1
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Kumar D, Yanagisawa M, Funato H. Sleep-dependent memory consolidation in young and aged brains. AGING BRAIN 2024; 6:100124. [PMID: 39309405 PMCID: PMC11416671 DOI: 10.1016/j.nbas.2024.100124] [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: 05/31/2024] [Revised: 08/29/2024] [Accepted: 09/02/2024] [Indexed: 09/25/2024] Open
Abstract
Young children and aged individuals are more prone to memory loss than young adults. One probable reason is insufficient sleep-dependent memory consolidation. Sleep timing and sleep-stage duration differ between children and aged individuals compared to adults. Frequent daytime napping and fragmented sleep architecture are common in children and older individuals. Moreover, sleep-dependent oscillations that play crucial roles in long-term memory storage differ among age groups. Notably, the frontal cortex, which is important for long-term memory storage undergoes major structural changes in children and aged subjects. The similarities in sleep dynamics between children and aged subjects suggest that a deficit in sleep-dependent consolidation contributes to memory loss in both age groups.
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Affiliation(s)
- Deependra Kumar
- International Institute for Integrative Sleep Medicine, University of Tsukuba, Tsukuba, Ibaraki 305-0006, Japan
| | - Masashi Yanagisawa
- International Institute for Integrative Sleep Medicine, University of Tsukuba, Tsukuba, Ibaraki 305-0006, Japan
| | - Hiromasa Funato
- International Institute for Integrative Sleep Medicine, University of Tsukuba, Tsukuba, Ibaraki 305-0006, Japan
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2
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Badal KK, Sadhu A, Raveendra BL, McCracken C, Lozano-Villada S, Shetty AC, Gillette P, Zhao Y, Stommes D, Fieber LA, Schmale MC, Mahurkar A, Hawkins RD, Puthanveettil SV. Single-neuron analysis of aging-associated changes in learning reveals impairments in transcriptional plasticity. Aging Cell 2024:e14228. [PMID: 38924663 DOI: 10.1111/acel.14228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2024] [Accepted: 05/02/2024] [Indexed: 06/28/2024] Open
Abstract
The molecular mechanisms underlying age-related declines in learning and long-term memory are still not fully understood. To address this gap, our study focused on investigating the transcriptional landscape of a singularly identified motor neuron L7 in Aplysia, which is pivotal in a specific type of nonassociative learning known as sensitization of the siphon-withdraw reflex. Employing total RNAseq analysis on a single isolated L7 motor neuron after short-term or long-term sensitization (LTS) training of Aplysia at 8, 10, and 12 months (representing mature, late mature, and senescent stages), we uncovered aberrant changes in transcriptional plasticity during the aging process. Our findings specifically highlight changes in the expression of messenger RNAs (mRNAs) that encode transcription factors, translation regulators, RNA methylation participants, and contributors to cytoskeletal rearrangements during learning and long noncoding RNAs (lncRNAs). Furthermore, our comparative gene expression analysis identified distinct transcriptional alterations in two other neurons, namely the motor neuron L11 and the giant cholinergic neuron R2, whose roles in LTS are not yet fully elucidated. Taken together, our analyses underscore cell type-specific impairments in the expression of key components related to learning and memory within the transcriptome as organisms age, shedding light on the complex molecular mechanisms driving cognitive decline during aging.
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Affiliation(s)
- Kerriann K Badal
- Department of Neuroscience, The Herbert Wertheim UF Scripps Institute for Biomedical Innovation & Technology, Jupiter, Florida, USA
- Integrated Biology Graduate Program, Florida Atlantic University, Jupiter, Florida, USA
| | - Abhishek Sadhu
- Department of Neuroscience, The Herbert Wertheim UF Scripps Institute for Biomedical Innovation & Technology, Jupiter, Florida, USA
| | - Bindu L Raveendra
- Department of Neuroscience, The Herbert Wertheim UF Scripps Institute for Biomedical Innovation & Technology, Jupiter, Florida, USA
| | - Carrie McCracken
- The Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Sebastian Lozano-Villada
- Department of Neuroscience, The Herbert Wertheim UF Scripps Institute for Biomedical Innovation & Technology, Jupiter, Florida, USA
- Harriet L. Wilkes Honors College, Florida Atlantic University, Jupiter, Florida, USA
| | - Amol C Shetty
- The Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Phillip Gillette
- National Resource for Aplysia, University of Miami Rosenstiel School of Marine, Atmospheric, and Earth Sciences, Miami, Florida, USA
| | - Yibo Zhao
- Department of Neuroscience, The Herbert Wertheim UF Scripps Institute for Biomedical Innovation & Technology, Jupiter, Florida, USA
| | - Dustin Stommes
- National Resource for Aplysia, University of Miami Rosenstiel School of Marine, Atmospheric, and Earth Sciences, Miami, Florida, USA
| | - Lynne A Fieber
- National Resource for Aplysia, University of Miami Rosenstiel School of Marine, Atmospheric, and Earth Sciences, Miami, Florida, USA
| | - Michael C Schmale
- National Resource for Aplysia, University of Miami Rosenstiel School of Marine, Atmospheric, and Earth Sciences, Miami, Florida, USA
| | - Anup Mahurkar
- The Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Robert D Hawkins
- Department of Neuroscience, Columbia University, New York, New York, USA
- New York State Psychiatric Institute, New York, New York, USA
| | - Sathyanarayanan V Puthanveettil
- Department of Neuroscience, The Herbert Wertheim UF Scripps Institute for Biomedical Innovation & Technology, Jupiter, Florida, USA
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3
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Fjell AM. Aging Brain from a Lifespan Perspective. Curr Top Behav Neurosci 2024. [PMID: 38797799 DOI: 10.1007/7854_2024_476] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Research during the last two decades has shown that the brain undergoes continuous changes throughout life, with substantial heterogeneity in age trajectories between regions. Especially, temporal and prefrontal cortices show large changes, and these correlate modestly with changes in the corresponding cognitive abilities such as episodic memory and executive function. Changes seen in normal aging overlap with changes seen in neurodegenerative conditions such as Alzheimer's disease; differences between what reflects normal aging vs. a disease-related change are often blurry. This calls for a dimensional view on cognitive decline in aging, where clear-cut distinctions between normality and pathology cannot be always drawn. Although much progress has been made in describing typical patterns of age-related changes in the brain, identifying risk and protective factors, and mapping cognitive correlates, there are still limits to our knowledge that should be addressed by future research. We need more longitudinal studies following the same participants over longer time intervals with cognitive testing and brain imaging, and an increased focus on the representativeness vs. selection bias in neuroimaging research of aging.
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Affiliation(s)
- Anders Martin Fjell
- Department of Psychology, Center for Lifespan Changes in Brain and Cognition, University of Oslo, Oslo, Norway.
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4
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Negrón-Oyarzo I, Dib T, Chacana-Véliz L, López-Quilodrán N, Urrutia-Piñones J. Large-scale coupling of prefrontal activity patterns as a mechanism for cognitive control in health and disease: evidence from rodent models. Front Neural Circuits 2024; 18:1286111. [PMID: 38638163 PMCID: PMC11024307 DOI: 10.3389/fncir.2024.1286111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Accepted: 03/11/2024] [Indexed: 04/20/2024] Open
Abstract
Cognitive control of behavior is crucial for well-being, as allows subject to adapt to changing environments in a goal-directed way. Changes in cognitive control of behavior is observed during cognitive decline in elderly and in pathological mental conditions. Therefore, the recovery of cognitive control may provide a reliable preventive and therapeutic strategy. However, its neural basis is not completely understood. Cognitive control is supported by the prefrontal cortex, structure that integrates relevant information for the appropriate organization of behavior. At neurophysiological level, it is suggested that cognitive control is supported by local and large-scale synchronization of oscillatory activity patterns and neural spiking activity between the prefrontal cortex and distributed neural networks. In this review, we focus mainly on rodent models approaching the neuronal origin of these prefrontal patterns, and the cognitive and behavioral relevance of its coordination with distributed brain systems. We also examine the relationship between cognitive control and neural activity patterns in the prefrontal cortex, and its role in normal cognitive decline and pathological mental conditions. Finally, based on these body of evidence, we propose a common mechanism that may underlie the impaired cognitive control of behavior.
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Affiliation(s)
- Ignacio Negrón-Oyarzo
- Instituto de Fisiología, Facultad de Ciencias, Universidad de Valparaíso, Valparaíso, Chile
| | - Tatiana Dib
- Instituto de Fisiología, Facultad de Ciencias, Universidad de Valparaíso, Valparaíso, Chile
| | - Lorena Chacana-Véliz
- Instituto de Fisiología, Facultad de Ciencias, Universidad de Valparaíso, Valparaíso, Chile
- Programa de Doctorado en Ciencias Mención en Neurociencia, Facultad de Ciencias, Universidad de Valparaíso, Valparaíso, Chile
| | - Nélida López-Quilodrán
- Instituto de Fisiología, Facultad de Ciencias, Universidad de Valparaíso, Valparaíso, Chile
- Programa de Doctorado en Ciencias Mención en Neurociencia, Facultad de Ciencias, Universidad de Valparaíso, Valparaíso, Chile
| | - Jocelyn Urrutia-Piñones
- Instituto de Fisiología, Facultad de Ciencias, Universidad de Valparaíso, Valparaíso, Chile
- Programa de Doctorado en Ciencias Mención en Neurociencia, Facultad de Ciencias, Universidad de Valparaíso, Valparaíso, Chile
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5
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Vande Casteele T, Laroy M, Van Cauwenberge M, Koole M, Dupont P, Sunaert S, Van den Stock J, Bouckaert F, Van Laere K, Emsell L, Vandenbulcke M. Preliminary evidence for preserved synaptic density in late-life depression. Transl Psychiatry 2024; 14:145. [PMID: 38485934 PMCID: PMC10940592 DOI: 10.1038/s41398-024-02837-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 02/11/2024] [Accepted: 02/14/2024] [Indexed: 03/18/2024] Open
Abstract
Late-life depression has been consistently associated with lower gray matter volume, the origin of which remains largely unexplained. Recent in-vivo PET findings in early-onset depression and Alzheimer's Disease suggest that synaptic deficits contribute to the pathophysiology of these disorders and may therefore contribute to lower gray matter volume in late-life depression. Here, we investigate synaptic density in vivo for the first time in late-life depression using the synaptic vesicle glycoprotein 2A receptor radioligand 11C-UCB-J. We included 24 currently depressed adults with late-life depression (73.0 ± 6.2 years, 16 female, geriatric depression scale = 19.5 ± 6.8) and 36 age- and gender-matched healthy controls (70.4 ± 6.2 years, 21 female, geriatric depression scale = 2.7 ± 2.9) that underwent simultaneous 11C-UCB-J positron emission tomography (PET) and 3D T1- and T2-FLAIR weighted magnetic resonance (MR) imaging on a 3-tesla PET-MR scanner. We used analyses of variance to test for 11C-UCB-J binding and gray matter volumes differences in regions implicated in depression. The late-life depression group showed a trend in lower gray matter volumes in the hippocampus (p = 0.04), mesial temporal (p = 0.02) and prefrontal cortex (p = 0.02) compared to healthy control group without surviving correction for multiple comparison. However, no group differences in 11C-UCB-J binding were found in these regions nor were any associations between 11C-UCB-J and depressive symptoms. Our data suggests that, in contrast to Alzheimer's Disease, lower gray matter volume in late-life depression is not associated with synaptic density changes. From a therapeutic standpoint, preserved synaptic density in late-life depression may be an encouraging finding.
