Learning Based Recovery CenterVA Connecticut Health SystemWest HavenConnecticutUSA
| | - Rachel E. Gur
- Department of PsychiatryUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Lifespan Brain Institute, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Children's Hospital of PhiladelphiaUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Ruben C. Gur
- Department of PsychiatryUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Lifespan Brain Institute, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Children's Hospital of PhiladelphiaUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Tim Hahn
- Department of Psychiatry and PsychotherapyUniversity of MünsterGermany
| | - Ben J. Harrison
- Melbourne Neuropsychiatry CenterUniversity of MelbourneMelbourneAustralia
| | - Catharine A. Hartman
- Interdisciplinary Center Psychopathology and Emotion regulationUniversity Medical Center Groningen, University of GroningenGroningenNetherlands
| | - Sean N. Hatton
- Brain and Mind CentreUniversity of SydneySydneyAustralia
| | - Andreas Heinz
- Division of Mind and Brain Research, Department of Psychiatry and PsychotherapyCharité‐Universitätsmedizin BerlinBerlinGermany
| | - Dirk J. Heslenfeld
- Departments of Experimental and Clinical PsychologyVrije Universiteit AmsterdamAmsterdamNetherlands
| | - Derrek P. Hibar
- Personalized Healthcare, Genentech, Inc.South San FranciscoCaliforniaUSA
| | - Ian B. Hickie
- Brain and Mind CentreUniversity of SydneySydneyAustralia
| | - Beng‐Choon Ho
- Department of Psychiatry, Carver College of MedicineThe University of IowaIowa CityIowaUSA
| | - Pieter J. Hoekstra
- Department of PsychiatryUniversity Medical Center Groningen, University of GroningenGroningenNetherlands
| | - Sarah Hohmann
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental HealthHeidelberg UniversityHeidelbergGermany
| | - Avram J. Holmes
- Department of PsychologyYale UniversityNew HavenConnecticutUSA
| | - Martine Hoogman
- Donders Institute for Brain, Cognition and BehaviourRadboud UniversityNijmegenNetherlands
- Department of Human GeneticsRadboud University Medical CenterNijmegenNetherlands
| | - Norbert Hosten
- Norbert Institute of Diagnostic Radiology and NeuroradiologyUniversity Medicine Greifswald, University of GreifswaldGreifswaldGermany
| | - Fleur M. Howells
- Department of Psychiatry and Mental HealthUniversity of Cape TownCape TownSouth Africa
- Neuroscience InstituteUniversity of Cape TownCape TownSouth Africa
| | | | - Chaim Huyser
- De Bascule, Academic Centre for Children and Adolescent PsychiatryAmsterdamNetherlands
| | - Neda Jahanshad
- Mind‐Body Research Group, Department of NeuroscienceKU LeuvenLeuvenBelgium
| | - Anthony James
- Department of PsychiatryOxford UniversityOxfordUnited Kingdom
| | - Terry L. Jernigan
- Center for Human Development, Departments of Cognitive Science, Psychiatry, and RadiologyUniversity of CaliforniaSan DiegoCaliforniaUSA
| | - Jiyang Jiang
- Centre for Healthy Brain Ageing, School of PsychiatryUniversity of New South WalesKensingtonNew South WalesAustralia
| | - Erik G. Jönsson
- Norwegian Centre for Mental Disorders Research (NORMENT)Institute of Clinical Medicine, University of OsloOsloNorway
| | - John A. Joska
- Department of Psychiatry and Mental HealthUniversity of Cape TownCape TownSouth Africa
| | - Rene Kahn
- Department of PsychiatryIcahn School of Medicine at Mount SinaiNew York CityNew YorkUSA
| | - Andrew Kalnin
- Department of RadiologyOhio State University College of MedicineColumbusOhioUSA
| | - Ryota Kanai
- Department of NeuroinformaticsAraya, Inc.TokyoJapan
| | - Marieke Klein
- Donders Institute for Brain, Cognition and BehaviourRadboud UniversityNijmegenNetherlands
- Department of Human GeneticsRadboud University Medical CenterNijmegenNetherlands
- Department of PsychiatryUniversity of California San DiegoSan DiegoCaliforniaUSA
| | | | - Laura Koenders
- Academisch Medisch CentrumUniversiteit van AmsterdamAmsterdamNetherlands
| | - Sanne Koops
- Rudolf Magnus Institute of NeuroscienceUniversity Medical Center UtrechtUtrechtNetherlands
| | - Bernd Krämer
- Section for Experimental Psychopathology and Neuroimaging, Department of General PsychiatryHeidelberg UniversityHeidelbergGermany
| | - Jonna Kuntsi
- Social, Genetic and Developmental Psychiatry CentreInstitute of Psychiatry, Psychology and Neuroscience, King's College LondonLondonUnited Kingdom
| | - Jim Lagopoulos
- Sunshine Coast Mind and NeuroscienceThompson Institute, University of the Sunshine CoastQueenslandAustralia
| | - Luisa Lázaro
- Mental Health Research Networking Center (CIBERSAM)MadridSpain
- Department of Child and Adolescent Psychiatry and PsychologyHospital Clinic, University of BarcelonaBarcelonaSpain
| | - Irina Lebedeva
- Mental Health Research CenterRussian Academy of Medical SciencesMoscowRussia
| | - Won Hee Lee
- Department of PsychiatryIcahn School of Medicine at Mount SinaiNew York CityNew YorkUSA
| | - Klaus‐Peter Lesch
- Department of Psychiatry, Psychosomatics and PsychotherapyJulius‐Maximilians Universität WürzburgWürzburgGermany
| | - Christine Lochner
- SA MRC Unit on Risk and Resilience in Mental Disorders, Department of PsychiatryStellenbosch UniversityStellenboschSouth Africa
| | | | - Sophie Maingault
- Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293Université de BordeauxBordeauxFrance
| | - Nicholas G. Martin
- Queensland Institute of Medical ResearchBerghofer Medical Research InstituteQueenslandAustralia
| | - Ignacio Martínez‐Zalacaín
- Mental Health Research Networking Center (CIBERSAM)MadridSpain
- Department of PsychiatryBellvitge University Hospital‐IDIBELL, University of BarcelonaBarcelonaSpain
| | - David Mataix‐Cols
- Centre for Psychiatric Research, Department of Clinical NeuroscienceKarolinska InstitutetSolnaSweden
- Stockholm Health Care ServicesStockholmSweden
| | - Bernard Mazoyer
- Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293Université de BordeauxBordeauxFrance
| | - Colm McDonald
- Clinical Neuroimaging Laboratory, Centre for Neuroimaging and Cognitive Genomics and NCBES Galway Neuroscience CentreNational University of IrelandGalwayIreland
| | - Brenna C. McDonald
- Department of Radiology and Imaging SciencesIndiana University School of MedicineIndianapolisIndianaUSA
| | | | - Katie L. McMahon
- School of Clinical Sciences, Institute of Health and Biomedical InnovationQueensland University of TechnologyQueenslandAustralia
| | - Genevieve McPhilemy
- Clinical Neuroimaging Laboratory, Centre for Neuroimaging and Cognitive Genomics and NCBES Galway Neuroscience CentreNational University of IrelandGalwayIreland
| | - Susanne Meinert
- Department of Psychiatry and PsychotherapyUniversity of MünsterGermany
| | - José M. Menchón
- Mental Health Research Networking Center (CIBERSAM)MadridSpain
- Department of PsychiatryBellvitge University Hospital‐IDIBELL, University of BarcelonaBarcelonaSpain
| | - Sarah E. Medland
- Queensland Institute of Medical ResearchBerghofer Medical Research InstituteQueenslandAustralia
| | - Andreas Meyer‐Lindenberg
- Department of Psychiatry and PsychotherapyCentral Institute of Mental Health, Heidelberg UniversityHeidelbergGermany
| | - Jilly Naaijen
- Donders Centre for Cognitive NeuroimagingRadboud UniversityNijmegenNetherlands
- Donders Institute for Brain, Cognition and BehaviourRadboud UniversityNijmegenNetherlands
| | - Pablo Najt
- Clinical Neuroimaging Laboratory, Centre for Neuroimaging and Cognitive Genomics and NCBES Galway Neuroscience CentreNational University of IrelandGalwayIreland
| | - Tomohiro Nakao
- Department of Clinical MedicineKyushu UniversityFukuokaJapan
| | | | - Lars Nyberg
- Department of Integrative Medical BiologyUmeå UniversityUmeåSweden
- Department of Radiation SciencesUmeå Center for Functional Brain Imaging, Umeå UniversityUmeåSweden
| | - Jaap Oosterlaan
- Department of Clinical NeuropsychologyAmsterdam University Medical Centre, Vrije Universiteit AmsterdamAmsterdamNetherlands
| | - Víctor Ortiz‐García de la Foz
- Mental Health Research Networking Center (CIBERSAM)MadridSpain
- Department of PsychiatryUniversity Hospital “Marques de Valdecilla”, Instituto de Investigación Valdecilla (IDIVAL)SantanderSpain
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM)Instituto de Salud Carlos IIIMadridSpain
| | - Yannis Paloyelis
- Department of NeuroimagingInstitute of Psychiatry, Psychology and Neuroscience, King's College LondonLondonUnited Kingdom
| | - Paul Pauli
- Department of Psychology, Biological Psychology, Clinical Psychology and PsychotherapyUniversity of WürzburgWürzburgGermany
- Centre of Mental HealthUniversity of WürzburgWürzburgGermany
| | - Giulio Pergola
- Department of Basic Medical Science, Neuroscience and Sense OrgansUniversity of Bari Aldo MoroBariItaly
| | - Edith Pomarol‐Clotet
- FIDMAG Germanes HospitalàriesBarcelonaSpain
- Mental Health Research Networking Center (CIBERSAM)MadridSpain
| | - Maria J. Portella
- FIDMAG Germanes HospitalàriesBarcelonaSpain
- Department of PsychiatryHospital de la Santa Creu i Sant Pau, Institut d'Investigació Biomèdica Sant Pau, Universitat Autònoma de BarcelonaBarcelonaSpain
| | - Steven G. Potkin
- Department of PsychiatryUniversity of California at IrvineIrvineCaliforniaUSA
| | - Joaquim Radua
- Centre for Psychiatric Research, Department of Clinical NeuroscienceKarolinska InstitutetSolnaSweden
- August Pi i Sunyer Biomedical Research Institut (IDIBAPS)BarcelonaSpain
- Department of Psychosis StudiesInstitute of Psychiatry, Psychology & Neuroscience, King's College LondonLondonUnited Kingdom
| | - Andreas Reif
- Department for Psychiatry, Psychosomatics and PsychotherapyUniversitätsklinikum Frankfurt, Goethe UniversitatFrankfurtGermany
| | - Daniel A. Rinker
- Norwegian Centre for Mental Disorders Research (NORMENT)Institute of Clinical Medicine, University of OsloOsloNorway
| | - Joshua L. Roffman
- Department of PsychiatryMassachusetts General HospitalBostonMassachusettsUSA
| | - Pedro G. P. Rosa
- Laboratory of Psychiatric Neuroimaging, Departamento e Instituto de Psiquiatria, Hospital das Clinicas HCFMUSP, Faculdade de MedicinaUniversidade de São PauloSão PauloBrazil
| | - Matthew D. Sacchet
- Center for Depression, Anxiety, and Stress ResearchMcLean Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Perminder S. Sachdev
- Centre for Healthy Brain Ageing, School of PsychiatryUniversity of New South WalesKensingtonNew South WalesAustralia
| | | | - Pascual Sánchez‐Juan
- Department of PsychiatryUniversity Hospital “Marques de Valdecilla”, Instituto de Investigación Valdecilla (IDIVAL)SantanderSpain
- Centro de Investigacion Biomedica en Red en Enfermedades Neurodegenerativas (CIBERNED)ValderrebolloSpain
| | | | | | - Andrew J. Saykin
- Department of Radiology and Imaging SciencesIndiana University School of MedicineIndianapolisIndianaUSA
| | - Mauricio H. Serpa
- Laboratory of Psychiatric Neuroimaging, Departamento e Instituto de Psiquiatria, Hospital das Clinicas HCFMUSP, Faculdade de MedicinaUniversidade de São PauloSão PauloBrazil
| | - Lianne Schmaal
- OrygenThe National Centre of Excellence in Youth Mental HealthMelbourneAustralia
- Centre for Youth Mental HealthThe University of MelbourneMelbourneAustralia
| | - Knut Schnell
- Department of Psychiatry and PsychotherapyUniversity Medical Center GöttingenGöttingenGermany
| | - Gunter Schumann
- Social, Genetic and Developmental Psychiatry CentreInstitute of Psychiatry, Psychology and Neuroscience, King's College LondonLondonUnited Kingdom
- Centre for Population Neuroscience and Precision MedicineInstitute of Psychiatry, Psychology & Neuroscience, King's College LondonLondonUnited Kingdom
| | - Kang Sim
- Department of General PsychiatryInstitute of Mental HealthSingaporeSingapore
| | - Jordan W. Smoller
- Center for Genomic MedicineMassachusetts General HospitalBostonMassachusettsUSA
| | - Iris Sommer
- Department of Biomedical Sciences of Cells and Systems, Rijksuniversiteit GroningenUniversity Medical Center GroningenGroningenNetherlands
| | - Carles Soriano‐Mas
- Mental Health Research Networking Center (CIBERSAM)MadridSpain
- Department of PsychiatryBellvitge University Hospital‐IDIBELL, University of BarcelonaBarcelonaSpain
| | - Dan J. Stein
- SA MRC Unit on Risk and Resilience in Mental Disorders, Department of PsychiatryStellenbosch UniversityStellenboschSouth Africa
| | - Lachlan T. Strike
- Queensland Brain InstituteUniversity of QueenslandQueenslandAustralia
| | | | - Christian K. Tamnes
- Norwegian Centre for Mental Disorders Research (NORMENT)Institute of Clinical Medicine, University of OsloOsloNorway