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Affiliation(s)
- Thomas Vande Casteele
- KU Leuven, Leuven Brain Institute, Department of Neurosciences, Neuropsychiatry, B-3000, Leuven, Belgium.
| | - Maarten Laroy
- KU Leuven, Leuven Brain Institute, Department of Neurosciences, Neuropsychiatry, B-3000, Leuven, Belgium
| | - Margot Van Cauwenberge
- KU Leuven, Leuven Brain Institute, Department of Neurosciences, Neuropsychiatry, B-3000, Leuven, Belgium
- Neurology, University Hospitals Leuven, B-3000, Leuven, Belgium
| | - Michel Koole
- KU Leuven, Leuven Brain Institute, Department of Imaging and Pathology, Nuclear Medicine, B-3000, Leuven, Belgium
| | - Patrick Dupont
- KU Leuven, Leuven Brain Institute, Department of Neurosciences, Laboratory for Cognitive Neurology, B-3000, Leuven, Belgium
| | - Stefan Sunaert
- KU Leuven, Leuven Brain Institute, Department of Imaging and Pathology, Translational MRI, B-3000, Leuven, Belgium
- Radiology, University Hospitals Leuven, B-3000, Leuven, Belgium
| | - Jan Van den Stock
- KU Leuven, Leuven Brain Institute, Department of Neurosciences, Neuropsychiatry, B-3000, Leuven, Belgium
- Geriatric Psychiatry, University Psychiatric Center KU Leuven, B-3000, Leuven, Belgium
| | - Filip Bouckaert
- KU Leuven, Leuven Brain Institute, Department of Neurosciences, Neuropsychiatry, B-3000, Leuven, Belgium
- Geriatric Psychiatry, University Psychiatric Center KU Leuven, B-3000, Leuven, Belgium
| | - Koen Van Laere
- KU Leuven, Leuven Brain Institute, Department of Imaging and Pathology, Nuclear Medicine, B-3000, Leuven, Belgium
- Nuclear Medicine, University Hospitals Leuven, B-3000, Leuven, Belgium
| | - Louise Emsell
- KU Leuven, Leuven Brain Institute, Department of Neurosciences, Neuropsychiatry, B-3000, Leuven, Belgium
- KU Leuven, Leuven Brain Institute, Department of Imaging and Pathology, Translational MRI, B-3000, Leuven, Belgium
- Geriatric Psychiatry, University Psychiatric Center KU Leuven, B-3000, Leuven, Belgium
| | - Mathieu Vandenbulcke
- KU Leuven, Leuven Brain Institute, Department of Neurosciences, Neuropsychiatry, B-3000, Leuven, Belgium
- Geriatric Psychiatry, University Psychiatric Center KU Leuven, B-3000, Leuven, Belgium
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6
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Capogna E, Sørensen Ø, Watne LO, Roe J, Strømstad M, Idland AV, Halaas NB, Blennow K, Zetterberg H, Walhovd KB, Fjell AM, Vidal-Piñeiro D. Subtypes of brain change in aging and their associations with cognition and Alzheimer's disease biomarkers. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.04.583291. [PMID: 38496633 PMCID: PMC10942348 DOI: 10.1101/2024.03.04.583291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Structural brain changes underly cognitive changes in older age and contribute to inter-individual variability in cognition. Here, we assessed how changes in cortical thickness, surface area, and subcortical volume, are related to cognitive change in cognitively unimpaired older adults using structural magnetic resonance imaging (MRI) data-driven clustering. Specifically, we tested (1) which brain structural changes over time predict cognitive change in older age (2) whether these are associated with core cerebrospinal fluid (CSF) Alzheimer's disease (AD) biomarkers phosphorylated tau (p-tau) and amyloid-β (Aβ42), and (3) the degree of overlap between clusters derived from different structural features. In total 1899 cognitively healthy older adults (50 - 93 years) were followed up to 16 years with neuropsychological and structural MRI assessments, a subsample of which (n = 612) had CSF p-tau and Aβ42 measurements. We applied Monte-Carlo Reference-based Consensus clustering to identify subgroups of older adults based on structural brain change patterns over time. Four clusters for each brain feature were identified, representing the degree of longitudinal brain decline. Each brain feature provided a unique contribution to brain aging as clusters were largely independent across modalities. Cognitive change and baseline cognition were best predicted by cortical area change, whereas higher levels of p-tau and Aβ42 were associated with changes in subcortical volume. These results provide insights into the link between changes in brain morphology and cognition, which may translate to a better understanding of different aging trajectories.
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Affiliation(s)
- Elettra Capogna
- Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, 0373 Oslo, Norway
| | - Øystein Sørensen
- Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, 0373 Oslo, Norway
| | - Leiv Otto Watne
- Department of Geriatric Medicine, Akershus University Hospital, Lørenskog, Norway
- Institute of Clinical Medicine, Campus Ahus, University of Oslo, Oslo, Norway
| | - James Roe
- Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, 0373 Oslo, Norway
| | - Marie Strømstad
- Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, 0373 Oslo, Norway
| | - Ane Victoria Idland
- Oslo Delirium Research Group, Department of Geriatric Medicine, Oslo University Hospital, Oslo, Norway
| | - Nathalie Bodd Halaas
- Oslo Delirium Research Group, Department of Geriatric Medicine, Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, Campus UllevÅl, University of Oslo, Oslo, Norway
| | - Kaj Blennow
- Institute of Neuroscience and Physiology, the Sahlgrenska Academy at University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- Paris Brain Institute, ICM, Pitié-Salpêtrière Hospital, Sorbonne University, Paris, France
- Neurodegenerative Disorder Research Center, Division of Life Sciences and Medicine, and Department of Neurology, Institute on Aging and Brain Disorders, University of Science and Technology of China and First Affiliated Hospital of USTC, Hefei, P.R. China
| | - Henrik Zetterberg
- Institute of Neuroscience and Physiology, the Sahlgrenska Academy at University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK
- UK Dementia Research Institute at UCL, London, UK
- Hong Center for Neurodegenerative Diseases, Hong Kong, China
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA
| | - Kristine Beate Walhovd
- Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, 0373 Oslo, Norway
- Computational Radiology and Artificial Intelligence, Department of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
| | - Anders Martin Fjell
- Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, 0373 Oslo, Norway
- Computational Radiology and Artificial Intelligence, Department of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
| | - Didac Vidal-Piñeiro
- Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, 0373 Oslo, Norway
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7
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Elliott ML, Nielsen JA, Hanford LC, Hamadeh A, Hilbert T, Kober T, Dickerson BC, Hyman BT, Mair RW, Eldaief MC, Buckner RL. Precision Brain Morphometry Using Cluster Scanning. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.12.23.23300492. [PMID: 38234845 PMCID: PMC10793507 DOI: 10.1101/2023.12.23.23300492] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
Measurement error limits the statistical power to detect group differences and longitudinal change in structural MRI morphometric measures (e.g., hippocampal volume, prefrontal thickness). Recent advances in scan acceleration enable extremely fast T1-weighted scans (~1 minute) to achieve morphometric errors that are close to the errors in longer traditional scans. As acceleration allows multiple scans to be acquired in rapid succession, it becomes possible to pool estimates to increase measurement precision, a strategy known as "cluster scanning." Here we explored brain morphometry using cluster scanning in a test-retest study of 40 individuals (12 younger adults, 18 cognitively unimpaired older adults, and 10 adults diagnosed with mild cognitive impairment or Alzheimer's Dementia). Morphometric errors from a single compressed sensing (CS) 1.0mm scan with 6x acceleration (CSx6) were, on average, 12% larger than a traditional scan using the Alzheimer's Disease Neuroimaging Initiative (ADNI) protocol. Pooled estimates from four clustered CSx6 acquisitions led to errors that were 34% smaller than ADNI despite having a shorter total acquisition time. Given a fixed amount of time, a gain in measurement precision can thus be achieved by acquiring multiple rapid scans instead of a single traditional scan. Errors were further reduced when estimates were pooled from eight CSx6 scans (51% smaller than ADNI). Neither pooling across a break nor pooling across multiple scan resolutions boosted this benefit. We discuss the potential of cluster scanning to improve morphometric precision, boost statistical power, and produce more sensitive disease progression biomarkers.
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Affiliation(s)
- Maxwell L Elliott
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | - Jared A Nielsen
- Department of Psychology, Neuroscience Center, Brigham Young University, Provo, UT, 84602, USA
| | - Lindsay C Hanford
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | - Aya Hamadeh
- Baylor College of Medicine, Houston, TX 77030
| | - Tom Hilbert
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Tobias Kober
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Bradford C Dickerson
- Frontotemporal Disorders Unit
- Alzheimer's Disease Research Center
- Athinoula A. Martinos Center for Biomedical Imaging
- Department of Neurology, Massachusetts General Hospital & Harvard Medical School
- Department of Psychiatry, Massachusetts General Hospital & Harvard Medical School, Charlestown, MA 02129, USA
| | - Bradley T Hyman
- Alzheimer's Disease Research Center
- Department of Neurology, Massachusetts General Hospital & Harvard Medical School
| | - Ross W Mair
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
- Athinoula A. Martinos Center for Biomedical Imaging
| | - Mark C Eldaief
- Frontotemporal Disorders Unit
- Alzheimer's Disease Research Center
- Department of Neurology, Massachusetts General Hospital & Harvard Medical School
- Department of Psychiatry, Massachusetts General Hospital & Harvard Medical School, Charlestown, MA 02129, USA
| | - Randy L Buckner
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
- Alzheimer's Disease Research Center
- Athinoula A. Martinos Center for Biomedical Imaging
- Department of Psychiatry, Massachusetts General Hospital & Harvard Medical School, Charlestown, MA 02129, USA
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8
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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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9
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Walhovd KB, Lövden M, Fjell AM. Timing of lifespan influences on brain and cognition. Trends Cogn Sci 2023; 27:901-915. [PMID: 37563042 DOI: 10.1016/j.tics.2023.07.001] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 07/04/2023] [Accepted: 07/04/2023] [Indexed: 08/12/2023]
Abstract
Modifiable risk and protective factors for boosting brain and cognitive development and preventing neurodegeneration and cognitive decline are embraced in neuroimaging studies. We call for sobriety regarding the timing and quantity of such influences on brain and cognition. Individual differences in the level of brain and cognition, many of which present already at birth and early in development, appear stable, larger, and more pervasive than differences in change across the lifespan. Incorporating early-life factors, including genetics, and investigating both level and change will reduce the risk of ascribing undue importance and causality to proximate factors in adulthood and older age. This has implications for both mechanistic understanding and prevention.
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Affiliation(s)
- Kristine B Walhovd
- Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, Norway; Computational Radiology and Artificial Intelligence, Department of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway.
| | - Martin Lövden
- Department of Psychology, University of Gothenburg, Gothenburg, Sweden
| | - Anders M Fjell
- Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, Norway; Computational Radiology and Artificial Intelligence, Department of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
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10
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García-García I, Donica O, Cohen AA, Gonseth Nusslé S, Heini A, Nusslé S, Pichard C, Rietschel E, Tanackovic G, Folli S, Draganski B. Maintaining brain health across the lifespan. Neurosci Biobehav Rev 2023; 153:105365. [PMID: 37604360 DOI: 10.1016/j.neubiorev.2023.105365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 07/24/2023] [Accepted: 08/17/2023] [Indexed: 08/23/2023]
Abstract
Across the lifespan, the human body and brain endure the impact of a plethora of exogenous and endogenous factors that determine the health outcome in old age. The overwhelming inter-individual variance spans between progressive frailty with loss of autonomy to largely preserved physical, cognitive, and social functions. Understanding the mechanisms underlying the diverse aging trajectories can inform future strategies to maintain a healthy body and brain. Here we provide a comprehensive overview of the current literature on lifetime factors governing brain health. We present the growing body of evidence that unhealthy alimentary regime, sedentary behaviour, sleep pathologies, cardio-vascular risk factors, and chronic inflammation exert their harmful effects in a cumulative and gradual manner, and that timely and efficient intervention could promote healthy and successful aging. We discuss the main effects and interactions between these risk factors and the resulting brain health outcomes to follow with a description of current strategies aiming to eliminate, treat, or counteract the risk factors. We conclude that the detailed insights about modifiable risk factors could inform personalized multi-domain strategies for brain health maintenance on the background of increased longevity.
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Affiliation(s)
- Isabel García-García
- Laboratory for Research in Neuroimaging (LREN), Department of Clinical Neurosciences, Centre for Research in Neurosciences, Lausanne University Hospital, University of Lausanne, Switzerland; Clinique la Prairie, Montreux, Switzerland
| | | | - Armand Aaron Cohen
- Department of Geriatrics and Rehabilitation, Hadassah University Medical Center Mount Scopus, Jerusalem, Israel
| | | | | | | | - Claude Pichard
- Nutrition Unit, University Hospital of Geneva, Geneva, Switzerland
| | | | | | | | - Bogdan Draganski
- Laboratory for Research in Neuroimaging (LREN), Department of Clinical Neurosciences, Centre for Research in Neurosciences, Lausanne University Hospital, University of Lausanne, Switzerland; Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
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11
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Smith ET, Hennessee JP, Wig GS, Frank S, Gonzalez H, Bacci J, Chan M, Carreno CA, Kennedy KM, Rodrigue KM, Hertzog C, Park DC. Longitudinal changes in gray matter correspond to changes in cognition across the lifespan: implications for theories of cognition. Neurobiol Aging 2023; 129:1-14. [PMID: 37247578 PMCID: PMC10524455 DOI: 10.1016/j.neurobiolaging.2023.04.014] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Revised: 04/30/2023] [Accepted: 04/30/2023] [Indexed: 05/31/2023]
Abstract
The present study examines the association between gray matter volume and cognition. Studies that have examined this issue have focused primarily on older adults, whereas the present study examines the issue across the entire adult lifespan. A total of 463 adults, ages 20-88 at first assessment, were followed longitudinally across three assessments over 8-10years. Significant individual differences in a general cognition measure comprised of measures of speed of processing, working memory, and episodic memory were observed, as well as in measures of cortical and subcortical gray matter. Parallel process latent growth curve modeling showed a reliable relationship between decreases in cortical matter and cognitive decline across the entire adult lifespan, which persisted after controlling for age effects. Implications of these findings in relation to progression toward dementia, risk assessment, cognitive intervention, and environmental factors are discussed, as well as implications for theories of cognitive aging.
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Affiliation(s)
- Evan T Smith
- School of Behavioral and Brain Sciences, Department of Psychology, Center for Vital Longevity, University of Texas at Dallas, Dallas, TX, USA.
| | - Joseph P Hennessee
- School of Behavioral and Brain Sciences, Department of Psychology, Center for Vital Longevity, University of Texas at Dallas, Dallas, TX, USA
| | - Gagan S Wig
- School of Behavioral and Brain Sciences, Department of Psychology, Center for Vital Longevity, University of Texas at Dallas, Dallas, TX, USA; Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Sarah Frank
- School of Behavioral and Brain Sciences, Department of Psychology, Center for Vital Longevity, University of Texas at Dallas, Dallas, TX, USA
| | - Hector Gonzalez
- School of Behavioral and Brain Sciences, Department of Psychology, Center for Vital Longevity, University of Texas at Dallas, Dallas, TX, USA
| | - Julia Bacci
- School of Behavioral and Brain Sciences, Department of Psychology, Center for Vital Longevity, University of Texas at Dallas, Dallas, TX, USA
| | - Micaela Chan
- School of Behavioral and Brain Sciences, Department of Psychology, Center for Vital Longevity, University of Texas at Dallas, Dallas, TX, USA
| | - Claudia A Carreno
- School of Behavioral and Brain Sciences, Department of Psychology, Center for Vital Longevity, University of Texas at Dallas, Dallas, TX, USA
| | - Kristen M Kennedy
- School of Behavioral and Brain Sciences, Department of Psychology, Center for Vital Longevity, University of Texas at Dallas, Dallas, TX, USA
| | - Karen M Rodrigue
- School of Behavioral and Brain Sciences, Department of Psychology, Center for Vital Longevity, University of Texas at Dallas, Dallas, TX, USA
| | | | - Denise C Park
- School of Behavioral and Brain Sciences, Department of Psychology, Center for Vital Longevity, University of Texas at Dallas, Dallas, TX, USA
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12
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Castro-Fonseca E, Morais V, da Silva CG, Wollner J, Freitas J, Mello-Neto AF, Oliveira LE, de Oliveira VC, Leite REP, Alho AT, Rodriguez RD, Ferretti-Rebustini REL, Suemoto CK, Jacob-Filho W, Nitrini R, Pasqualucci CA, Grinberg LT, Tovar-Moll F, Lent R. The influence of age and sex on the absolute cell numbers of the human brain cerebral cortex. Cereb Cortex 2023; 33:8654-8666. [PMID: 37106573 PMCID: PMC10321098 DOI: 10.1093/cercor/bhad148] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 04/13/2023] [Accepted: 04/14/2023] [Indexed: 04/29/2023] Open
Abstract
The human cerebral cortex is one of the most evolved regions of the brain, responsible for most higher-order neural functions. Since nerve cells (together with synapses) are the processing units underlying cortical physiology and morphology, we studied how the human neocortex is composed regarding the number of cells as a function of sex and age. We used the isotropic fractionator for cell quantification of immunocytochemically labeled nuclei from the cerebral cortex donated by 43 cognitively healthy subjects aged 25-87 years old. In addition to previously reported sexual dimorphism in the medial temporal lobe, we found more neurons in the occipital lobe of men, higher neuronal density in women's frontal lobe, but no sex differences in the number and density of cells in the other lobes and the whole neocortex. On average, the neocortex has ~10.2 billion neurons, 34% in the frontal lobe and the remaining 66% uniformly distributed among the other 3 lobes. Along typical aging, there is a loss of non-neuronal cells in the frontal lobe and the preservation of the number of neurons in the cortex. Our study made possible to determine the different degrees of modulation that sex and age evoke on cortical cellularity.