- Department of Psychiatric ResearchDiakonhjemmet HospitalOsloNorway
- PROMENTA Research Center, Department of PsychologyUniversity of OsloOsloNorway
| | - Henk S. Temmingh
- Department of Psychiatry and Mental HealthUniversity of Cape TownCape TownSouth Africa
| | - Sophia I. Thomopoulos
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of MedicineUniversity of Southern CaliforniaMarina del ReyCaliforniaUSA
| | | | - Diana Tordesillas‐Gutiérrez
- FIDMAG Germanes HospitalàriesBarcelonaSpain
- Neuroimaging Unit, Technological FacilitiesValdecilla Biomedical Research Institute IDIVALCantabriaSpain
| | - Julian N. Trollor
- Centre for Healthy Brain Ageing, School of PsychiatryUniversity of New South WalesKensingtonNew South WalesAustralia
| | - Jessica A. Turner
- College of Arts and SciencesGeorgia State UniversityAtlantaGeorgiaUSA
| | - Anne Uhlmann
- Department of Psychiatry and Mental HealthUniversity of Cape TownCape TownSouth Africa
| | - Odile A. van den Heuvel
- Department of PsychiatryAmsterdam University Medical Centre, Vrije UniversiteitAmsterdamNetherlands
| | - Dennis van den Meer
- Norwegian Centre for Mental Disorders Research (NORMENT)Institute of Clinical Medicine, University of OsloOsloNorway
- Division of Mental Health and AddictionInstitute of Clinical Medicine, University of OsloOsloNorway
- School of Mental Health and Neuroscience, Faculty of Health, Medicine and Life SciencesMaastricht UniversityMaastrichtNetherlands
| | - Nic J. A. van der Wee
- Department of PsychiatryLeiden University Medical CenterLeidenNetherlands
- Leiden Institute for Brain and CognitionLeiden University Medical CenterLeidenNetherlands
| | - Neeltje E. M. van Haren
- Department of Child and Adolescent Psychiatry/PsychologyErasmus University Medical Center, Sophia Children's HospitalRotterdamThe Netherlands
| | - Dennis van 't Ent
- Department of Biological PsychologyVrije UniversiteitAmsterdamNetherlands
| | - Theo G. M. van Erp
- Department of PsychiatryUniversity of California at IrvineIrvineCaliforniaUSA
- Center for the Neurobiology of Learning and MemoryUniversity of California IrvineIrvineCaliforniaUSA
- Institute of Community MedicineUniversity Medicine, Greifswald, University of GreifswaldGreifswaldGermany
| | - Ilya M. Veer
- Division of Mind and Brain Research, Department of Psychiatry and PsychotherapyCharité‐Universitätsmedizin BerlinBerlinGermany
| | - Dick J. Veltman
- Department of PsychiatryAmsterdam University Medical Centre, Vrije UniversiteitAmsterdamNetherlands
| | - Aristotle Voineskos
- Kimel Family Translational Imaging Genetics Laboratory, Campbell Family Mental Health Research InstituteCAMHCampbellCanada
- Department of PsychiatryUniversity of TorontoTorontoCanada
| | - Henry Völzke
- Institute of Community MedicineUniversity Medicine, Greifswald, University of GreifswaldGreifswaldGermany
- German Centre for Cardiovascular Research (DZHK), partner site GreifswaldGreifswaldGermany
- German Center for Diabetes Research (DZD), partner site GreifswaldGreifswaldGermany
| | - Henrik Walter
- Division of Mind and Brain Research, Department of Psychiatry and PsychotherapyCharité‐Universitätsmedizin BerlinBerlinGermany
| | - Esther Walton
- Department of PsychologyUniversity of BathBathUnited Kingdom
| | - Lei Wang
- Department of Psychiatry and Behavioral Sciences, Feinberg School of MedicineNorthwestern UniversityEvanstonIllinoisUSA
| | - Yang Wang
- Department of RadiologyMedical College of WisconsinMilwaukeeWisconsinUSA
| | - Thomas H. Wassink
- Department of Psychiatry, Carver College of MedicineThe University of IowaIowa CityIowaUSA
| | - Bernd Weber
- Institute for Experimental Epileptology and Cognition ResearchUniversity of BonnBonnGermany
| | - Wei Wen
- Centre for Healthy Brain Ageing, School of PsychiatryUniversity of New South WalesKensingtonNew South WalesAustralia
| | - John D. West
- Department of Radiology and Imaging SciencesIndiana University School of MedicineIndianapolisIndianaUSA
| | | | - Heather Whalley
- Division of PsychiatryUniversity of EdinburghEdinburghUnited Kingdom
| | - Lara M. Wierenga
- Developmental and Educational Psychology Unit, Institute of PsychologyLeiden UniversityLeidenNetherlands
| | - Katharina Wittfeld
- Department of Psychiatry and PsychotherapyUniversity Medicine Greifswald, University of GreifswaldGreifswaldGermany
- German Center for Neurodegenerative Diseases (DZNE)Site Rostock/GreifswaldGreifswaldGermany
| | - Daniel H. Wolf
- Department of PsychiatryUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Amanda Worker
- Department of Psychiatry, Djavad Mowafaghian Centre for Brain HealthUniversity of British ColumbiaVancouverCanada
| | | | - Kun Yang
- National High Magnetic Field LaboratoryFlorida State UniversityTallahasseeFloridaUSA
| | - Yulyia Yoncheva
- Department of Child and Adolescent Psychiatry, Child Study CenterNYU Langone HealthNew York CityNew YorkUSA
| | - Marcus V. Zanetti
- Laboratory of Psychiatric Neuroimaging, Departamento e Instituto de Psiquiatria, Hospital das Clinicas HCFMUSP, Faculdade de MedicinaUniversidade de São PauloSão PauloBrazil
- Instituto de Ensino e PesquisaHospital Sírio‐LibanêsSão PauloBrazil
| | - Georg C. Ziegler
- Division of Molecular Psychiatry, Center of Mental HealthUniversity of WürzburgWürzburgGermany
| | | | - Paul M. Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of MedicineUniversity of Southern CaliforniaMarina del ReyCaliforniaUSA
| | - Danai Dima
- Department of NeuroimagingInstitute of Psychiatry, Psychology and Neuroscience, King's College LondonLondonUnited Kingdom
- Department of Psychology, School of Arts and Social SciencesCity University of LondonLondonUnited Kingdom
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20
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Blinkouskaya Y, Caçoilo A, Gollamudi T, Jalalian S, Weickenmeier J. Brain aging mechanisms with mechanical manifestations. Mech Ageing Dev 2021; 200:111575. [PMID: 34600936 PMCID: PMC8627478 DOI: 10.1016/j.mad.2021.111575] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2021] [Revised: 09/09/2021] [Accepted: 09/22/2021] [Indexed: 12/14/2022]
Abstract
Brain aging is a complex process that affects everything from the subcellular to the organ level, begins early in life, and accelerates with age. Morphologically, brain aging is primarily characterized by brain volume loss, cortical thinning, white matter degradation, loss of gyrification, and ventricular enlargement. Pathophysiologically, brain aging is associated with neuron cell shrinking, dendritic degeneration, demyelination, small vessel disease, metabolic slowing, microglial activation, and the formation of white matter lesions. In recent years, the mechanics community has demonstrated increasing interest in modeling the brain's (bio)mechanical behavior and uses constitutive modeling to predict shape changes of anatomically accurate finite element brain models in health and disease. Here, we pursue two objectives. First, we review existing imaging-based data on white and gray matter atrophy rates and organ-level aging patterns. This data is required to calibrate and validate constitutive brain models. Second, we review the most critical cell- and tissue-level aging mechanisms that drive white and gray matter changes. We focuse on aging mechanisms that ultimately manifest as organ-level shape changes based on the idea that the integration of imaging and mechanical modeling may help identify the tipping point when normal aging ends and pathological neurodegeneration begins.
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Affiliation(s)
- Yana Blinkouskaya
- Department of Mechanical Engineering, Stevens Institute of Technology, Hoboken, NJ 07030, United States
| | - Andreia Caçoilo
- Department of Mechanical Engineering, Stevens Institute of Technology, Hoboken, NJ 07030, United States
| | - Trisha Gollamudi
- Department of Biomedical Engineering, Stevens Institute of Technology, Hoboken, NJ 07030, United States
| | - Shima Jalalian
- Department of Mechanical Engineering, Stevens Institute of Technology, Hoboken, NJ 07030, United States
| | - Johannes Weickenmeier
- Department of Mechanical Engineering, Stevens Institute of Technology, Hoboken, NJ 07030, United States.
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21
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Andrews EF, Jacqmot O, Espinheira Gomes FNCM, Sha MF, Niogi SN, Johnson PJ. Characterizing the canine and feline optic pathways in vivo with diffusion MRI. Vet Ophthalmol 2021; 25 Suppl 1:60-71. [PMID: 34784441 DOI: 10.1111/vop.12940] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 09/12/2021] [Accepted: 09/15/2021] [Indexed: 11/29/2022]
Abstract
The visual system is known to be vital for cognition and perception in the feline and canine and much behavioral research for these species has used visual stimuli and focused on visual perception. There has been extensive investigations into the visual pathway in cats and dogs via histological and neurobiological methods, however to date, only one study has mapped the canine optic pathway in vivo. Advanced imaging methods such as diffusion MRI (DTI) have been routinely used in human research to study the visual system in vivo. This study applied DTI imaging methods to assess and characterize the optic pathway of feline and canine subjects in vivo. The optic nerve (ON), optic tract (OT), and optic radiation (OR) were successfully delineated for each species and the average volume and FA for each tract is reported. The application of DTI to map the optic pathway for canine and feline subjects provides a healthy baseline for comparison in future studies.
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Affiliation(s)
- Erica F Andrews
- Cornell College of Veterinary Medicine, Cornell University, Ithaca, NY, USA
| | - Olivier Jacqmot
- Anatomical Research and Clinical Studies (ARCS), Vrije Universiteit Brussel, Brussels, Belgium.,MOVE-HIM (Morpho Veterinary and Human Imaging) Brussels, Universitair Ziekenhuis Brussel (UZ Brussel), Brussels, Belgium
| | | | - Megan F Sha
- Cornell College of Veterinary Medicine, Cornell University, Ithaca, NY, USA
| | - Sumit N Niogi
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - Philippa J Johnson
- Cornell College of Veterinary Medicine, Cornell University, Ithaca, NY, USA
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22
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Fitzroy AB, Kainec KA, Spencer RMC. Ageing-related changes in nap neuroscillatory activity are mediated and moderated by grey matter volume. Eur J Neurosci 2021; 54:7332-7354. [PMID: 34541728 PMCID: PMC8809479 DOI: 10.1111/ejn.15468] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Revised: 08/14/2021] [Accepted: 09/10/2021] [Indexed: 12/03/2022]
Abstract
Ageing‐related changes in grey matter result in changes in the intensity and topography of sleep neural activity. However, it is unclear whether these findings can be explained by ageing‐related differences in sleep pressure or circadian influence. The current study used high‐density electroencephalography to assess how grey matter volume differences between young and older adults mediate and moderate neuroscillatory activity differences during a midday nap following a motor sequencing task. Delta, theta, and sigma amplitude were reduced in older relative to young adults, especially over frontocentral scalp, leading to increases in relative delta frontality and relative sigma lateral centroposteriority. Delta reductions in older adults were mediated by grey matter loss in frontal medial cortex, primary motor cortex, thalamus, caudate, putamen, and pallidum, and were moderated by putamen grey matter volume. Theta reductions were mediated by grey matter loss in primary motor cortex, thalamus, and caudate, and were moderated by putamen and pallidum grey matter volume. Sigma changes were moderated by putamen and pallidum grey matter volume. Moderation results suggested that across frequencies, young adults with more grey matter had increased activity, whereas older adults with more grey matter had unchanged or decreased activity. These results provide a critical extension of previous findings from overnight sleep in a midday nap, indicating that they are not driven by sleep pressure or circadian confounds. Moreover, these results suggest brain regions associated with motor sequence learning contribute to sleep neural activity following a motor sequencing task.