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Affiliation(s)
- Emily Castro-Fonseca
- Institute of Biomedical Sciences, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
- D’Or Institute for Research and Education, Rio de Janeiro, Brazil
| | - Viviane Morais
- Institute of Biomedical Sciences, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Camila G da Silva
- Institute of Biomedical Sciences, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Juliana Wollner
- Institute of Biomedical Sciences, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Jaqueline Freitas
- Institute of Biomedical Sciences, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Arthur F Mello-Neto
- Institute of Biomedical Sciences, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Luiz E Oliveira
- Institute of Biomedical Sciences, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Vilson C de Oliveira
- Institute of Biomedical Sciences, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Renata E P Leite
- Biobank for Aging Studies, LIM 22, University of São Paulo Medical School, São Paulo, Brazil
- Laboratory of Medical Research in Aging (LIM-66), University of São Paulo Medical School, São Paulo, Brazil
| | - Ana T Alho
- Biobank for Aging Studies, LIM 22, University of São Paulo Medical School, São Paulo, Brazil
| | - Roberta D Rodriguez
- Biobank for Aging Studies, LIM 22, University of São Paulo Medical School, São Paulo, Brazil
- Department of Pathology, University of São Paulo Medical School, São Paulo, Brazil
| | - Renata E L Ferretti-Rebustini
- Biobank for Aging Studies, LIM 22, University of São Paulo Medical School, São Paulo, Brazil
- Department of Medical Surgical Nursing, University of São Paulo School of Nursing, São Paulo, Brazil
| | - Claudia K Suemoto
- Biobank for Aging Studies, LIM 22, University of São Paulo Medical School, São Paulo, Brazil
- Laboratory of Medical Research in Aging (LIM-66), University of São Paulo Medical School, São Paulo, Brazil
| | - Wilson Jacob-Filho
- Biobank for Aging Studies, LIM 22, University of São Paulo Medical School, São Paulo, Brazil
- Laboratory of Medical Research in Aging (LIM-66), University of São Paulo Medical School, São Paulo, Brazil
| | - Ricardo Nitrini
- Biobank for Aging Studies, LIM 22, University of São Paulo Medical School, São Paulo, Brazil
- Department of Neurology, University of São Paulo Medical School, São Paulo, Brazil
| | - Carlos A Pasqualucci
- Biobank for Aging Studies, LIM 22, University of São Paulo Medical School, São Paulo, Brazil
- Department of Pathology, University of São Paulo Medical School, São Paulo, Brazil
| | - Lea T Grinberg
- Biobank for Aging Studies, LIM 22, University of São Paulo Medical School, São Paulo, Brazil
- Department of Pathology, University of São Paulo Medical School, São Paulo, Brazil
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, United States
| | - Fernanda Tovar-Moll
- Institute of Biomedical Sciences, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
- D’Or Institute for Research and Education, Rio de Janeiro, Brazil
| | - Roberto Lent
- Institute of Biomedical Sciences, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
- D’Or Institute for Research and Education, Rio de Janeiro, Brazil
- National Institute of Translational Neuroscience, Ministry of Science and Technology, São Paulo, Brazil
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13
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Fürtjes AE, Arathimos R, Coleman JRI, Cole JH, Cox SR, Deary IJ, de la Fuente J, Madole JW, Tucker‐Drob EM, Ritchie SJ. General dimensions of human brain morphometry inferred from genome-wide association data. Hum Brain Mapp 2023; 44:3311-3323. [PMID: 36987996 PMCID: PMC10171533 DOI: 10.1002/hbm.26283] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 02/01/2023] [Accepted: 03/08/2023] [Indexed: 03/30/2023] Open
Abstract
Understanding the neurodegenerative mechanisms underlying cognitive decline in the general population may facilitate early detection of adverse health outcomes in late life. This study investigates genetic links between brain morphometry, ageing and cognitive ability. We develop Genomic Principal Components Analysis (Genomic PCA) to model general dimensions of brain-wide morphometry at the level of their underlying genetic architecture. Genomic PCA is applied to genome-wide association data for 83 brain-wide volumes (36,778 UK Biobank participants) and we extract genomic principal components (PCs) to capture global dimensions of genetic covariance across brain regions (unlike ancestral PCs that index genetic similarity between participants). Using linkage disequilibrium score regression, we estimate genetic overlap between those general brain dimensions and cognitive ageing. The first genetic PCs underlying the morphometric organisation of 83 brain-wide regions accounted for substantial genetic variance (R2 = 40%) with the pattern of component loadings corresponding closely to those obtained from phenotypic analyses. Genetically more central regions to overall brain structure - specifically frontal and parietal volumes thought to be part of the central executive network - tended to be somewhat more susceptible towards age (r = -0.27). We demonstrate the moderate genetic overlap between the first PC underlying each of several structural brain networks and general cognitive ability (rg = 0.17-0.21), which was not specific to a particular subset of the canonical networks examined. We provide a multivariate framework integrating covariance across multiple brain regions and the genome, revealing moderate shared genetic etiology between brain-wide morphometry and cognitive ageing.
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Affiliation(s)
- Anna E. Fürtjes
- Social, Genetic and Developmental Psychiatry (SGDP) CentreInstitute of Psychiatry, Psychology & Neuroscience, King's College LondonLondonSE5 8AFUK
| | - Ryan Arathimos
- Social, Genetic and Developmental Psychiatry (SGDP) CentreInstitute of Psychiatry, Psychology & Neuroscience, King's College LondonLondonSE5 8AFUK
- National Institutes for Health Research Maudsley Biomedical Research CentreSouth London and Maudsley NHS TrustLondonSE5 8AFUK
| | - Jonathan R. I. Coleman
- Social, Genetic and Developmental Psychiatry (SGDP) CentreInstitute of Psychiatry, Psychology & Neuroscience, King's College LondonLondonSE5 8AFUK
- National Institutes for Health Research Maudsley Biomedical Research CentreSouth London and Maudsley NHS TrustLondonSE5 8AFUK
| | - James H. Cole
- Department of NeuroimageingInstitute of Psychiatry, Psychology & Neuroscience, King's College LondonLondonSE5 8AFUK
- Centre for Medical Image Computing, Department of Computer ScienceUniversity College LondonLondonWC1V 6LJUK
- Dementia Research CentreInstitute of Neurology, University College LondonLondonWC1N 3BGUK
| | - Simon R. Cox
- Department of PsychologyThe University of EdinburghEdinburghEH8 9JZUK
- Lothian Birth CohortsUniversity of EdinburghEdinburghEH8 9JZUK
| | - Ian J. Deary
- Department of PsychologyThe University of EdinburghEdinburghEH8 9JZUK
- Lothian Birth CohortsUniversity of EdinburghEdinburghEH8 9JZUK
| | - Javier de la Fuente
- Department of PsychologyUniversity of Texas at AustinAustinTexas78712‐1043USA
- Population Research Center and Center on Ageing and Population SciencesUniversity of Texas at AustinAustinTexas78712‐1043USA
| | - James W. Madole
- Department of PsychologyUniversity of Texas at AustinAustinTexas78712‐1043USA
| | - Elliot M. Tucker‐Drob
- Department of PsychologyUniversity of Texas at AustinAustinTexas78712‐1043USA
- Population Research Center and Center on Ageing and Population SciencesUniversity of Texas at AustinAustinTexas78712‐1043USA
| | - Stuart J. Ritchie
- Social, Genetic and Developmental Psychiatry (SGDP) CentreInstitute of Psychiatry, Psychology & Neuroscience, King's College LondonLondonSE5 8AFUK
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14
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Aribisala BS, Valdés Hernández MDC, Okely JA, Cox SR, Ballerini L, Dickie DA, Wiseman SJ, Riha RL, Muñoz Maniega S, Radakovic R, Taylor A, Pattie A, Corley J, Redmond P, Bastin ME, Deary I, Wardlaw JM. Sleep quality, perivascular spaces and brain health markers in ageing - A longitudinal study in the Lothian Birth Cohort 1936. Sleep Med 2023; 106:123-131. [PMID: 37005116 DOI: 10.1016/j.sleep.2023.03.016] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 03/07/2023] [Accepted: 03/11/2023] [Indexed: 03/18/2023]
Abstract
BACKGROUND Sleep is thought to play a major role in brain health and general wellbeing. However, few longitudinal studies have explored the relationship between sleep habits and imaging markers of brain health, particularly markers of brain waste clearance such as perivascular spaces (PVS), of neurodegeneration such as brain atrophy, and of vascular disease, such as white matter hyperintensities (WMH). We explore these associations using data collected over 6 years from a birth cohort of older community-dwelling adults in their 70s. METHOD We analysed brain MRI data from ages 73, 76 and 79 years, and self-reported sleep duration, sleep quality and vascular risk factors from community-dwelling participants in the Lothian Birth Cohort 1936 (LBC1936) study. We calculated sleep efficiency (at age 76), quantified PVS burden (at age 73), and WMH and brain volumes (age 73 to 79), calculated the white matter damage metric, and used structural equation modelling (SEM) to explore associations and potential causative pathways between indicators related to brain waste cleaning (i.e., sleep and PVS burden), brain and WMH volume changes during the 8th decade of life. RESULTS Lower sleep efficiency was associated with a reduction in normal-appearing white matter (NAWM) volume (β = 0.204, P = 0.009) from ages 73 to 79, but not concurrent volume (i.e. age 76). Increased daytime sleep correlated with less night-time sleep (r = -0.20, P < 0.001), and with increasing white matter damage metric (β = -0.122, P = 0.018) and faster WMH growth (β = 0.116, P = 0.026). Shorter night-time sleep duration was associated with steeper 6-year reduction of NAWM volumes (β = 0.160, P = 0.011). High burden of PVS at age 73 (volume, count, and visual scores), was associated with faster deterioration in white matter: reduction of NAWM volume (β = -0.16, P = 0.012) and increasing white matter damage metric (β = 0.37, P < 0.001) between ages 73 and 79. On SEM, centrum semiovale PVS burden mediated 5% of the associations between sleep parameters and brain changes. CONCLUSION Sleep impairments, and higher PVS burden, a marker of impaired waste clearance, were associated with faster loss of healthy white matter and increasing WMH in the 8th decade of life. A small percentage of the effect of sleep in white matter health was mediated by the burden of PVS consistent with the proposed role for sleep in brain waste clearance.
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Affiliation(s)
- Benjamin S Aribisala
- Department of Neuroimaging Sciences, University of Edinburgh, Edinburgh, UK; Lothian Birth Cohort Studies, Department of Psychology, University of Edinburgh, Edinburgh, UK; Department of Computer Science, Lagos State University, Lagos, Nigeria
| | - Maria Del C Valdés Hernández
- Department of Neuroimaging Sciences, University of Edinburgh, Edinburgh, UK; Lothian Birth Cohort Studies, Department of Psychology, University of Edinburgh, Edinburgh, UK; UK Dementia Research Institute Centre at the University of Edinburgh, UK
| | - Judith A Okely
- Lothian Birth Cohort Studies, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Simon R Cox
- Lothian Birth Cohort Studies, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Lucia Ballerini
- Department of Neuroimaging Sciences, University of Edinburgh, Edinburgh, UK; Lothian Birth Cohort Studies, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | | | - Stewart J Wiseman
- Department of Neuroimaging Sciences, University of Edinburgh, Edinburgh, UK; UK Dementia Research Institute Centre at the University of Edinburgh, UK
| | - Renata L Riha
- Lothian Birth Cohort Studies, Department of Psychology, University of Edinburgh, Edinburgh, UK; Department of Sleep Medicine, Royal Infirmary of Edinburgh, NHS Lothian, UK
| | - Susana Muñoz Maniega
- Department of Neuroimaging Sciences, University of Edinburgh, Edinburgh, UK; Lothian Birth Cohort Studies, Department of Psychology, University of Edinburgh, Edinburgh, UK; UK Dementia Research Institute Centre at the University of Edinburgh, UK
| | - Ratko Radakovic
- Lothian Birth Cohort Studies, Department of Psychology, University of Edinburgh, Edinburgh, UK; Faculty of Health and Medical Sciences, University of East Anglia, Norwich, UK; Alzheimer Scotland Dementia Research Centre, University of Edinburgh, Edinburgh, UK; Euan MacDonald Centre for MND Research, University of Edinburgh, Edinburgh, UK
| | - Adele Taylor
- Lothian Birth Cohort Studies, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Alison Pattie
- Lothian Birth Cohort Studies, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Janie Corley
- Lothian Birth Cohort Studies, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Paul Redmond
- Lothian Birth Cohort Studies, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Mark E Bastin
- Department of Neuroimaging Sciences, University of Edinburgh, Edinburgh, UK; Lothian Birth Cohort Studies, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Ian Deary
- Lothian Birth Cohort Studies, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Joanna M Wardlaw
- Department of Neuroimaging Sciences, University of Edinburgh, Edinburgh, UK; Lothian Birth Cohort Studies, Department of Psychology, University of Edinburgh, Edinburgh, UK; UK Dementia Research Institute Centre at the University of Edinburgh, UK.
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15
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Nyberg L, Andersson M, Lundquist A, Baaré WFC, Bartrés-Faz D, Bertram L, Boraxbekk CJ, Brandmaier AM, Demnitz N, Drevon CA, Duezel S, Ebmeier KP, Ghisletta P, Henson R, Jensen DEA, Kievit RA, Knights E, Kühn S, Lindenberger U, Plachti A, Pudas S, Roe JM, Madsen KS, Solé-Padullés C, Sommerer Y, Suri S, Zsoldos E, Fjell AM, Walhovd KB. Individual differences in brain aging: heterogeneity in cortico-hippocampal but not caudate atrophy rates. Cereb Cortex 2023; 33:5075-5081. [PMID: 36197324 PMCID: PMC10151879 DOI: 10.1093/cercor/bhac400] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 09/13/2022] [Accepted: 09/14/2022] [Indexed: 11/13/2022] Open
Abstract
It is well documented that some brain regions, such as association cortices, caudate, and hippocampus, are particularly prone to age-related atrophy, but it has been hypothesized that there are individual differences in atrophy profiles. Here, we document heterogeneity in regional-atrophy patterns using latent-profile analysis of 1,482 longitudinal magnetic resonance imaging observations. The results supported a 2-group solution reflecting differences in atrophy rates in cortical regions and hippocampus along with comparable caudate atrophy. The higher-atrophy group had the most marked atrophy in hippocampus and also lower episodic memory, and their normal caudate atrophy rate was accompanied by larger baseline volumes. Our findings support and refine models of heterogeneity in brain aging and suggest distinct mechanisms of atrophy in striatal versus hippocampal-cortical systems.
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Affiliation(s)
- Lars Nyberg
- Department of Radiation Sciences (Radiology), Umeå University, 901 87 Umeå, Sweden
- Department of Integrative Medical Biology, Umeå University, 901 87 Umeå, Sweden
- Umeå Center for Functional Brain Imaging, Umeå University, 901 87 Umeå, Sweden
- Center for Lifespan Changes in Brain and Cognition, University of Oslo, Department of Psychology, University of Oslo, 0373 Oslo, Norway
| | - Micael Andersson
- Department of Integrative Medical Biology, Umeå University, 901 87 Umeå, Sweden
- Umeå Center for Functional Brain Imaging, Umeå University, 901 87 Umeå, Sweden
| | - Anders Lundquist
- Umeå Center for Functional Brain Imaging, Umeå University, 901 87 Umeå, Sweden
- Department of Statistics, USBE, Umeå University, Umeå S-90187, Sweden
| | - William F C Baaré
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital - Amager and Hvidovre, 2650 Copenhagen, Denmark
| | - David Bartrés-Faz
- Department of Medicine, Faculty of Medicine and Health Sciences, Institut de Neurociències, Universitat de Barcelona, and Institut d’Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), 08036 Barcelona, Spain
| | - Lars Bertram
- Center for Lifespan Changes in Brain and Cognition, University of Oslo, Department of Psychology, University of Oslo, 0373 Oslo, Norway
- Lübeck Interdisciplinary Platform for Genome Analytics (LIGA), University of Lübeck, 23562 Lübeck, Germany
| | - Carl-Johan Boraxbekk
- Department of Radiation Sciences (Radiology), Umeå University, 901 87 Umeå, Sweden
- Umeå Center for Functional Brain Imaging, Umeå University, 901 87 Umeå, Sweden
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital - Amager and Hvidovre, 2650 Copenhagen, Denmark
- Faculty of Medical and Health Sciences, Institute for Clinical Medicine, University of Copenhagen, 2400 Copenhagen, Denmark
- Department of Neurology, Institute of Sports Medicine Copenhagen (ISMC), Copenhagen University Hospital - Bispebjerg and Frederiksberg, 2400 Copenhagen, Denmark
| | - Andreas M Brandmaier
- Center for Lifespan Psychology, Max Planck Institute for Human Development, 14195 Berlin, Germany
- MSB Medical School Berlin, 14197 Berlin, Germany
- Max Plank UCL Centre for Computational Psychiatry and Ageing Research, 14195 Berlin, Germany, and London, UK
| | - Naiara Demnitz
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital - Amager and Hvidovre, 2650 Copenhagen, Denmark
| | - Christian A Drevon
- Vitas AS, Science Park, 0349 Oslo, Norway
- Department of Nutrition, Faculty of Medicine, Institute of Basic Medical Sciences, University of Oslo, 0317 Oslo Norway
| | - Sandra Duezel
- Center for Lifespan Psychology, Max Planck Institute for Human Development, 14195 Berlin, Germany
| | - Klaus P Ebmeier
- Department of Psychiatry, University of Oxford, OX3 7JX Oxford, UK
| | - Paolo Ghisletta
- Faculty of Psychology and Educational Sciences, University of Geneva, 1204 Geneva, Switzerland
- UniDistance Suisse, 3900 Brig, Switzerland
- Swiss National Centre of Competence in Research LIVES, University of Geneva, 1204 Geneva, Switzerland
| | - Richard Henson
- Medical Research Council Cognition and Brain Sciences Unit, Department of Psychiatry, University of Cambridge, Cambridge CB2 7EF, England
| | - Daria E A Jensen
- Department of Psychiatry, University of Oxford, OX3 7JX Oxford, UK
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Human Brain Activity, University of Oxford, OX3 9DU Oxford, UK
| | - Rogier A Kievit
- Cognitive Neuroscience Department, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, 6525 GD Nijmegen, The Netherlands
| | - Ethan Knights
- Medical Research Council Cognition and Brain Sciences Unit, Department of Psychiatry, University of Cambridge, Cambridge CB2 7EF, England
| | - Simone Kühn
- Lise Meitner Group for Environmental Neuroscience, Max Planck Institute for Human Development & Clinic for Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
| | - Ulman Lindenberger
- Center for Lifespan Psychology, Max Planck Institute for Human Development, 14195 Berlin, Germany
- Max Plank UCL Centre for Computational Psychiatry and Ageing Research, 14195 Berlin, Germany, and London, UK
| | - Anna Plachti
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital - Amager and Hvidovre, 2650 Copenhagen, Denmark
| | - Sara Pudas
- Department of Integrative Medical Biology, Umeå University, 901 87 Umeå, Sweden
- Umeå Center for Functional Brain Imaging, Umeå University, 901 87 Umeå, Sweden
| | - James M Roe
- Center for Lifespan Changes in Brain and Cognition, University of Oslo, Department of Psychology, University of Oslo, 0373 Oslo, Norway
| | - Kathrine Skak Madsen
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital - Amager and Hvidovre, 2650 Copenhagen, Denmark
- Radiography, Department of Technology, University College Copenhagen, 2200 Copenhagen N, Denmark
| | - Cristina Solé-Padullés
- Department of Medicine, Faculty of Medicine and Health Sciences, Institut de Neurociències, Universitat de Barcelona, and Institut d’Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), 08036 Barcelona, Spain
| | - Yasmine Sommerer
- Lübeck Interdisciplinary Platform for Genome Analytics (LIGA), University of Lübeck, 23562 Lübeck, Germany
| | - Sana Suri
- Department of Psychiatry, University of Oxford, OX3 7JX Oxford, UK
- Cognitive Neuroscience Department, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, 6525 GD Nijmegen, The Netherlands
| | - Enikő Zsoldos
- Department of Psychiatry, University of Oxford, OX3 7JX Oxford, UK
- Cognitive Neuroscience Department, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, 6525 GD Nijmegen, The Netherlands
| | - Anders M Fjell
- Center for Lifespan Changes in Brain and Cognition, University of Oslo, Department of Psychology, University of Oslo, 0373 Oslo, Norway
- Center for Computational Radiology and Artificial Intelligence, Oslo University Hospital, 0373 Oslo, Norway
| | - Kristine B Walhovd
- Center for Lifespan Changes in Brain and Cognition, University of Oslo, Department of Psychology, University of Oslo, 0373 Oslo, Norway
- Center for Computational Radiology and Artificial Intelligence, Oslo University Hospital, 0373 Oslo, Norway
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16
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Corley J, Conte F, Harris SE, Taylor AM, Redmond P, Russ TC, Deary IJ, Cox SR. Predictors of longitudinal cognitive ageing from age 70 to 82 including APOE e4 status, early-life and lifestyle factors: the Lothian Birth Cohort 1936. Mol Psychiatry 2023; 28:1256-1271. [PMID: 36481934 PMCID: PMC10005946 DOI: 10.1038/s41380-022-01900-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Revised: 11/17/2022] [Accepted: 11/21/2022] [Indexed: 12/13/2022]
Abstract
Discovering why some people's cognitive abilities decline more than others is a key challenge for cognitive ageing research. The most effective strategy may be to address multiple risk factors from across the life-course simultaneously in relation to robust longitudinal cognitive data. We conducted a 12-year follow-up of 1091 (at age 70) men and women from the longitudinal Lothian Birth Cohort 1936 study. Comprehensive repeated cognitive measures of visuospatial ability, processing speed, memory, verbal ability, and a general cognitive factor were collected over five assessments (age 70, 73, 76, 79, and 82 years) and analysed using multivariate latent growth curve modelling. Fifteen life-course variables were used to predict variation in cognitive ability levels at age 70 and cognitive slopes from age 70 to 82. Only APOE e4 carrier status was found to be reliably informative of general- and domain-specific cognitive decline, despite there being many life-course correlates of cognitive level at age 70. APOE e4 carriers had significantly steeper slopes across all three fluid cognitive domains compared with non-carriers, especially for memory (β = -0.234, p < 0.001) and general cognitive function (β = -0.246, p < 0.001), denoting a widening gap in cognitive functioning with increasing age. Our findings suggest that when many other candidate predictors of cognitive ageing slope are entered en masse, their unique contributions account for relatively small proportions of variance, beyond variation in APOE e4 status. We conclude that APOE e4 status is important for identifying those at greater risk for accelerated cognitive ageing, even among ostensibly healthy individuals.