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Affiliation(s)
- Ahren B Fitzroy
- Neuroscience and Behavior Program, University of Massachusetts Amherst, Amherst, MA, USA.,Department of Psychological and Brain Sciences, University of Massachusetts Amherst, Amherst, MA, USA
| | - Kyle A Kainec
- Neuroscience and Behavior Program, University of Massachusetts Amherst, Amherst, MA, USA.,Department of Psychological and Brain Sciences, University of Massachusetts Amherst, Amherst, MA, USA
| | - Rebecca M C Spencer
- Neuroscience and Behavior Program, University of Massachusetts Amherst, Amherst, MA, USA.,Department of Psychological and Brain Sciences, University of Massachusetts Amherst, Amherst, MA, USA.,Institute for Applied Life Sciences, University of Massachusetts Amherst, Amherst, MA, USA
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23
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Shokri-Kojori E, Bennett IJ, Tomeldan ZA, Krawczyk DC, Rypma B. Estimates of brain age for gray matter and white matter in younger and older adults: Insights into human intelligence. Brain Res 2021; 1763:147431. [PMID: 33737067 PMCID: PMC8428193 DOI: 10.1016/j.brainres.2021.147431] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Revised: 02/01/2021] [Accepted: 03/10/2021] [Indexed: 12/18/2022]
Abstract
Aging entails a multifaceted complex of changes in macro- and micro-structural properties of human brain gray matter (GM) and white matter (WM) tissues, as well as in intellectual abilities. To better capture tissue-specific brain aging, we combined volume and distribution properties of diffusivity indices to derive subject-specific age scores for each tissue. We compared age-related variance between younger and older adults for GM and WM age scores, and tested whether tissue-specific age scores could explain different effects of aging on fluid (Gf) and crystalized (Gc) intelligence in younger and older adults. Chronological age was strongly associated with GM (R2 = 0.73) and WM (R2 = 0.57) age scores. The GM age score accounted for significantly more variance in chronological age in younger relative to older adults (p < 0.001), whereas the WM age score accounted for significantly more variance in chronological age in older compared to younger adults (p < 0.025). Consistent with existing literature, younger adults outperformed older adults in Gf while older adults outperformed younger adults in Gc. The GM age score was negatively associated with Gf in younger adults (p < 0.02), whereas the WM age score was negatively associated with Gc in older adults (p < 0.02). Our results provide evidence for differences in the effects of age on GM and WM in younger versus older adults that may contribute to age-related differences in Gf and Gc.
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Affiliation(s)
- Ehsan Shokri-Kojori
- Center for BrainHealth®, School of Behavioral and Brain Sciences, The University of Texas at Dallas, Dallas, TX, USA.
| | - Ilana J Bennett
- Center for BrainHealth®, School of Behavioral and Brain Sciences, The University of Texas at Dallas, Dallas, TX, USA; Department of Psychology, University of California, Riverside, Riverside, CA, USA
| | - Zuri A Tomeldan
- Center for BrainHealth®, School of Behavioral and Brain Sciences, The University of Texas at Dallas, Dallas, TX, USA
| | - Daniel C Krawczyk
- Center for BrainHealth®, School of Behavioral and Brain Sciences, The University of Texas at Dallas, Dallas, TX, USA; Department of Psychiatry, The University of Texas Southwestern Medical Center at Dallas, Dallas, TX, USA
| | - Bart Rypma
- Center for BrainHealth®, School of Behavioral and Brain Sciences, The University of Texas at Dallas, Dallas, TX, USA; Department of Psychiatry, The University of Texas Southwestern Medical Center at Dallas, Dallas, TX, USA
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24
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Xu S, Yao X, Han L, Lv Y, Bu X, Huang G, Fan Y, Yu T, Huang G. Brain network analyses of diffusion tensor imaging for brain aging. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2021; 18:6066-6078. [PMID: 34517523 DOI: 10.3934/mbe.2021303] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
The approach of graph-based diffusion tensor imaging (DTI) networks has been used to explore the complicated structural connectivity of brain aging. In this study, the changes of DTI networks of brain aging were quantitatively and qualitatively investigated by comparing the characteristics of brain network. A cohort of 60 volunteers was enrolled and equally divided into young adults (YA) and older adults (OA) groups. The network characteristics of critical nodes, path length (Lp), clustering coefficient (Cp), global efficiency (Eglobal), local efficiency (Elocal), strength (Sp), and small world attribute (σ) were employed to evaluate the DTI networks at the levels of whole brain, bilateral hemispheres and critical brain regions. The correlations between each network characteristic and age were predicted, respectively. Our findings suggested that the DTI networks produced significant changes in network configurations at the critical nodes and node edges for the YA and OA groups. The analysis of whole brains network revealed that Lp, Cp increased (p < 0.05, positive correlation), Eglobal, Elocal, Sp decreased (p < 0.05, negative correlation), and σ unchanged (p ≥ 0.05, non-correlation) between the YA and OA groups. The analyses of bilateral hemispheres and brain regions showed similar results as that of the whole-brain analysis. Therefore the proposed scheme of DTI networks could be used to evaluate the WM changes of brain aging, and the network characteristics of critical nodes exhibited valuable indications for WM degeneration.
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Affiliation(s)
- Song Xu
- College of Medical Imaging, Shanghai University of Medicine and Health Sciences, Shanghai 201318, China
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Xufeng Yao
- College of Medical Imaging, Shanghai University of Medicine and Health Sciences, Shanghai 201318, China
- Shanghai Key Laboratory of Molecular Imaging, Shanghai University of Medicine and Health Sciences, Shanghai 201318, China
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Liting Han
- College of Medical Imaging, Shanghai University of Medicine and Health Sciences, Shanghai 201318, China
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Yuting Lv
- College of Medical Imaging, Shanghai University of Medicine and Health Sciences, Shanghai 201318, China
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Xixi Bu
- College of Medical Imaging, Shanghai University of Medicine and Health Sciences, Shanghai 201318, China
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Gan Huang
- College of Medical Imaging, Shanghai University of Medicine and Health Sciences, Shanghai 201318, China
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Yifeng Fan
- School of Medical Imaging, Hangzhou Medical College, Hangzhou 310053, China
| | - Tonggang Yu
- Shanghai Gamma Knife Hospital, Fudan University, Shanghai 200235, China
| | - Gang Huang
- College of Medical Imaging, Shanghai University of Medicine and Health Sciences, Shanghai 201318, China
- Shanghai Key Laboratory of Molecular Imaging, Shanghai University of Medicine and Health Sciences, Shanghai 201318, China
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
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25
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Irimia A. Cross-Sectional Volumes and Trajectories of the Human Brain, Gray Matter, White Matter and Cerebrospinal Fluid in 9473 Typically Aging Adults. Neuroinformatics 2021; 19:347-366. [PMID: 32856237 DOI: 10.1007/s12021-020-09480-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Accurate knowledge of adult human brain volume (BV) is critical for studies of aging- and disease-related brain alterations, and for monitoring the trajectories of neural and cognitive functions in conditions like Alzheimer's disease and traumatic brain injury. This scoping meta-analysis aggregates normative reference values for BV and three related volumetrics-gray matter volume (GMV), white matter volume (WMV) and cerebrospinal fluid volume (CSFV)-from typically-aging adults studied cross-sectionally using magnetic resonance imaging (MRI). Drawing from an aggregate sample of 9473 adults, this study provides (A) regression coefficients β describing the age-dependent trajectories of volumetric measures by sex within the range from 20 to 70 years based on both linear and quadratic models, and (B) average values for BV, GMV, WMV and CSFV at the representative ages of 20 (young age), 45 (middle age) and 70 (old age). The results provided synthesize ~20 years of brain volumetrics research and allow one to estimate BV at any age between 20 and 70. Importantly, however, such estimates should be used and interpreted with caution because they depend on MRI hardware specifications (e.g. scanner manufacturer, magnetic field strength), data acquisition parameters (e.g. spatial resolution, weighting), and brain segmentation algorithms. Guidelines are proposed to facilitate future meta- and mega-analyses of brain volumetrics.
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Affiliation(s)
- Andrei Irimia
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, 3715 McClintock Avenue, Los Angeles, CA, 90089, USA.
- Denney Research Center, Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, 1042 Downey Way, Los Angeles, CA, 90089, USA.
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26
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Barry EF, Loftus JP, Luh WM, de Leon MJ, Niogi SN, Johnson PJ. Diffusion tensor-based analysis of white matter in the healthy aging canine brain. Neurobiol Aging 2021; 105:129-136. [PMID: 34062488 DOI: 10.1016/j.neurobiolaging.2021.04.021] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Revised: 04/23/2021] [Accepted: 04/24/2021] [Indexed: 12/14/2022]
Abstract
White matter dysfunction and degeneration have been a topic of great interest in healthy and pathological aging. While ex vivo studies have investigated age-related changes in canines, little in vivo canine aging research exists. Quantitative diffusion MRI such as diffusion tensor imaging (DTI) has demonstrated aging and neurodegenerative white matter changes in humans. However, this method has not been applied and adapted in vivo to canine populations. This study aimed to test the hypothesis that white matter diffusion changes frequently reported in human aging are also found in aged canines. The study used Tract Based Spatial Statistics (TBSS) and a region of interest (ROI) approach to investigate age related changes in fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AxD) and radial diffusivity (RD). The results show that, compared to younger animals, aged canines have significant decreases in FA in parietal and temporal regions as well as the corpus callosum and fornix. Additionally, AxD decreases were observed in parietal, frontal, and midbrain regions. Similarly, an age- related increase in RD was observed in the right parietal lobe while MD decreases were found in the midbrain. These findings suggest that canine samples show commonalities with human brain aging as both exhibit similar white matter diffusion tensor changes with increasing age.
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Affiliation(s)
- Erica F Barry
- Cornell College of Veterinary Medicine, Cornell University, Ithaca, NY
| | - John P Loftus
- Cornell College of Veterinary Medicine, Cornell University, Ithaca, NY
| | - Wen-Ming Luh
- National Institute on Aging, Baltimore, Maryland
| | - Mony J de Leon
- Department of Radiology, Weill Cornell Medicine, New York, NY
| | - Sumit N Niogi
- Department of Radiology, Weill Cornell Medicine, New York, NY
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27
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Hays CC, Zlatar ZZ, Meloy MJ, Osuna J, Liu TT, Galasko DR, Wierenga CE. Anterior Cingulate Structure and Perfusion is Associated with Cerebrospinal Fluid Tau among Cognitively Normal Older Adult APOEɛ4 Carriers. J Alzheimers Dis 2021; 73:87-101. [PMID: 31743999 DOI: 10.3233/jad-190504] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Evidence suggests the ɛ4 allele of the apolipoprotein E (APOE) gene may accelerate an age-related process of cortical thickening and cerebral blood flow (CBF) reduction in the anterior cingulate cortex (ACC). Although the neural basis of this association remains unclear, evidence suggests it might reflect early neurodegenerative processes. However, to date, associations between cerebrospinal fluid (CSF) biomarkers of neurodegeneration, such as CSF tau, and APOE-related alterations in ACC cortical thickness (CTH) and CBF have yet to be explored. The current study explored the interaction of CSF tau and APOE genotype (ɛ4+, ɛ4-) on FreeSurfer-derived CTH and arterial spin labeling MRI-measured resting CBF in the ACC (caudal ACC [cACC] and rostral ACC [rACC]) among a sample of 45 cognitively normal older adults. Secondary analyses also examined associations between APOE, CTH/CBF, and cognitive performance. In the cACC, higher CSF tau was associated with higher CTH and lower CBF in ɛ4+, whereas these relationships were not evident in ɛ4-. In the rACC, higher CSF tau was associated with higher CTH for both ɛ4+ and ɛ4-, and with lower CBF only in ɛ4+. Significant interactions of CSF tau and APOE on CTH/CBF were not observed in two posterior reference regions implicated in Alzheimer's disease. Secondary analyses revealed a negative relationship between cACC CTH and executive functioning in ɛ4+ and a positive relationship in ɛ4-. Findings suggest the presence of an ɛ4-related pattern of increased CTH and reduced CBF in the ACC that is associated with biomarkers of neurodegeneration and subtle decrements in cognition.