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Affiliation(s)
- Janie Corley
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, UK.
| | - Federica Conte
- Department of Psychology, University of Milano-Bicocca, Milan, Italy
| | - Sarah E Harris
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Adele M Taylor
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Paul Redmond
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Tom C Russ
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, UK
- Alzheimer Scotland Dementia Research Centre, University of Edinburgh, Edinburgh, UK
| | - Ian J Deary
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Simon R Cox
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, UK
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17
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Nyberg L, Andersson M, Lundquist A. Longitudinal change-change associations of cognition with cortical thickness and surface area. AGING BRAIN 2023. [DOI: 10.1016/j.nbas.2023.100070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2023] Open
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18
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Frank D, Garo-Pascual M, Velasquez PAR, Frades B, Peled N, Zhang L, Strange BA. Brain structure and episodic learning rate in cognitively healthy ageing. Neuroimage 2022; 263:119630. [PMID: 36113738 DOI: 10.1016/j.neuroimage.2022.119630] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 09/08/2022] [Accepted: 09/12/2022] [Indexed: 10/31/2022] Open
Abstract
Memory normally declines with ageing and these age-related cognitive changes are associated with changes in brain structure. Episodic memory retrieval has been widely studied during ageing, whereas learning has received less attention. Here we examined the neural correlates of episodic learning rate in ageing. Our study sample consisted of 982 cognitively healthy female and male older participants from the Vallecas Project cohort, without a clinical diagnosis of mild cognitive impairment or dementia. The learning rate across the three consecutive recall trials of the verbal memory task (Free and Cued Selective Reminding Test) recall trials was used as a predictor of grey matter (GM) using voxel-based morphometry, and WM microstructure using tract-based spatial statistics on fractional anisotropy (FA) and mean diffusivity (MD) measures. Immediate Recall improved by 1.4 items per trial on average, and this episodic learning rate was faster in women and negatively associated with age. Structurally, hippocampal and anterior thalamic GM volume correlated positively with learning rate. Learning also correlated with the integrity of WM microstructure (high FA and low MD) in an extensive network of tracts including bilateral anterior thalamic radiation, fornix, and long-range tracts. These results suggest that episodic learning rate is associated with key anatomical structures for memory functioning, motivating further exploration of the differential diagnostic properties between episodic learning rate and retrieval in ageing.
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Affiliation(s)
- Darya Frank
- Laboratory for Clinical Neuroscience, Centro de Tecnología Biomédica, CTB, Universidad Politécnica de Madrid, Madrid 28223, Spain.
| | - Marta Garo-Pascual
- Laboratory for Clinical Neuroscience, Centro de Tecnología Biomédica, CTB, Universidad Politécnica de Madrid, Madrid 28223, Spain; Alzheimer's Disease Research Unit, CIEN Foundation, Queen Sofia Foundation Alzheimer Center, Madrid 28031, Spain; PhD Program in Neuroscience, Autonoma de Madrid University, Madrid 28049, Spain.
| | - Pablo Alejandro Reyes Velasquez
- Laboratory for Clinical Neuroscience, Centro de Tecnología Biomédica, CTB, Universidad Politécnica de Madrid, Madrid 28223, Spain
| | - Belén Frades
- Alzheimer's Disease Research Unit, CIEN Foundation, Queen Sofia Foundation Alzheimer Center, Madrid 28031, Spain
| | - Noam Peled
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA; Harvard Medical School, Boston, MA 02115, USA
| | - Linda Zhang
- Alzheimer's Disease Research Unit, CIEN Foundation, Queen Sofia Foundation Alzheimer Center, Madrid 28031, Spain
| | - Bryan A Strange
- Laboratory for Clinical Neuroscience, Centro de Tecnología Biomédica, CTB, Universidad Politécnica de Madrid, Madrid 28223, Spain; Alzheimer's Disease Research Unit, CIEN Foundation, Queen Sofia Foundation Alzheimer Center, Madrid 28031, Spain.
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19
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Jiang R, Scheinost D, Zuo N, Wu J, Qi S, Liang Q, Zhi D, Luo N, Chung Y, Liu S, Xu Y, Sui J, Calhoun V. A Neuroimaging Signature of Cognitive Aging from Whole-Brain Functional Connectivity. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2022; 9:e2201621. [PMID: 35811304 PMCID: PMC9403648 DOI: 10.1002/advs.202201621] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Revised: 06/02/2022] [Indexed: 05/14/2023]
Abstract
Cognitive decline is amongst one of the most commonly reported complaints during normal aging. Despite evidence that age and cognition are linked with similar neural correlates, no previous studies have directly ascertained how these two constructs overlap in the brain in terms of neuroimaging-based prediction. Based on a long lifespan healthy cohort (CamCAN, aged 19-89 years, n = 567), it is shown that both cognitive function (domains spanning executive function, emotion processing, motor function, and memory) and human age can be reliably predicted from unique patterns of functional connectivity, with models generalizable in two external datasets (n = 533 and n = 453). Results show that cognitive decline and normal aging both manifest decrease within-network connections (especially default mode and ventral attention networks) and increase between-network connections (somatomotor network). Whereas dorsal attention network is an exception, which is highly predictive on cognitive ability but is weakly correlated with aging. Further, the positively weighted connections in predicting fluid intelligence significantly mediate its association with age. Together, these findings offer insights into why normal aging is often associated with cognitive decline in terms of brain network organization, indicating a process of neural dedifferentiation and compensational theory.
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Affiliation(s)
- Rongtao Jiang
- Department of Radiology and Biomedical ImagingYale School of MedicineNew HavenCT06520USA
| | - Dustin Scheinost
- Department of Radiology and Biomedical ImagingYale School of MedicineNew HavenCT06520USA
- Interdepartmental Neuroscience ProgramYale UniversityNew HavenCT06520USA
- Department of Statistics and Data ScienceYale UniversityNew HavenCT06520USA
- Child Study CenterYale School of MedicineNew HavenCT06510USA
| | - Nianming Zuo
- Brainnetome Center and National Laboratory of Pattern RecognitionInstitute of AutomationChinese Academy of SciencesBeijing100190P. R. China
- School of Artificial IntelligenceUniversity of Chinese Academy of SciencesBeijing100049P. R. China
| | - Jing Wu
- Department of Medical OncologyBeijing You‐An HospitalCapital Medical UniversityBeijing100069P. R. China
| | - Shile Qi
- College of Computer Science and TechnologyNanjing University of Aeronautics and AstronauticsNanjing211106P. R. China
| | - Qinghao Liang
- Department of Biomedical EngineeringYale UniversityNew HavenCT06520USA
| | - Dongmei Zhi
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijing100088P. R. China
| | - Na Luo
- Brainnetome Center and National Laboratory of Pattern RecognitionInstitute of AutomationChinese Academy of SciencesBeijing100190P. R. China
- School of Artificial IntelligenceUniversity of Chinese Academy of SciencesBeijing100049P. R. China
| | - Young‐Chul Chung
- Department of PsychiatryJeonbuk National University Medical SchoolJeonju54907Republic of Korea
- Department of PsychiatryChonbuk National University HospitalJeonju54907Republic of Korea
| | - Sha Liu
- Department of Psychiatry and MDT Center for Cognitive Impairment and Sleep DisordersFirst HospitalFirst Clinical Medical College of Shanxi Medical UniversityTaiyuan030001P. R. China
| | - Yong Xu
- Department of Psychiatry and MDT Center for Cognitive Impairment and Sleep DisordersFirst HospitalFirst Clinical Medical College of Shanxi Medical UniversityTaiyuan030001P. R. China
| | - Jing Sui
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijing100088P. R. China
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia Institute of TechnologyEmory University and Georgia State UniversityAtlantaGA30303USA
| | - Vince Calhoun
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia Institute of TechnologyEmory University and Georgia State UniversityAtlantaGA30303USA
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20
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Cox SR, Deary IJ. Brain and cognitive ageing: The present, and some predictions (…about the future). AGING BRAIN 2022; 2:100032. [PMID: 36908875 PMCID: PMC9997131 DOI: 10.1016/j.nbas.2022.100032] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Revised: 01/18/2022] [Accepted: 01/31/2022] [Indexed: 11/26/2022] Open
Abstract
Experiencing decline in one's cognitive abilities is among the most feared aspects of growing old [53]. Age-related cognitive decline carries a huge personal, societal, and financial cost both in pathological ageing (such as dementias) and also within the non-clinical majority of the population. A projected 152 million people worldwide will suffer from dementia by 2050 [3]. The early stages of cognitive decline are much more prevalent than dementia, and can still impose serious limitations of performance on everyday activities, independence, and quality of life in older age [5], [60], [80]. Cognitive decline also predicts poorer health, adherence to medical regimens, and financial decision-making, and can herald dementia, illness, and death [6], [40]. Of course, when seeking to understand why some people experience more severe cognitive ageing than others, researchers have turned to the organ of thinking for clues about the nature, possible mechanisms, and determinants that might underpin more and less successful cognitive agers. However, that organ is relatively inaccessible, a limitation partly alleviated by advances in neuroimaging. Here we discuss lessons for cognitive and brain ageing that have come from neuroimaging research (especially structural brain imaging), what neuroimaging still has left to teach us, and our views on possible ways forward in this multidisciplinary field.
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Affiliation(s)
- Simon R. Cox
- Lothian Birth Cohorts, Department of Psychology, The University of Edinburgh, UK
- Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK
| | - Ian J. Deary
- Lothian Birth Cohorts, Department of Psychology, The University of Edinburgh, UK
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21
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Sisakhti M, Shafaghi L, Batouli SAH. The Volumetric Changes of the Pineal Gland with Age: An Atlas-based Structural Analysis. Exp Aging Res 2022; 48:474-504. [DOI: 10.1080/0361073x.2022.2033593] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Affiliation(s)
- Minoo Sisakhti
- Department of Cognitive Psychology, Institute for Cognitive Sciences Studies, Tehran, Iran
| | - Lida Shafaghi
- Department of Neuroscience and Addiction Studies, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran
- Department of Computational Cognition, Humanlab Technologies, Vancouver, Canada
| | - Seyed Amir Hossein Batouli
- Department of Neuroscience and Addiction Studies, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran
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22
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Tucker-Drob EM, de la Fuente J, Köhncke Y, Brandmaier AM, Nyberg L, Lindenberger U. A strong dependency between changes in fluid and crystallized abilities in human cognitive aging. SCIENCE ADVANCES 2022; 8:eabj2422. [PMID: 35108051 PMCID: PMC8809681 DOI: 10.1126/sciadv.abj2422] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Accepted: 12/10/2021] [Indexed: 05/06/2023]
Abstract
Theories of adult cognitive development classically distinguish between fluid abilities, which require effortful processing at the time of assessment, and crystallized abilities, which require the retrieval and application of knowledge. On average, fluid abilities decline throughout adulthood, whereas crystallized abilities show gains into old age. These diverging age trends, along with marked individual differences in rates of change, have led to the proposition that individuals might compensate for fluid declines with crystallized gains. Here, using data from two large longitudinal studies, we show that rates of change are strongly correlated across fluid and crystallized abilities. Hence, individuals showing greater losses in fluid abilities tend to show smaller gains, or even losses, in crystallized abilities. This observed commonality between fluid and crystallized changes places constraints on theories of compensation and directs attention toward domain-general drivers of adult cognitive decline and maintenance.
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Affiliation(s)
- Elliot M. Tucker-Drob
- Department of Psychology, Center on Aging and Population Sciences, and Population Research Center, University of Texas at Austin, Austin, TX, USA
| | - Javier de la Fuente
- Department of Psychology, Center on Aging and Population Sciences, and Population Research Center, University of Texas at Austin, Austin, TX, USA
| | - Ylva Köhncke
- 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, Berlin, Germany and London, UK
| | - Lars Nyberg
- Departments of Radiation Sciences and Integrative Medical Biology, Umeå Center for Functional Brain Imaging (UFBI), Umeå University, Umeå, Sweden
| | - Ulman Lindenberger
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Berlin, Germany and London, UK
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23
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Johansson J, Wåhlin A, Lundquist A, Brandmaier AM, Lindenberger U, Nyberg L. Model of brain maintenance reveals specific change-change association between medial-temporal lobe integrity and episodic memory. AGING BRAIN 2022; 2:100027. [PMID: 36908884 PMCID: PMC9999442 DOI: 10.1016/j.nbas.2021.100027] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Revised: 12/21/2021] [Accepted: 12/22/2021] [Indexed: 11/15/2022] Open
Abstract
Brain maintenance has been identified as a major determinant of successful memory aging. However, the extent to which brain maintenance in support of successful memory aging is specific to memory-related brain regions or forms part of a brain-wide phenomenon is unresolved. Here, we used longitudinal brain-wide gray matter MRI volumes in 262 healthy participants aged 55 to 80 years at baseline to investigate separable dimensions of brain atrophy, and explored the links of these dimensions to different dimensions of cognitive change. We statistically adjusted for common causes of change in both brain and cognition to reveal a potentially unique signature of brain maintenance related to successful memory aging. Critically, medial temporal lobe (MTL)/hippocampal change and episodic memory change were characterized by unique, residual variance beyond general factors of change in brain and cognition, and a reliable association between these two residualized variables was established (r = 0.36, p < 0.01). The present study is the first to provide solid evidence for a specific association between changes in (MTL)/hippocampus and episodic memory in normal human aging. We conclude that hippocampus-specific brain maintenance relates to the specific preservation of episodic memory in old age, in line with the notion that brain maintenance operates at both general and domain-specific levels.
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Affiliation(s)
- Jarkko Johansson
- Department of Radiation Sciences, Diagnostic Radiology, Umeå University, S-90187 Umeå, Sweden.,Umeå Center for Functional Brain Imaging (UFBI), Umeå University, S-90187 Umeå, Sweden
| | - Anders Wåhlin
- Department of Radiation Sciences, Diagnostic Radiology, Umeå University, S-90187 Umeå, Sweden.,Umeå Center for Functional Brain Imaging (UFBI), Umeå University, S-90187 Umeå, Sweden
| | - Anders Lundquist
- Umeå Center for Functional Brain Imaging (UFBI), Umeå University, S-90187 Umeå, Sweden.,Department of Statistics, USBE, Umeå University, S-90187 Umeå, Sweden
| | - Andreas M Brandmaier
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Lentzeallee 94, D-14195 Berlin, Germany.,Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Berlin Germany and London, UK
| | - Ulman Lindenberger
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Lentzeallee 94, D-14195 Berlin, Germany.,Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Berlin Germany and London, UK
| | - Lars Nyberg
- Department of Radiation Sciences, Diagnostic Radiology, Umeå University, S-90187 Umeå, Sweden.,Umeå Center for Functional Brain Imaging (UFBI), Umeå University, S-90187 Umeå, Sweden.,Department of Integrative Medical Biology, Umeå University, S-90187 Umeå, Sweden.,Wallenberg Center for Molecular Medicine, Umeå University, Umeå, Sweden
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24
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Xing XX. Globally Aging Cortical Spontaneous Activity Revealed by Multiple Metrics and Frequency Bands Using Resting-State Functional MRI. Front Aging Neurosci 2021; 13:803436. [PMID: 35027890 PMCID: PMC8748263 DOI: 10.3389/fnagi.2021.803436] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Accepted: 12/09/2021] [Indexed: 11/24/2022] Open
Abstract
Most existing aging studies using functional MRI (fMRI) are based on cross-sectional data but misinterpreted their findings (i.e., age-related differences) as longitudinal outcomes (i.e., aging-related changes). To delineate aging-related changes the of human cerebral cortex, we employed the resting-state fMRI (rsfMRI) data from 24 healthy elders in the PREVENT-AD cohort, obtaining five longitudinal scans per subject. Cortical spontaneous activity is measured globally with three rsfMRI metrics including its amplitude, homogeneity, and homotopy at three different frequency bands (slow-5: 0.02-0.03 Hz, slow-4: 0.03-0.08 Hz, and slow-3 band: 0.08-0.22 Hz). General additive mixed models revealed a universal pattern of the aging-related changes for the global cortical spontaneous activity, indicating increases of these rsfMRI metrics during aging. This aging pattern follows specific frequency and spatial profiles where higher slow bands show more non-linear curves and the amplitude exhibits more extensive and significant aging-related changes than the connectivity. These findings provide strong evidence that cortical spontaneous activity is aging globally, inspiring its clinical utility as neuroimaging markers for neruodegeneration disorders.