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Affiliation(s)
- Chelsea C Hays
- VA San Diego Healthcare System, San Diego, CA, USA.,SDSU/UC San Diego Joint Doctoral Program in Clinical Psychology, San Diego, CA, USA
| | - Zvinka Z Zlatar
- Department of Psychiatry, UC San Diego, La Jolla, CA, USA.,SDSU/UC San Diego Joint Doctoral Program in Clinical Psychology, San Diego, CA, USA
| | - M J Meloy
- VA San Diego Healthcare System, San Diego, CA, USA
| | - Jessica Osuna
- VA San Diego Healthcare System, San Diego, CA, USA.,Department of Psychiatry, UC San Diego, La Jolla, CA, USA
| | - Thomas T Liu
- Department of Radiology, UC San Diego, La Jolla, CA, USA
| | - Douglas R Galasko
- VA San Diego Healthcare System, San Diego, CA, USA.,Department of Neurosciences, UC San Diego, La Jolla, CA, USA
| | - Christina E Wierenga
- VA San Diego Healthcare System, San Diego, CA, USA.,Department of Psychiatry, UC San Diego, La Jolla, CA, USA.,SDSU/UC San Diego Joint Doctoral Program in Clinical Psychology, San Diego, CA, USA
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28
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Hagiwara A, Fujimoto K, Kamagata K, Murata S, Irie R, Kaga H, Someya Y, Andica C, Fujita S, Kato S, Fukunaga I, Wada A, Hori M, Tamura Y, Kawamori R, Watada H, Aoki S. Age-Related Changes in Relaxation Times, Proton Density, Myelin, and Tissue Volumes in Adult Brain Analyzed by 2-Dimensional Quantitative Synthetic Magnetic Resonance Imaging. Invest Radiol 2021; 56:163-172. [PMID: 32858581 PMCID: PMC7864648 DOI: 10.1097/rli.0000000000000720] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Revised: 07/20/2020] [Accepted: 07/20/2020] [Indexed: 11/04/2022]
Abstract
OBJECTIVES Quantitative synthetic magnetic resonance imaging (MRI) enables the determination of fundamental tissue properties, namely, T1 and T2 relaxation times and proton density (PD), in a single scan. Myelin estimation and brain segmentation based on these quantitative values can also be performed automatically. This study aimed to reveal the changes in tissue characteristics and volumes of the brain according to age and provide age-specific reference values obtained by quantitative synthetic MRI. MATERIALS AND METHODS This was a prospective study of healthy subjects with no history of brain diseases scanned with a multidynamic multiecho sequence for simultaneous measurement of relaxometry of T1, T2, and PD. We performed myelin estimation and brain volumetry based on these values. We performed volume-of-interest analysis on both gray matter (GM) and white matter (WM) regions for T1, T2, PD, and myelin volume fraction maps. Tissue volumes were calculated in the whole brain, producing brain parenchymal volume, GM volume, WM volume, and myelin volume. These volumes were normalized by intracranial volume to a brain parenchymal fraction, GM fraction, WM fraction, and myelin fraction (MyF). We examined the changes in the mean regional quantitative values and segmented tissue volumes according to age. RESULTS We analyzed data of 114 adults (53 men and 61 women; median age, 66.5 years; range, 21-86 years). T1, T2, and PD values showed quadratic changes according to age and stayed stable or decreased until around 60 years of age and increased thereafter. Myelin volume fraction showed a reversed trend. Brain parenchymal fraction and GM fraction decreased throughout all ages. The approximation curves showed that WM fraction and MyF gradually increased until around the 40s to 50s and decreased thereafter. A significant decline in MyF was first noted in the 60s age group (Tukey test, P < 0.001). CONCLUSIONS Our study showed changes according to age in tissue characteristic values and brain volumes using quantitative synthetic MRI. The reference values for age demonstrated in this study may be useful to discriminate brain disorders from healthy brains.
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Affiliation(s)
- Akifumi Hagiwara
- From the Department of Radiology, Juntendo University Graduate School of Medicine
| | - Kotaro Fujimoto
- From the Department of Radiology, Juntendo University Graduate School of Medicine
- Department of Radiology, Graduate School of Medicine, The University of Tokyo
| | - Koji Kamagata
- From the Department of Radiology, Juntendo University Graduate School of Medicine
| | - Syo Murata
- From the Department of Radiology, Juntendo University Graduate School of Medicine
| | - Ryusuke Irie
- From the Department of Radiology, Juntendo University Graduate School of Medicine
| | - Hideyoshi Kaga
- Department of Metabolism & Endocrinology, Juntendo University Graduate School of Medicine
| | - Yuki Someya
- Sportology Center, Juntendo University Graduate School of Medicine
| | - Christina Andica
- From the Department of Radiology, Juntendo University Graduate School of Medicine
| | - Shohei Fujita
- From the Department of Radiology, Juntendo University Graduate School of Medicine
- Department of Radiology, Graduate School of Medicine, The University of Tokyo
| | - Shimpei Kato
- From the Department of Radiology, Juntendo University Graduate School of Medicine
- Department of Radiology, Graduate School of Medicine, The University of Tokyo
| | - Issei Fukunaga
- Department of Radiological Technology, Faculty of Health Science, Juntendo University
| | - Akihiko Wada
- From the Department of Radiology, Juntendo University Graduate School of Medicine
| | - Masaaki Hori
- From the Department of Radiology, Juntendo University Graduate School of Medicine
- Department of Radiology, Toho University Omori Medical Center, Tokyo, Japan
| | - Yoshifumi Tamura
- Department of Metabolism & Endocrinology, Juntendo University Graduate School of Medicine
- Sportology Center, Juntendo University Graduate School of Medicine
| | - Ryuzo Kawamori
- Department of Metabolism & Endocrinology, Juntendo University Graduate School of Medicine
- Sportology Center, Juntendo University Graduate School of Medicine
| | - Hirotaka Watada
- Department of Metabolism & Endocrinology, Juntendo University Graduate School of Medicine
- Sportology Center, Juntendo University Graduate School of Medicine
| | - Shigeki Aoki
- From the Department of Radiology, Juntendo University Graduate School of Medicine
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29
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Hopkins WD, Mareno MC, Webb SJN, Schapiro SJ, Raghanti MA, Sherwood CC. Age-related changes in chimpanzee (Pan troglodytes) cognition: Cross-sectional and longitudinal analyses. Am J Primatol 2021; 83:e23214. [PMID: 33169860 PMCID: PMC7904603 DOI: 10.1002/ajp.23214] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Revised: 08/17/2020] [Accepted: 10/25/2020] [Indexed: 01/06/2023]
Abstract
Chimpanzees are the species most closely related to humans, yet age-related changes in brain and cognition remain poorly understood. The lack of studies on age-related changes in cognition in chimpanzees is particularly unfortunate in light of the recent evidence demonstrating that this species naturally develops Alzheimer's disease (AD) neuropathology. Here, we tested 213 young, middle-aged, and elderly captive chimpanzees on the primate cognitive test battery (PCTB), a set of 13 tasks that assess physical and social cognition in nonhuman primates. A subset of these chimpanzees (n = 146) was tested a second time on a portion of the PCTB tasks as a means of evaluating longitudinal changes in cognition. Cross-sectional analyses revealed a significant quadratic association between age and cognition with younger and older chimpanzees performing more poorly than middle-aged individuals. Longitudinal analyses showed that the oldest chimpanzees at the time of the first test showed the greatest decline in cognition, although the effect was mild. The collective data show that chimpanzees, like other nonhuman primates, show age-related decline in cognition. Further investigations into whether the observed cognitive decline is associated with AD pathologies in chimpanzees would be invaluable in understanding the comparative biology of aging and neuropathology in primates.
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Affiliation(s)
- William D Hopkins
- Department of Comparative Medicine, University of Texas MD Anderson Cancer Center, Bastrop, TX, USA
| | - Mary Catherine Mareno
- Department of Comparative Medicine, University of Texas MD Anderson Cancer Center, Bastrop, TX, USA
| | - Sarah J Neal Webb
- Department of Comparative Medicine, University of Texas MD Anderson Cancer Center, Bastrop, TX, USA
| | - Steven J Schapiro
- Department of Comparative Medicine, University of Texas MD Anderson Cancer Center, Bastrop, TX, USA
- Department of Experimental Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Mary Ann Raghanti
- Department of Anthropology, School of Biomedical Sciences, and Brain Health Research Institute Kent State University, Kent, Ohio 44242, USA
| | - Chet C Sherwood
- Department of Anthropology and Center for the Advanced Study of Human Paleobiology, The George Washington University, Washington, DC, USA
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30
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Che Mohd Nassir CMN, Mohamad Ghazali M, Ahmad Safri A, Jaffer U, Abdullah WZ, Idris NS, Muzaimi M. Elevated Circulating Microparticle Subpopulations in Incidental Cerebral White Matter Hyperintensities: A Multimodal Study. Brain Sci 2021; 11:133. [PMID: 33498429 PMCID: PMC7909442 DOI: 10.3390/brainsci11020133] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Revised: 01/13/2021] [Accepted: 01/19/2021] [Indexed: 01/02/2023] Open
Abstract
Asymptomatic (or "silent") manifestations of cerebral small vessel disease (CSVD) are widely recognized through incidental findings of white matter hyperintensities (WMHs) as a result of magnetic resonance imaging (MRI). This study aims to examine the potential associations of surrogate markers for the evaluation of white matter integrity in CSVD among asymptomatic individuals through a battery of profiling involving QRISK2 cardiocerebrovascular risk prediction, neuroimaging, neurocognitive evaluation, and microparticles (MPs) titers. Sixty asymptomatic subjects (mean age: 39.83 ± 11.50 years) with low to moderate QRISK2 scores were recruited and underwent neurocognitive evaluation for memory and cognitive performance, peripheral venous blood collection for enumeration of selected MPs subpopulations, and 3T MRI brain scan with specific diffusion MRI (dMRI) sequences inclusive of diffusion tensor imaging (DTI). WMHs were detected in 20 subjects (33%). Older subjects (mean age: 46.00 ± 12.00 years) had higher WMHs prevalence, associated with higher QRISK2 score and reduced processing speed. They also had significantly higher mean percentage of platelet (CD62P)- and leukocyte (CD62L)-derived MPs. No association was found between reduced white matter integrity-especially at the left superior longitudinal fasciculus (LSLF)-with age and neurocognitive function; however, LSLF was associated with higher QRISK2 score, total MPs, and CD62L- and endothelial cell-derived MPs (CD146). Therefore, this study establishes these multimodal associations as potential surrogate markers for "silent" CSVD manifestations in the well-characterized cardiocerebrovascular demographic of relatively young, neurologically asymptomatic adults. Furthermore, to the best of our knowledge, this study is the first to exhibit elevated MP counts in asymptomatic CSVD (i.e., CD62P and CD62L), which warrants further delineation.
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Affiliation(s)
- Che Mohd Nasril Che Mohd Nassir
- Department of Neurosciences, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian 16150, Kelantan, Malaysia; (C.M.N.C.M.N.); (M.M.G.); (A.A.S.); (U.J.)
| | - Mazira Mohamad Ghazali
- Department of Neurosciences, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian 16150, Kelantan, Malaysia; (C.M.N.C.M.N.); (M.M.G.); (A.A.S.); (U.J.)
| | - Amanina Ahmad Safri
- Department of Neurosciences, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian 16150, Kelantan, Malaysia; (C.M.N.C.M.N.); (M.M.G.); (A.A.S.); (U.J.)
| | - Usman Jaffer
- Department of Neurosciences, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian 16150, Kelantan, Malaysia; (C.M.N.C.M.N.); (M.M.G.); (A.A.S.); (U.J.)
| | - Wan Zaidah Abdullah
- Department of Haematology, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian 16150, Kelantan, Malaysia;
- Hospital Universiti Sains Malaysia, Jalan Raja Perempuan Zainab II, Kubang Kerian 16150, Kelantan, Malaysia;
| | - Nur Suhaila Idris
- Hospital Universiti Sains Malaysia, Jalan Raja Perempuan Zainab II, Kubang Kerian 16150, Kelantan, Malaysia;
- Department of Family Medicine, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian 16150, Kelantan, Malaysia
| | - Mustapha Muzaimi
- Department of Neurosciences, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian 16150, Kelantan, Malaysia; (C.M.N.C.M.N.); (M.M.G.); (A.A.S.); (U.J.)
- Hospital Universiti Sains Malaysia, Jalan Raja Perempuan Zainab II, Kubang Kerian 16150, Kelantan, Malaysia;
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Abrol A, Fu Z, Salman M, Silva R, Du Y, Plis S, Calhoun V. Deep learning encodes robust discriminative neuroimaging representations to outperform standard machine learning. Nat Commun 2021; 12:353. [PMID: 33441557 PMCID: PMC7806588 DOI: 10.1038/s41467-020-20655-6] [Citation(s) in RCA: 87] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Accepted: 12/09/2020] [Indexed: 12/27/2022] Open
Abstract
Recent critical commentaries unfavorably compare deep learning (DL) with standard machine learning (SML) approaches for brain imaging data analysis. However, their conclusions are often based on pre-engineered features depriving DL of its main advantage — representation learning. We conduct a large-scale systematic comparison profiled in multiple classification and regression tasks on structural MRI images and show the importance of representation learning for DL. Results show that if trained following prevalent DL practices, DL methods have the potential to scale particularly well and substantially improve compared to SML methods, while also presenting a lower asymptotic complexity in relative computational time, despite being more complex. We also demonstrate that DL embeddings span comprehensible task-specific projection spectra and that DL consistently localizes task-discriminative brain biomarkers. Our findings highlight the presence of nonlinearities in neuroimaging data that DL can exploit to generate superior task-discriminative representations for characterizing the human brain. Recent critical commentaries unfavorably compare deep learning (DL) with standard machine learning (SML) for brain imaging data analysis. Here, the authors show that if trained following prevalent DL practices, DL methods substantially improve compared to SML methods by encoding robust discriminative brain representations.
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Affiliation(s)
- Anees Abrol
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA.
| | - Zening Fu
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Mustafa Salman
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA.,School of Electrical & Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Rogers Silva
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Yuhui Du
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA.,School of Computer & Information Technology, Shanxi University, Taiyuan, China
| | - Sergey Plis
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Vince Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA.,School of Electrical & Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA
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Glutamine + glutamate level predicts the magnitude of microstructural organization in the gray matter in the healthy elderly. Int Psychogeriatr 2021; 33:21-29. [PMID: 31578159 PMCID: PMC8482373 DOI: 10.1017/s1041610219001418] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
BACKGROUND Diffusion tensor imaging (DTI), which is a technique for measuring the degree and direction of movement of water molecules in tissue, has been widely used to noninvasively assess white matter (WM) or gray matter (GM) microstructures in vivo. Mean diffusivity (MD), which is the average diffusion across all directions, has been considered as a marker of WM tract degeneration or extracellular space enlargement in GM. Recent lines of evidence suggest that cortical MD can better identify early-stage Alzheimer's disease than structural morphometric parameters in magnetic resonance imaging. However, knowledge of the relationships between cortical MD and other biological factors in the same cortical region, e.g. metabolites, is still limited. METHODS Thirty-three healthy elderly individuals [aged 50-77 years (mean, 63.8±7.4 years); 11 males and 22 females] were enrolled. We estimated the associations between cortical MD and neurotransmitter levels. Specifically, we measured levels of γ-aminobutyric acid (GABA) and glutamate + glutamine (Glx), which are inhibitory and excitatory neurotransmitters, respectively, in medial prefrontal cortex (mPFC) and posterior cingulate cortex (PCC) using MEGA-PRESS magnetic resonance spectroscopy, and we measured regional cortical MD using DTI. RESULTS Cortical MD was significantly negatively associated with Glx levels in both mPFC and PCC. No significant association was observed between cortical MD and GABA levels in either GM region. CONCLUSION Our findings suggest that degeneration of microstructural organization in GM, as determined on the basis of cortical MD measured by DTI, is accompanied by the decline of Glx metabolism within the same GM region.
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Morand A, Segobin S, Lecouvey G, Gonneaud J, Eustache F, Rauchs G, Desgranges B. Brain Substrates of Time-Based Prospective Memory Decline in Aging: A Voxel-Based Morphometry and Diffusion Tensor Imaging Study. Cereb Cortex 2021; 31:396-409. [PMID: 32935836 DOI: 10.1093/cercor/bhaa232] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Revised: 06/29/2020] [Accepted: 07/28/2020] [Indexed: 11/14/2022] Open
Abstract
Time-based prospective memory (TBPM) allows us to remember to perform intended actions at a specific time in the future. TBPM is sensitive to the effects of age, but the neural substrates of this decline are still poorly understood. The aim of the present study was thus to better characterize the brain substrates of the age-related decline in TBPM, focusing on macrostructural gray matter and microstructural white matter integrity. We administered a TBPM task to 22 healthy young (26 ± 5.2 years) and 23 older (63 ± 5.9 years) participants, who also underwent volumetric magnetic resonance imaging and diffusion tensor imaging scans. Neuroimaging analyses revealed lower gray matter volumes in several brain areas in older participants, but these did not correlate with TBPM performance. By contrast, an age-related decline in fractional anisotropy in several white-matter tracts connecting frontal and occipital regions did correlate with TBPM performance, whereas there was no significant correlation in healthy young subjects. According to the literature, these tracts are connected to the anterior prefrontal cortex and the thalamus, 2 structures involved in TBPM. These results confirm the view that a disconnection process occurs in aging and contributes to cognitive decline.
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Affiliation(s)
| | | | - Grégory Lecouvey
- Normandie Université, UNICAEN, PSL Université Paris, 14000 Caen, France
| | - Julie Gonneaud
- Normandie Université, UNICAEN, PSL Université Paris, 14000 Caen, France
| | - Francis Eustache
- Normandie Université, UNICAEN, PSL Université Paris, 14000 Caen, France
| | - Géraldine Rauchs
- Normandie Université, UNICAEN, PSL Université Paris, 14000 Caen, France
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Gray DT, De La Peña NM, Umapathy L, Burke SN, Engle JR, Trouard TP, Barnes CA. Auditory and Visual System White Matter Is Differentially Impacted by Normative Aging in Macaques. J Neurosci 2020; 40:8913-8923. [PMID: 33051354 PMCID: PMC7659446 DOI: 10.1523/jneurosci.1163-20.2020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Revised: 08/06/2020] [Accepted: 10/04/2020] [Indexed: 11/21/2022] Open
Abstract
Deficits in auditory and visual processing are commonly encountered by older individuals. In addition to the relatively well described age-associated pathologies that reduce sensory processing at the level of the cochlea and eye, multiple changes occur along the ascending auditory and visual pathways that further reduce sensory function in each domain. One fundamental question that remains to be directly addressed is whether the structure and function of the central auditory and visual systems follow similar trajectories across the lifespan or sustain the impacts of brain aging independently. The present study used diffusion magnetic resonance imaging and electrophysiological assessments of auditory and visual system function in adult and aged macaques to better understand how age-related changes in white matter connectivity at multiple levels of each sensory system might impact auditory and visual function. In particular, the fractional anisotropy (FA) of auditory and visual system thalamocortical and interhemispheric corticocortical connections was estimated using probabilistic tractography analyses. Sensory processing and sensory system FA were both reduced in older animals compared with younger adults. Corticocortical FA was significantly reduced only in white matter of the auditory system of aged monkeys, while thalamocortical FA was lower only in visual system white matter of the same animals. Importantly, these structural alterations were significantly associated with sensory function within each domain. Together, these results indicate that age-associated deficits in auditory and visual processing emerge in part from microstructural alterations to specific sensory white matter tracts, and not from general differences in white matter condition across the aging brain.SIGNIFICANCE STATEMENT Age-associated deficits in sensory processing arise from structural and functional alterations to both peripheral sensory organs and central brain regions. It remains unclear whether different sensory systems undergo similar or distinct trajectories in function across the lifespan. To provide novel insights into this question, this study combines electrophysiological assessments of auditory and visual function with diffusion MRI in aged macaques. The results suggest that age-related sensory processing deficits in part result from factors that impact the condition of specific white matter tracts, and not from general decreases in connectivity between sensory brain regions. Such anatomic specificity argues for a framework aimed at understanding vulnerabilities with relatively local influence and brain region specificity.
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Affiliation(s)
- Daniel T Gray
- Division of Neural System, Memory and Aging, University of Arizona, Tucson, Arizona 85724
- Evelyn F. McKnight Brain Institute, University of Arizona, Tucson, Arizona 85724
| | - Nicole M De La Peña
- Division of Neural System, Memory and Aging, University of Arizona, Tucson, Arizona 85724
- Evelyn F. McKnight Brain Institute, University of Arizona, Tucson, Arizona 85724
| | - Lavanya Umapathy
- Electrical and Computer Engineering, University of Arizona, Tucson, Arizona 85724
| | - Sara N Burke
- Evelyn F. McKnight Brain Institute, University of Florida, Gainesville, Florida 32609
| | - James R Engle
- Division of Neural System, Memory and Aging, University of Arizona, Tucson, Arizona 85724
- Evelyn F. McKnight Brain Institute, University of Arizona, Tucson, Arizona 85724
| | - Theodore P Trouard
- Evelyn F. McKnight Brain Institute, University of Arizona, Tucson, Arizona 85724
- Department of Biomedical Engineering, University of Arizona, Tucson, Arizona 85724
| | - Carol A Barnes
- Division of Neural System, Memory and Aging, University of Arizona, Tucson, Arizona 85724
- Evelyn F. McKnight Brain Institute, University of Arizona, Tucson, Arizona 85724
- Departments of Psychology, Neurology and Neuroscience, University of Arizona, Tucson, Arizona 85724
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Köhncke Y, Düzel S, Sander MC, Lindenberger U, Kühn S, Brandmaier AM. Hippocampal and Parahippocampal Gray Matter Structural Integrity Assessed by Multimodal Imaging Is Associated with Episodic Memory in Old Age. Cereb Cortex 2020; 31:1464-1477. [PMID: 33150357 PMCID: PMC7869080 DOI: 10.1093/cercor/bhaa287] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Revised: 07/29/2020] [Accepted: 08/30/2020] [Indexed: 02/07/2023] Open
Abstract
Maintained structural integrity of hippocampal and cortical gray matter may explain why some older adults show rather preserved episodic memory. However, viable measurement models for estimating individual differences in gray matter structural integrity are lacking; instead, findings rely on fallible single indicators of integrity. Here, we introduce multitrait–multimethod methodology to capture individual differences in gray matter integrity, based on multimodal structural imaging in a large sample of 1522 healthy adults aged 60–88 years from the Berlin Aging Study II, including 333 participants who underwent magnetic resonance imaging. Structural integrity factors expressed the common variance of voxel-based morphometry, mean diffusivity, and magnetization transfer ratio for each of four regions of interest: hippocampus, parahippocampal gyrus, prefrontal cortex, and precuneus. Except for precuneus, the integrity factors correlated with episodic memory. Associations with hippocampal and parahippocampal integrity persisted after controlling for age, sex, and education. Our results support the proposition that episodic memory ability in old age benefits from maintained structural integrity of hippocampus and parahippocampal gyrus. Exploratory follow-up analyses on sex differences showed that this effect is restricted to men. Multimodal factors of structural brain integrity might help to improve our biological understanding of human memory aging.
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Affiliation(s)
- Ylva Köhncke
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Lentzeallee 94, 14195 Berlin, Germany
| | - Sandra Düzel
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Lentzeallee 94, 14195 Berlin, Germany
| | - Myriam C Sander
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Lentzeallee 94, 14195 Berlin, Germany
| | - Ulman Lindenberger
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Lentzeallee 94, 14195 Berlin, Germany.,Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Max Planck Institute for Human Development, Lentzeallee 94, 14195 Berlin, Germany
| | - Simone Kühn
- Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246 Hamburg, Germany.,Lise Meitner Group for Environmental Neuroscience, Max Planck Institute for Human Development, Lentzeallee 94, 14195 Berlin, Germany
| | - Andreas M Brandmaier
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Lentzeallee 94, 14195 Berlin, Germany.,Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Max Planck Institute for Human Development, Lentzeallee 94, 14195 Berlin, Germany
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Bellantuono L, Marzano L, La Rocca M, Duncan D, Lombardi A, Maggipinto T, Monaco A, Tangaro S, Amoroso N, Bellotti R. Predicting brain age with complex networks: From adolescence to adulthood. Neuroimage 2020; 225:117458. [PMID: 33099008 DOI: 10.1016/j.neuroimage.2020.117458] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Accepted: 10/13/2020] [Indexed: 01/21/2023] Open
Abstract
In recent years, several studies have demonstrated that machine learning and deep learning systems can be very useful to accurately predict brain age. In this work, we propose a novel approach based on complex networks using 1016 T1-weighted MRI brain scans (in the age range 7-64years). We introduce a structural connectivity model of the human brain: MRI scans are divided in rectangular boxes and Pearson's correlation is measured among them in order to obtain a complex network model. Brain connectivity is then characterized through few and easy-to-interpret centrality measures; finally, brain age is predicted by feeding a compact deep neural network. The proposed approach is accurate, robust and computationally efficient, despite the large and heterogeneous dataset used. Age prediction accuracy, in terms of correlation between predicted and actual age r=0.89and Mean Absolute Error MAE =2.19years, compares favorably with results from state-of-the-art approaches. On an independent test set including 262 subjects, whose scans were acquired with different scanners and protocols we found MAE =2.52. The only imaging analysis steps required in the proposed framework are brain extraction and linear registration, hence robust results are obtained with a low computational cost. In addition, the network model provides a novel insight on aging patterns within the brain and specific information about anatomical districts displaying relevant changes with aging.