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Affiliation(s)
- Xiu-Xia Xing
- Department of Applied Mathematics, College of Mathematics, Faculty of Science, Beijing University of Technology, Beijing, China
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25
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The Influence of Virus Infection on Microglia and Accelerated Brain Aging. Cells 2021; 10:cells10071836. [PMID: 34360004 PMCID: PMC8303900 DOI: 10.3390/cells10071836] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 07/15/2021] [Accepted: 07/16/2021] [Indexed: 12/12/2022] Open
Abstract
Microglia are the resident immune cells of the central nervous system contributing substantially to health and disease. There is increasing evidence that inflammatory microglia may induce or accelerate brain aging, by interfering with physiological repair and remodeling processes. Many viral infections affect the brain and interfere with microglia functions, including human immune deficiency virus, flaviviruses, SARS-CoV-2, influenza, and human herpes viruses. Especially chronic viral infections causing low-grade neuroinflammation may contribute to brain aging. This review elucidates the potential role of various neurotropic viruses in microglia-driven neurocognitive deficiencies and possibly accelerated brain aging.
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26
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Cerebral small vessel disease burden and longitudinal cognitive decline from age 73 to 82: the Lothian Birth Cohort 1936. Transl Psychiatry 2021; 11:376. [PMID: 34226517 PMCID: PMC8257729 DOI: 10.1038/s41398-021-01495-4] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 06/14/2021] [Accepted: 06/22/2021] [Indexed: 12/15/2022] Open
Abstract
Slowed processing speed is considered a hallmark feature of cognitive decline in cerebral small vessel disease (SVD); however, it is unclear whether SVD's association with slowed processing might be due to its association with overall declining general cognitive ability. We quantified the total MRI-visible SVD burden of 540 members of the Lothian Birth Cohort 1936 (age: 72.6 ± 0.7 years; 47% female). Using latent growth curve modelling, we tested associations between total SVD burden at mean age 73 and changes in general cognitive ability, processing speed, verbal memory and visuospatial ability, measured at age 73, 76, 79 and 82. Covariates included age, sex, vascular risk and childhood cognitive ability. In the fully adjusted models, greater SVD burden was associated with greater declines in general cognitive ability (standardised β: -0.201; 95% CI: [-0.36, -0.04]; pFDR = 0.022) and processing speed (-0.222; [-0.40, -0.04]; pFDR = 0.022). SVD burden accounted for between 4 and 5% of variance in declines of general cognitive ability and processing speed. After accounting for the covariance between tests of processing speed and general cognitive ability, only SVD's association with greater decline in general cognitive ability remained significant, prior to FDR correction (-0.222; [-0.39, -0.06]; p = 0.008; pFDR = 0.085). Our findings do not support the notion that SVD has a specific association with declining processing speed, independent of decline in general cognitive ability (which captures the variance shared across domains of cognitive ability). The association between SVD burden and declining general cognitive ability supports the notion of SVD as a diffuse, whole-brain disease and suggests that trials monitoring SVD-related cognitive changes should consider domain-specific changes in the context of overall, general cognitive decline.
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27
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d'Arbeloff T, Elliott ML, Knodt AR, Sison M, Melzer TR, Ireland D, Ramrakha S, Poulton R, Caspi A, Moffitt TE, Hariri AR. Midlife Cardiovascular Fitness Is Reflected in the Brain's White Matter. Front Aging Neurosci 2021; 13:652575. [PMID: 33889085 PMCID: PMC8055854 DOI: 10.3389/fnagi.2021.652575] [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] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Accepted: 03/05/2021] [Indexed: 12/18/2022] Open
Abstract
Disappointing results from clinical trials designed to delay structural brain decline and the accompanying increase in risk for dementia in older adults have precipitated a shift in testing promising interventions from late in life toward midlife before irreversible damage has accumulated. This shift, however, requires targeting midlife biomarkers that are associated with clinical changes manifesting only in late life. Here we explored possible links between one putative biomarker, distributed integrity of brain white matter, and two intervention targets, cardiovascular fitness and healthy lifestyle behaviors, in midlife. At age 45, fractional anisotropy (FA) derived from diffusion weighted MRI was used to estimate the microstructural integrity of distributed white matter tracts in a population-representative birth cohort. Age-45 cardiovascular fitness (VO2Max; N = 801) was estimated from heart rates obtained during submaximal exercise tests; age-45 healthy lifestyle behaviors were estimated using the Nyberg Health Index (N = 854). Ten-fold cross-validated elastic net predictive modeling revealed that estimated VO2Max was modestly associated with distributed FA. In contrast, there was no significant association between Nyberg Health Index scores and FA. Our findings suggest that cardiovascular fitness levels, but not healthy lifestyle behaviors, are associated with the distributed integrity of white matter in the brain in midlife. These patterns could help inform future clinical intervention research targeting ADRDs.
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Affiliation(s)
- Tracy d'Arbeloff
- Department of Psychology & Neuroscience, Duke University, Durham, NC, United States
| | - Maxwell L Elliott
- Department of Psychology & Neuroscience, Duke University, Durham, NC, United States
| | - Annchen R Knodt
- Department of Psychology & Neuroscience, Duke University, Durham, NC, United States
| | - Maria Sison
- Department of Psychology & Neuroscience, Duke University, Durham, NC, United States
| | - Tracy R Melzer
- New Zealand Brain Research Institute, Christchurch, New Zealand.,Department of Medicine, University of Otago, Christchurch, New Zealand
| | - David Ireland
- Dunedin Multidisciplinary Health and Development Research Unit, Department of Psychology, University of Otago, Dunedin, New Zealand
| | - Sandhya Ramrakha
- Dunedin Multidisciplinary Health and Development Research Unit, Department of Psychology, University of Otago, Dunedin, New Zealand
| | - Richie Poulton
- Dunedin Multidisciplinary Health and Development Research Unit, Department of Psychology, University of Otago, Dunedin, New Zealand
| | - Avshalom Caspi
- Department of Psychology & Neuroscience, Duke University, Durham, NC, United States.,Social, Genetic, & Developmental Psychiatry Research Centre, Institute of Psychiatry, Psychology, & Neuroscience, King's College London, London, United Kingdom.,Department of Psychiatry & Behavioral Sciences, Duke University School of Medicine, Durham, NC, United States.,Center for Genomic and Computational Biology, Duke University, Durham, NC, United States
| | - Terrie E Moffitt
- Department of Psychology & Neuroscience, Duke University, Durham, NC, United States.,Social, Genetic, & Developmental Psychiatry Research Centre, Institute of Psychiatry, Psychology, & Neuroscience, King's College London, London, United Kingdom.,Department of Psychiatry & Behavioral Sciences, Duke University School of Medicine, Durham, NC, United States.,Center for Genomic and Computational Biology, Duke University, Durham, NC, United States
| | - Ahmad R Hariri
- Department of Psychology & Neuroscience, Duke University, Durham, NC, United States
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28
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Hwang H, Cho G, Jin MJ, Ryoo JH, Choi Y, Lee SH. A knowledge-based multivariate statistical method for examining gene-brain-behavioral/cognitive relationships: Imaging genetics generalized structured component analysis. PLoS One 2021; 16:e0247592. [PMID: 33690643 PMCID: PMC7946325 DOI: 10.1371/journal.pone.0247592] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Accepted: 02/10/2021] [Indexed: 12/30/2022] Open
Abstract
With advances in neuroimaging and genetics, imaging genetics is a naturally emerging field that combines genetic and neuroimaging data with behavioral or cognitive outcomes to examine genetic influence on altered brain functions associated with behavioral or cognitive variation. We propose a statistical approach, termed imaging genetics generalized structured component analysis (IG-GSCA), which allows researchers to investigate such gene-brain-behavior/cognitive associations, taking into account well-documented biological characteristics (e.g., genetic pathways, gene-environment interactions, etc.) and methodological complexities (e.g., multicollinearity) in imaging genetic studies. We begin by describing the conceptual and technical underpinnings of IG-GSCA. We then apply the approach for investigating how nine depression-related genes and their interactions with an environmental variable (experience of potentially traumatic events) influence the thickness variations of 53 brain regions, which in turn affect depression severity in a sample of Korean participants. Our analysis shows that a dopamine receptor gene and an interaction between a serotonin transporter gene and the environment variable have statistically significant effects on a few brain regions' variations that have statistically significant negative impacts on depression severity. These relationships are largely supported by previous studies. We also conduct a simulation study to safeguard whether IG-GSCA can recover parameters as expected in a similar situation.
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Affiliation(s)
- Heungsun Hwang
- Department of Psychology, McGill University, Montreal, Quebec, Canada
| | - Gyeongcheol Cho
- Department of Psychology, McGill University, Montreal, Quebec, Canada
| | - Min Jin Jin
- Institute of Liberal Education, Kongju National University, Gongju, Korea
| | - Ji Hoon Ryoo
- Department of Education, Yonsei University, Seoul, Korea
| | - Younyoung Choi
- Department of Counseling Psychology, Hanyang Cyber University, Seoul, Korea
| | - Seung Hwan Lee
- Department of Psychiatry, Inje University Ilsan-Paik Hospital and Inje University, Goyang, Korea
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van Kesteren EJ, Kievit RA. Exploratory factor analysis with structured residuals for brain network data. Netw Neurosci 2021; 5:1-27. [PMID: 33688604 PMCID: PMC7935039 DOI: 10.1162/netn_a_00162] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Accepted: 07/28/2020] [Indexed: 11/05/2022] Open
Abstract
Dimension reduction is widely used and often necessary to make network analyses and their interpretation tractable by reducing high-dimensional data to a small number of underlying variables. Techniques such as exploratory factor analysis (EFA) are used by neuroscientists to reduce measurements from a large number of brain regions to a tractable number of factors. However, dimension reduction often ignores relevant a priori knowledge about the structure of the data. For example, it is well established that the brain is highly symmetric. In this paper, we (a) show the adverse consequences of ignoring a priori structure in factor analysis, (b) propose a technique to accommodate structure in EFA by using structured residuals (EFAST), and (c) apply this technique to three large and varied brain-imaging network datasets, demonstrating the superior fit and interpretability of our approach. We provide an R software package to enable researchers to apply EFAST to other suitable datasets.
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Affiliation(s)
- Erik-Jan van Kesteren
- Utrecht University, Department of Methodology and Statistics, Utrecht, the Netherlands
| | - Rogier A. Kievit
- University of Cambridge, MRC Cognition and Brain Sciences Unit, Cambridge, UK
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