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Affiliation(s)
- Loredana Bellantuono
- Dipartimento Interateneo di Fisica, Universitá degli Studi di Bari Aldo Moro, Bari, Italy
| | - Luca Marzano
- Dipartimento Interateneo di Fisica, Universitá degli Studi di Bari Aldo Moro, Bari, Italy
| | - Marianna La Rocca
- University of Southern California, Laboratory of NeuroImaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Los Angeles, CA, United States
| | - Dominique Duncan
- University of Southern California, Laboratory of NeuroImaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Los Angeles, CA, United States
| | - Angela Lombardi
- Istituto Nazionale di Fisica Nucleare, Sez. di Bari, Bari, Italy
| | - Tommaso Maggipinto
- Dipartimento Interateneo di Fisica, Universitá degli Studi di Bari Aldo Moro, Bari, Italy
| | - Alfonso Monaco
- Istituto Nazionale di Fisica Nucleare, Sez. di Bari, Bari, Italy.
| | - Sabina Tangaro
- Istituto Nazionale di Fisica Nucleare, Sez. di Bari, Bari, Italy; Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Universitá degli Studi di Bari Aldo Moro, Bari, Italy
| | - Nicola Amoroso
- Dipartimento di Farmacia - Scienze del Farmaco, Universitá degli Studi di Bari Aldo Moro, Bari, Italy; Istituto Nazionale di Fisica Nucleare, Sez. di Bari, Bari, Italy
| | - Roberto Bellotti
- Istituto Nazionale di Fisica Nucleare, Sez. di Bari, Bari, Italy; Dipartimento Interateneo di Fisica, Universitá degli Studi di Bari Aldo Moro, Bari, Italy
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Neuroanatomical changes associated with age-related hearing loss and listening effort. Brain Struct Funct 2020; 225:2689-2700. [PMID: 32960318 PMCID: PMC7674350 DOI: 10.1007/s00429-020-02148-w] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Accepted: 09/09/2020] [Indexed: 02/03/2023]
Abstract
Age-related hearing loss is associated with a decrease in hearing abilities for high frequencies and therefore leads to impairments in understanding speech—in particular, under adverse listening conditions. Growing evidence suggests that age-related hearing loss is related to various neural changes, for instance, affecting auditory and frontal brain regions. How the decreased auditory input and the increased listening effort in daily life are associated with structural changes is less clear, since previous evidence is scarce and mostly involved low sample sizes. Hence, the aim of the current study was to investigate the impact of age-related untreated hearing loss and subjectively rated daily life listening effort on grey matter and white matter changes in a large sample of participants (n = 71). For that aim, we conducted anatomical MRI and diffusion tensor imaging (DTI) in elderly hard-of-hearing and age-matched normal-hearing participants. Our results showed significantly lower grey matter volume in the middle frontal cortex in hard-of-hearing compared to normal-hearing participants. Further, higher listening effort was associated with lower grey matter volume and cortical thickness in the orbitofrontal cortex and lower grey matter volume in the inferior frontal cortex. No significant relations between hearing abilities or listening effort were obtained for white matter integrity in tracts connecting auditory and prefrontal as well as visual areas. These findings provide evidence that hearing impairment as well as daily life listening effort seems to be associated with grey matter loss in prefrontal brain regions. We further conclude that alterations in cortical thickness seem to be linked to the increased listening effort rather than the hearing loss itself.
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38
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Jackson TB, Maldonado T, Eakin SM, Orr JM, Bernard JA. Cerebellar and prefrontal-cortical engagement during higher-order rule learning in older adulthood. Neuropsychologia 2020; 148:107620. [PMID: 32920030 DOI: 10.1016/j.neuropsychologia.2020.107620] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2020] [Revised: 07/31/2020] [Accepted: 09/04/2020] [Indexed: 11/30/2022]
Abstract
To date most aging research has focused on cortical systems and networks, ignoring the cerebellum which has been implicated in both cognitive and motor function. Critically, older adults (OA) show marked differences in cerebellar volume and functional networks, suggesting it may play a key role in the behavioral differences observed in advanced age. OA may be less able to recruit cerebellar resources due to network and structural differences. Here, 26 young adults (YA) and 25 OA performed a second-order learning task, known to activate the cerebellum in the fMRI environment. Behavioral results indicated that YA performed significantly better and learned more quickly compared to OA. Functional imaging detailed robust parietal and cerebellar activity during learning (compared to control) blocks within each group. OA showed increased activity (relative to YA) in the left inferior parietal lobe in response to instruction cues during learning (compared to control); whereas, YA showed increased activity (relative to OA) in the left anterior cingulate to feedback cues during learning, potentially explaining age-related performance differences. Visual interpretation of effect size maps showed more bilateral posterior cerebellar activation in OA compared to YA during learning blocks, but early learning showed widespread cerebellar activation in YA compared to OA. There were qualitatively large age-related differences in cerebellar recruitment in terms of effect sizes, yet no statistical difference. These findings serve to further elucidate age-related differences and similarities in cerebellar and cortical brain function and implicate the cerebellum and its networks as regions of interest in aging research.
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Affiliation(s)
- T Bryan Jackson
- Department of Psychological and Brain Sciences, Texas A&M University, College Station, USA.
| | - Ted Maldonado
- Department of Psychological and Brain Sciences, Texas A&M University, College Station, USA
| | - Sydney M Eakin
- Department of Psychological and Brain Sciences, Texas A&M University, College Station, USA
| | - Joseph M Orr
- Department of Psychological and Brain Sciences, Texas A&M University, College Station, USA; Texas A&M Institute for Neuroscience, Texas A&M University, College Station, USA
| | - Jessica A Bernard
- Department of Psychological and Brain Sciences, Texas A&M University, College Station, USA; Texas A&M Institute for Neuroscience, Texas A&M University, College Station, USA
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Harris MA, Shen X, Cox SR, Gibson J, Adams MJ, Clarke TK, Deary IJ, Lawrie SM, McIntosh AM, Whalley HC. Stratifying major depressive disorder by polygenic risk for schizophrenia in relation to structural brain measures. Psychol Med 2020; 50:1653-1662. [PMID: 31317844 DOI: 10.1017/s003329171900165x] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
BACKGROUND Substantial clinical heterogeneity of major depressive disorder (MDD) suggests it may group together individuals with diverse aetiologies. Identifying distinct subtypes should lead to more effective diagnosis and treatment, while providing more useful targets for further research. Genetic and clinical overlap between MDD and schizophrenia (SCZ) suggests an MDD subtype may share underlying mechanisms with SCZ. METHODS The present study investigated whether a neurobiologically distinct subtype of MDD could be identified by SCZ polygenic risk score (PRS). We explored interactive effects between SCZ PRS and MDD case/control status on a range of cortical, subcortical and white matter metrics among 2370 male and 2574 female UK Biobank participants. RESULTS There was a significant SCZ PRS by MDD interaction for rostral anterior cingulate cortex (RACC) thickness (β = 0.191, q = 0.043). This was driven by a positive association between SCZ PRS and RACC thickness among MDD cases (β = 0.098, p = 0.026), compared to a negative association among controls (β = -0.087, p = 0.002). MDD cases with low SCZ PRS showed thinner RACC, although the opposite difference for high-SCZ-PRS cases was not significant. There were nominal interactions for other brain metrics, but none remained significant after correcting for multiple comparisons. CONCLUSIONS Our significant results indicate that MDD case-control differences in RACC thickness vary as a function of SCZ PRS. Although this was not the case for most other brain measures assessed, our specific findings still provide some further evidence that MDD in the presence of high genetic risk for SCZ is subtly neurobiologically distinct from MDD in general.
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Affiliation(s)
- Mathew A Harris
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK
| | - Xueyi Shen
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK
| | - Simon R Cox
- Department of Psychology, University of Edinburgh, Edinburgh, UK
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
| | - Jude Gibson
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK
| | - Mark J Adams
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK
| | - Toni-Kim Clarke
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK
| | - Ian J Deary
- Department of Psychology, University of Edinburgh, Edinburgh, UK
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
| | | | - Andrew M McIntosh
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
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Abstract
Abstract. MoCA is a short cognitive screening tool. We examined the relationship of MoCA performance to white matter integrity, gray matter volume, and surface-based measurements at normal aging in a study in which older and younger cognitively unaffected subjects participated. The sample was split according to MoCA performance, and the data were analyzed using a general linear model (Age × MoCA). We found effects in the expected direction for all methods. The main effects on age and performance as well as interactions occurred for regions associated with aging, pathological and nonpathological. Older low-performing subjects showed structural deficits compared to older high-performing subjects. Therefore, the global index of cognitive status reflects relevant features of the brain structure.
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Affiliation(s)
- Gebhard Sammer
- Cognitive NeuroScience at the Centre for Psychiatry, University of Gießen, Germany
- Department of Psychology, University of Gießen, Germany
- Bender Institute of Neuroimaging, University of Gießen, Germany
| | - Eva Lenz
- Cognitive NeuroScience at the Centre for Psychiatry, University of Gießen, Germany
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41
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Luo N, Sui J, Abrol A, Lin D, Chen J, Vergara VM, Fu Z, Du Y, Damaraju E, Xu Y, Turner JA, Calhoun VD. Age-related structural and functional variations in 5,967 individuals across the adult lifespan. Hum Brain Mapp 2020; 41:1725-1737. [PMID: 31876339 PMCID: PMC7267948 DOI: 10.1002/hbm.24905] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2019] [Revised: 11/24/2019] [Accepted: 12/10/2019] [Indexed: 12/13/2022] Open
Abstract
Exploring brain changes across the human lifespan is becoming an important topic in neuroscience. Though there are multiple studies which investigated the relationship between age and brain imaging, the results are heterogeneous due to small sample sizes and relatively narrow age ranges. Here, based on year-wise estimation of 5,967 subjects from 13 to 72 years old, we aimed to provide a more precise description of adult lifespan variation trajectories of gray matter volume (GMV), structural network correlation (SNC), and functional network connectivity (FNC) using independent component analysis and multivariate linear regression model. Our results revealed the following relationships: (a) GMV linearly declined with age in most regions, while parahippocampus showed an inverted U-shape quadratic relationship with age; SNC presented a U-shape quadratic relationship with age within cerebellum, and inverted U-shape relationship primarily in the default mode network (DMN) and frontoparietal (FP) related correlation. (b) FNC tended to linearly decrease within resting-state networks (RSNs), especially in the visual network and DMN. Early increase was revealed between RSNs, primarily in FP and DMN, which experienced a decrease at older ages. U-shape relationship was also revealed to compensate for the cognition deficit in attention and subcortical related connectivity at late years. (c) The link between middle occipital gyrus and insula, as well as precuneus and cerebellum, exhibited similar changing trends between SNC and FNC across the adult lifespan. Collectively, these results highlight the benefit of lifespan study and provide a precise description of age-related regional variation and SNC/FNC changes based on a large dataset.
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Affiliation(s)
- Na Luo
- Brainnetome Center and National Laboratory of Pattern RecognitionInstitute of Automation, Chinese Academy of SciencesBeijingChina
- University of Chinese Academy of SciencesBeijingChina
| | - Jing Sui
- Brainnetome Center and National Laboratory of Pattern RecognitionInstitute of Automation, Chinese Academy of SciencesBeijingChina
- University of Chinese Academy of SciencesBeijingChina
- CAS Center for Excellence in Brain Science and Intelligence TechnologyInstitute of Automation, Chinese Academy of SciencesBeijingChina
| | - Anees Abrol
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgia
| | - Dongdong Lin
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgia
| | - Jiayu Chen
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgia
| | - Victor M. Vergara
- CAS Center for Excellence in Brain Science and Intelligence TechnologyInstitute of Automation, Chinese Academy of SciencesBeijingChina
| | - Zening Fu
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgia
| | - Yuhui Du
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgia
- School of Computer and Information TechnologyShanxi UniversityTaiyuanChina
| | - Eswar Damaraju
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgia
| | - Yong Xu
- Department of PsychiatryFirst Clinical Medical College/ First Hospital of Shanxi Medical UniversityTaiyuanChina
| | - Jessica A. Turner
- Department of PsychologyNeuroscience Institute, Georgia State UniversityAtlantaGeorgia
| | - Vince D. Calhoun
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgia
- Department of PsychiatryYale University, School of MedicineNew HavenConnecticut
- Department of Psychology, Computer ScienceNeuroscience Institute, and Physics, Georgia State UniversityAtlantaGeorgia
- Department of Electrical and Computer EngineeringGeorgia Institute of TechnologyAtlantaGeorgia
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42
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Rezaee Z, Dutta A. Lobule‐Specific Dosage Considerations for Cerebellar Transcranial Direct Current Stimulation During Healthy Aging: A Computational Modeling Study Using Age‐Specific Magnetic Resonance Imaging Templates. Neuromodulation 2020; 23:341-365. [DOI: 10.1111/ner.13098] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2019] [Revised: 12/02/2019] [Accepted: 12/18/2019] [Indexed: 12/22/2022]
Affiliation(s)
- Zeynab Rezaee
- Department of Biomedical Engineering University at Buffalo Buffalo NY USA
| | - Anirban Dutta
- Department of Biomedical Engineering University at Buffalo Buffalo NY USA
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43
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Cui D, Zhang L, Zheng F, Wang H, Meng Q, Lu W, Liu Z, Yin T, Qiu J. Volumetric reduction of cerebellar lobules associated with memory decline across the adult lifespan. Quant Imaging Med Surg 2020; 10:148-159. [PMID: 31956538 DOI: 10.21037/qims.2019.10.19] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Background The human cerebellum plays an essential role in motor control, is involved in cognitive function and helps to regulate emotional responses. However, little is known about the relationship between cerebellar lobules and age-related memory decline. We aimed to investigate volume alterations in cerebellar lobules at different ages and assess their correlations with reduced memory recall abilities. Methods A sample of 275 individuals were divided into the following four groups: 20-35 years (young), 36-50 years (early-middle age), 51-65 years (late-middle age), and 66-89 years (old). Volumes of the cerebellar lobules were obtained using volBrain software. Analysis of covariance and post hoc analysis were used to analyze group differences in cerebellar lobular volumes, and multiple comparisons were performed using the Bonferroni method. Spearman correlation was used to investigate the relationship between lobular volumes and memory recall scores. Results In this study, we found that older adults had smaller cerebellar volumes than the other subjects. Volumetric decreases in size were noted in bilateral lobule VI and lobule crus I. Moreover, the volumes of bilateral lobule crus I, lobule VI, and right lobule IV were significantly associated with memory recall scores. Conclusions In the present study, we found that some lobules of the cerebellum appear more predisposed to age-related changes than other lobules. These findings provide further evidence that specific regions of the cerebellum could be used to assess the risk of memory decline across the adult lifespan.
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Affiliation(s)
- Dong Cui
- Institute of Biomedical Engineering, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300192, China.,College of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an 271016, China
| | - Li Zhang
- College of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an 271016, China.,Medical Engineering and Technology Research Center, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an 271016, China.,Imaging-X Joint Laboratory, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an 271016, China
| | - Fenglian Zheng
- College of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an 271016, China.,Medical Engineering and Technology Research Center, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an 271016, China.,Imaging-X Joint Laboratory, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an 271016, China
| | - Huiqin Wang
- Institute of Biomedical Engineering, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300192, China
| | - Qingjian Meng
- College of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an 271016, China
| | - Wen Lu
- College of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an 271016, China
| | - Zhipeng Liu
- Institute of Biomedical Engineering, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300192, China
| | - Tao Yin
- Institute of Biomedical Engineering, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300192, China
| | - Jianfeng Qiu
- College of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an 271016, China.,Medical Engineering and Technology Research Center, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an 271016, China.,Imaging-X Joint Laboratory, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an 271016, China
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44
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Lombardi A, Monaco A, Donvito G, Amoroso N, Bellotti R, Tangaro S. Brain Age Prediction With Morphological Features Using Deep Neural Networks: Results From Predictive Analytic Competition 2019. Front Psychiatry 2020; 11:619629. [PMID: 33551880 PMCID: PMC7854554 DOI: 10.3389/fpsyt.2020.619629] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/20/2020] [Accepted: 12/18/2020] [Indexed: 12/05/2022] Open
Abstract
Morphological changes in the brain over the lifespan have been successfully described by using structural magnetic resonance imaging (MRI) in conjunction with machine learning (ML) algorithms. International challenges and scientific initiatives to share open access imaging datasets also contributed significantly to the advance in brain structure characterization and brain age prediction methods. In this work, we present the results of the predictive model based on deep neural networks (DNN) proposed during the Predictive Analytic Competition 2019 for brain age prediction of 2638 healthy individuals. We used FreeSurfer software to extract some morphological descriptors from the raw MRI scans of the subjects collected from 17 sites. We compared the proposed DNN architecture with other ML algorithms commonly used in the literature (RF, SVR, Lasso). Our results highlight that the DNN models achieved the best performance with MAE = 4.6 on the hold-out test, outperforming the other ML strategies. We also propose a complete ML framework to perform a robust statistical evaluation of feature importance for the clinical interpretability of the results.
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Affiliation(s)
- Angela Lombardi
- Istituto Nazionale di Fisica Nucleare, Bari, Italy.,Dipartimento Interateneo di Fisica, Università degli Studi di Bari Aldo Moro, Bari, Italy
| | | | | | - Nicola Amoroso
- Istituto Nazionale di Fisica Nucleare, Bari, Italy.,Dipartimento di Farmacia - Scienze del Farmaco, Universitá degli Studi di Bari Aldo Moro, Bari, Italy
| | - Roberto Bellotti
- Istituto Nazionale di Fisica Nucleare, Bari, Italy.,Dipartimento Interateneo di Fisica, Università degli Studi di Bari Aldo Moro, Bari, Italy
| | - Sabina Tangaro
- Istituto Nazionale di Fisica Nucleare, Bari, Italy.,Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Università degli Studi di Bari Aldo Moro, Bari, Italy
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45
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Tullo S, Patel R, Devenyi GA, Salaciak A, Bedford SA, Farzin S, Wlodarski N, Tardif CL, Breitner JCS, Chakravarty MM. MR-based age-related effects on the striatum, globus pallidus, and thalamus in healthy individuals across the adult lifespan. Hum Brain Mapp 2019; 40:5269-5288. [PMID: 31452289 PMCID: PMC6864890 DOI: 10.1002/hbm.24771] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2019] [Revised: 07/17/2019] [Accepted: 08/05/2019] [Indexed: 01/18/2023] Open
Abstract
While numerous studies have used magnetic resonance imaging (MRI) to elucidate normative age-related trajectories in subcortical structures across the human lifespan, there exists substantial heterogeneity among different studies. Here, we investigated the normative relationships between age and morphology (i.e., volume and shape), and microstructure (using the T1-weighted/T2-weighted [T1w/T2w] signal ratio as a putative index of myelin and microstructure) of the striatum, globus pallidus, and thalamus across the adult lifespan using a dataset carefully quality controlled, yielding a final sample of 178 for the morphological analyses, and 162 for the T1w/T2w analyses from an initial dataset of 253 healthy subjects, aged 18-83. In accordance with previous cross-sectional studies of adults, we observed age-related volume decrease that followed a quadratic relationship between age and bilateral striatal and thalamic volumes, and a linear relationship in the globus pallidus. Our shape indices consistently demonstrated age-related posterior and medial areal contraction bilaterally across all three structures. Beyond morphology, we observed a quadratic inverted U-shaped relationship between T1w/T2w signal ratio and age, with a peak value occurring in middle age (at around 50 years old). After permutation testing, the Akaike information criterion determined age relationships remained significant for the bilateral globus pallidus and thalamus, for both the volumetric and T1w/T2w analyses. Our findings serve to strengthen and expand upon previous volumetric analyses by providing a normative baseline of morphology and microstructure of these structures to which future studies investigating patients with various disorders can be compared.
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Affiliation(s)
- Stephanie Tullo
- Integrated Program in NeuroscienceMcGill UniversityMontrealQuebecCanada
- Computational Brain Anatomy Laboratory, Cerebral Imaging CentreDouglas Mental Health University InstituteVerdunQuebecCanada
| | - Raihaan Patel
- Computational Brain Anatomy Laboratory, Cerebral Imaging CentreDouglas Mental Health University InstituteVerdunQuebecCanada
- Department of Biological and Biomedical EngineeringMcGill UniversityMontrealQuebecCanada
| | - Gabriel A. Devenyi
- Computational Brain Anatomy Laboratory, Cerebral Imaging CentreDouglas Mental Health University InstituteVerdunQuebecCanada
- Department of PsychiatryMcGill UniversityMontrealQuebecCanada
| | - Alyssa Salaciak
- Computational Brain Anatomy Laboratory, Cerebral Imaging CentreDouglas Mental Health University InstituteVerdunQuebecCanada
| | - Saashi A. Bedford
- Integrated Program in NeuroscienceMcGill UniversityMontrealQuebecCanada
- Computational Brain Anatomy Laboratory, Cerebral Imaging CentreDouglas Mental Health University InstituteVerdunQuebecCanada
| | - Sarah Farzin
- Computational Brain Anatomy Laboratory, Cerebral Imaging CentreDouglas Mental Health University InstituteVerdunQuebecCanada
| | - Nancy Wlodarski
- Computational Brain Anatomy Laboratory, Cerebral Imaging CentreDouglas Mental Health University InstituteVerdunQuebecCanada
| | - Christine L. Tardif
- McConnell Brain Imaging CenterMontreal Neurological Institute, McGill UniversityMontrealQuebecCanada
| | | | - John C. S. Breitner
- Centre for the Studies on the Prevention of ADDouglas Mental Health University InstituteVerdunQuebecCanada
| | - M. Mallar Chakravarty
- Integrated Program in NeuroscienceMcGill UniversityMontrealQuebecCanada
- Computational Brain Anatomy Laboratory, Cerebral Imaging CentreDouglas Mental Health University InstituteVerdunQuebecCanada
- Department of Biological and Biomedical EngineeringMcGill UniversityMontrealQuebecCanada
- Department of PsychiatryMcGill UniversityMontrealQuebecCanada
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46
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Garbarino S, Lorenzi M, Oxtoby NP, Vinke EJ, Marinescu RV, Eshaghi A, Ikram MA, Niessen WJ, Ciccarelli O, Barkhof F, Schott JM, Vernooij MW, Alexander DC. Differences in topological progression profile among neurodegenerative diseases from imaging data. eLife 2019; 8:e49298. [PMID: 31793876 PMCID: PMC6922631 DOI: 10.7554/elife.49298] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2019] [Accepted: 12/02/2019] [Indexed: 01/01/2023] Open
Abstract
The spatial distribution of atrophy in neurodegenerative diseases suggests that brain connectivity mediates disease propagation. Different descriptors of the connectivity graph potentially relate to different underlying mechanisms of propagation. Previous approaches for evaluating the influence of connectivity on neurodegeneration consider each descriptor in isolation and match predictions against late-stage atrophy patterns. We introduce the notion of a topological profile - a characteristic combination of topological descriptors that best describes the propagation of pathology in a particular disease. By drawing on recent advances in disease progression modeling, we estimate topological profiles from the full course of pathology accumulation, at both cohort and individual levels. Experimental results comparing topological profiles for Alzheimer's disease, multiple sclerosis and normal ageing show that topological profiles explain the observed data better than single descriptors. Within each condition, most individual profiles cluster around the cohort-level profile, and individuals whose profiles align more closely with other cohort-level profiles show features of that cohort. The cohort-level profiles suggest new insights into the biological mechanisms underlying pathology propagation in each disease.
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Affiliation(s)
- Sara Garbarino
- Centre for Medical Image Computing, Department of Computer ScienceUniversity College LondonLondonUnited Kingdom
- Université Côte d’Azur, Inria, Epione Research ProjectSophia AntipolisFrance
| | - Marco Lorenzi
- Université Côte d’Azur, Inria, Epione Research ProjectSophia AntipolisFrance
| | - Neil P Oxtoby
- Centre for Medical Image Computing, Department of Computer ScienceUniversity College LondonLondonUnited Kingdom
| | - Elisabeth J Vinke
- Department of EpidemiologyErasmus Medical CenterRotterdamNetherlands
| | - Razvan V Marinescu
- Centre for Medical Image Computing, Department of Computer ScienceUniversity College LondonLondonUnited Kingdom
| | - Arman Eshaghi
- Centre for Medical Image Computing, Department of Computer ScienceUniversity College LondonLondonUnited Kingdom
- Queen Square Multiple Sclerosis Centre, UCL Queen Square Institute of Neurology, Faculty of Brain SciencesUniversity College LondonLondonUnited Kingdom
| | - M Arfan Ikram
- Department of EpidemiologyErasmus Medical CenterRotterdamNetherlands
- Department of Radiology and Nuclear medicineErasmus MCRotterdamNetherlands
| | - Wiro J Niessen
- Department of Radiology and Nuclear medicineErasmus MCRotterdamNetherlands
| | - Olga Ciccarelli
- Queen Square Multiple Sclerosis Centre, UCL Queen Square Institute of Neurology, Faculty of Brain SciencesUniversity College LondonLondonUnited Kingdom
| | - Frederik Barkhof
- Centre for Medical Image Computing, Department of Computer ScienceUniversity College LondonLondonUnited Kingdom
- Department of Radiology and Nuclear medicineVUmcAmsterdamNetherlands
| | - Jonathan M Schott
- Dementia Research Centre, Institute of NeurologyUniversity College LondonLondonUnited Kingdom
| | - Meike W Vernooij
- Department of EpidemiologyErasmus Medical CenterRotterdamNetherlands
- Department of Radiology and Nuclear medicineErasmus MCRotterdamNetherlands
| | - Daniel C Alexander
- Centre for Medical Image Computing, Department of Computer ScienceUniversity College LondonLondonUnited Kingdom
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The Effects of Lutein and Zeaxanthin Supplementation on Brain Morphology in Older Adults: A Randomized, Controlled Trial. J Aging Res 2019; 2019:3709402. [PMID: 31871787 PMCID: PMC6913342 DOI: 10.1155/2019/3709402] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2019] [Accepted: 10/29/2019] [Indexed: 12/16/2022] Open
Abstract
A growing literature emphasizes the importance of lifestyle factors such as nutrition in successful aging. The current study examined if one year of supplementation with lutein (L) and zeaxanthin (Z), two nutrients with known antioxidative properties and cognitive benefits, impacted structural brain outcomes in older adults using a double-blind, randomized, placebo-controlled trial design. Community-dwelling older adults (20 males and 27 females) aged 65–87 years (M = 71.8 years, SD = 6.04 years) were randomized into supplement (N = 33) and placebo groups (N = 14) using simple randomization. The supplement group received 10 mg L + 2 mg Z daily for 12 months while the placebo group received a visually identical, inert placebo. L and Z were measured via retinal concentrations (macular pigment optical density or MPOD). Structural brain outcomes, focusing on global and frontal-temporal lobe regions, were acquired using both T1-weighted and DTI MRI sequences. We hypothesized that the supplement group would increase, maintain, or show attenuated loss in hypothesized regions-of-interest (ROIs) while the placebo group would show age-related declines in brain structural integrity over the course of the trial. While results showed age-related declines for frontal and temporal gray and white matter volumes, as well as fornix white matter microstructure across both groups, only minimal differences were found between the supplement and placebo groups. However, exploratory analyses showed that individuals who responded better to supplementation (i.e., showed greater increases in MPOD) showed less decline in global and prefrontal gray matter volume than supplement “nonresponders.” While results suggest that one year of L and Z supplementation may have limited effects on structural brain outcomes overall, there may be a subsample of individuals for whom supplementation of L and Z provides greater benefits. ClinicalTrials.gov number, NCT02023645.
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48
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Individual identification for different age groups using functional connectivity strength. Neurol Sci 2019; 41:417-426. [PMID: 31713193 DOI: 10.1007/s10072-019-04109-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2019] [Accepted: 10/15/2019] [Indexed: 10/25/2022]
Abstract
BACKGROUND AND PURPOSE Many studies demonstrate individual differences in functional network, especially those with age. Meanwhile, aging is one of the potential risk factors for Alzheimer's disease. Therefore, it is important to explore the discrepant pattern in aging population. METHODS Most existing methods mostly target ancient atlas for the extraction of the classification features and not consider the effect of global signal. We use two novel atlases for the extraction of classification features and then use the whole and intra-hemispheric functional connectivity strength (FCS) as classification parameters to classify different age groups, respectively. Meanwhile, the regression of global signal or not during the preprocessing has been considered. Next, the support vector machine-recursive feature elimination (SVM-RFE) method is applied for feature selection and the SVM method is applied for classification. In addition, the receiver operating characteristic curve and area under the curve are drawn to evaluate the robustness of classifier. Finally, the discriminative features are related to the physiological mechanism of aging. RESULTS The promising classification performance exhibits that the FCS can effectively distinguish different age groups. Moreover, the SVM-RFE method can increase the accuracy and extract the discriminative features. The classifiers constructed by the features derived from different atlas receive similar classification performance. CONCLUSION This study successfully distinguishes the young group, middle-aged group, and elderly group through FCS parameter, indicating the functional pattern of the network exists difference between three groups. Moreover, the results received by the SVM-RFE method and SVM classifier have the very good robustness and not specific to particular atlas and not affected by global signal and appropriate for the FCS of the whole brain or intra-hemisphere, which suggests that we can apply them to disease diagnosis in the future.
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Clark AL, Sorg SF, Holiday K, Bigler ED, Bangen KJ, Evangelista ND, Bondi MW, Schiehser DM, Delano-Wood L. Fatigue Is Associated With Global and Regional Thalamic Morphometry in Veterans With a History of Mild Traumatic Brain Injury. J Head Trauma Rehabil 2019; 33:382-392. [PMID: 29385016 PMCID: PMC6066453 DOI: 10.1097/htr.0000000000000377] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
OBJECTIVE Fatigue is a complex, multidimensional phenomenon that commonly occurs following traumatic brain injury (TBI). The thalamus-a structure vulnerable to both primary and secondary injuries in TBI-is thought to play a pivotal role in the manifestation of fatigue. We explored how neuroimaging markers of local and global thalamic morphometry relate to the subjective experience of fatigue post-TBI. METHODS Sixty-three Veterans with a history of mild TBI underwent structural magnetic resonance imaging and completed questionnaires related to fatigue and psychiatric symptoms. FMRIB's Software (FSL) was utilized to obtain whole brain and thalamic volume estimates, as well as to perform regional thalamic morphometry analyses. RESULTS Independent of age, sex, intracranial volume, posttraumatic stress disorder, and depressive symptoms, greater levels of self-reported fatigue were significantly associated with decreased right (P = .026) and left (P = .046) thalamic volumes. Regional morphometry analyses revealed that fatigue was significantly associated with reductions in the anterior and dorsomedial aspects of the right thalamic body (P < .05). Similar trends were observed for the left thalamic body (P < .10). CONCLUSIONS Both global and regional thalamic morphometric changes are associated with the subjective experience of fatigue in Veterans with a history of mild TBI. These findings support a theory in which disruption of thalamocorticostriatal circuitry may result in the manifestation of fatigue in individuals with a history of neurotrauma.
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Affiliation(s)
- Alexandra L. Clark
- San Diego State University/University of California, San Diego
(SDSU/UCSD) Joint Doctoral Program in Clinical Psychology
- VA San Diego Healthcare System (VASDHS)
| | - Scott F. Sorg
- VA San Diego Healthcare System (VASDHS)
- University of California San Diego, School of Medicine, Department
of Psychiatry
| | - Kelsey Holiday
- San Diego State University/University of California, San Diego
(SDSU/UCSD) Joint Doctoral Program in Clinical Psychology
- VA San Diego Healthcare System (VASDHS)
| | - Erin D. Bigler
- Department of Psychology and the Neuroscience Center, Brigham and
Young University
| | - Katherine J. Bangen
- VA San Diego Healthcare System (VASDHS)
- University of California San Diego, School of Medicine, Department
of Psychiatry
| | | | - Mark W. Bondi
- VA San Diego Healthcare System (VASDHS)
- University of California San Diego, School of Medicine, Department
of Psychiatry
| | - Dawn M. Schiehser
- VA San Diego Healthcare System (VASDHS)
- Center of Excellence for Stress and Mental Health, VASDHS
- University of California San Diego, School of Medicine, Department
of Psychiatry
| | - Lisa Delano-Wood
- VA San Diego Healthcare System (VASDHS)
- Center of Excellence for Stress and Mental Health, VASDHS
- University of California San Diego, School of Medicine, Department
of Psychiatry
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50
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Gaudreault PO, Gosselin N, Lafortune M, Deslauriers-Gauthier S, Martin N, Bouchard M, Dubé J, Lina JM, Doyon J, Carrier J. The association between white matter and sleep spindles differs in young and older individuals. Sleep 2019; 41:5025912. [PMID: 29860401 DOI: 10.1093/sleep/zsy113] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2017] [Indexed: 11/12/2022] Open
Abstract
Study Objectives Sleep is a reliable indicator of cognitive health in older individuals. Sleep spindles (SS) are non-rapid eye movement (NREM) sleep oscillations implicated in sleep-dependent learning. Their generation imply a complex activation of the thalamo-cortico-thalamic loop. Since SS require neuronal synchrony, the integrity of the white matter (WM) underlying these connections is of major importance. During aging, both SS and WM undergo important changes. The goal of this study was to investigate whether WM integrity could predict the age-related reductions in SS characteristics. Methods Thirty young and 31 older participants underwent a night of polysomnographic recording and a 3T magnetic resonance imaging acquisition including a diffusion sequence. SS were detected in NREM sleep and EEG spectral analysis was performed for the sigma frequency band. WM diffusion metrics were computed in a voxelwise design of analysis. Results Compared to young participants, older individuals showed lower SS density, amplitude, and sigma power. Diffusion metrics were correlated with SS amplitude and sigma power in tracts connecting the thalamus to the frontal cortex for the young but not for the older group, suggesting a moderation effect. Moderation analyses showed that diffusion metrics explained between 14% and 39% of SS amplitude and sigma power variance in the young participants only. Conclusion Our results indicate that WM underlying the thalamo-cortico-thalamic loop predicts SS characteristics in young individuals, but does not explain age-related changes in SS. Other neurophysiological factors could better explain the effect of age on SS characteristics.
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Affiliation(s)
- Pierre-Olivier Gaudreault
- Center for Advanced Research in Sleep Medicine, Hôpital du Sacré-Cœur de Montréal, Montreal, QC, Canada.,Department of Psychology, Université de Montréal, Montreal, QC, Canada
| | - Nadia Gosselin
- Center for Advanced Research in Sleep Medicine, Hôpital du Sacré-Cœur de Montréal, Montreal, QC, Canada.,Department of Psychology, Université de Montréal, Montreal, QC, Canada
| | - Marjolaine Lafortune
- Center for Advanced Research in Sleep Medicine, Hôpital du Sacré-Cœur de Montréal, Montreal, QC, Canada
| | - Samuel Deslauriers-Gauthier
- Center for Advanced Research in Sleep Medicine, Hôpital du Sacré-Cœur de Montréal, Montreal, QC, Canada.,Sherbrooke Connectivity Imaging Lab, Computer Science Department, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Nicolas Martin
- Center for Advanced Research in Sleep Medicine, Hôpital du Sacré-Cœur de Montréal, Montreal, QC, Canada.,Department of Psychology, Université de Montréal, Montreal, QC, Canada
| | - Maude Bouchard
- Center for Advanced Research in Sleep Medicine, Hôpital du Sacré-Cœur de Montréal, Montreal, QC, Canada.,Department of Psychology, Université de Montréal, Montreal, QC, Canada
| | - Jonathan Dubé
- Center for Advanced Research in Sleep Medicine, Hôpital du Sacré-Cœur de Montréal, Montreal, QC, Canada.,Department of Psychology, Université de Montréal, Montreal, QC, Canada
| | - Jean-Marc Lina
- Center for Advanced Research in Sleep Medicine, Hôpital du Sacré-Cœur de Montréal, Montreal, QC, Canada
| | - Julien Doyon
- Research Center, Institut Universitaire de Gériatrie de Montréal, Montreal, QC, Canada
| | - Julie Carrier
- Center for Advanced Research in Sleep Medicine, Hôpital du Sacré-Cœur de Montréal, Montreal, QC, Canada.,Department of Psychology, Université de Montréal, Montreal, QC, Canada.,Research Center, Institut Universitaire de Gériatrie de Montréal, Montreal, QC, Canada
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