Article

# Consistent neuroanatomical age-related volume differences across multiple samples. Neurobiol Aging

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Center for the Study of Human Cognition, Department of Psychology, University of Oslo, Norway.
(Impact Factor: 5.01). 07/2009; 32(5):916-32. DOI: 10.1016/j.neurobiolaging.2009.05.013
Source: PubMed

ABSTRACT

Magnetic resonance imaging (MRI) is the principal method for studying structural age-related brain changes in vivo. However, previous research has yielded inconsistent results, precluding understanding of structural changes of the aging brain. This inconsistency is due to methodological differences and/or different aging patterns across samples. To overcome these problems, we tested age effects on 17 different neuroanatomical structures and total brain volume across five samples, of which one was split to further investigate consistency (883 participants). Widespread age-related volume differences were seen consistently across samples. In four of the five samples, all structures, except the brainstem, showed age-related volume differences. The strongest and most consistent effects were found for cerebral cortex, pallidum, putamen and accumbens volume. Total brain volume, cerebral white matter, caudate, hippocampus and the ventricles consistently showed non-linear age functions. Healthy aging appears associated with more widespread and consistent age-related neuroanatomical volume differences than previously believed.

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Available from: Naftali Raz, Oct 06, 2015
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• "Indeed, the cross-sectional approach is easier to implement, but it suffers from major flaws including potential cohort bias and the influence of elder individuals at the presymptomatic stage of a neurodegenerative disorder which may cause an overestimation of the age effect (Burgmans et al., 2009). The considered age range is also a source of heterogeneity between studies (Walhovd et al., 2011) as some studies evaluate the effect of age over the entire adult lifespan (Mueller et al., 2007, 2009; Che´telat et al., 2008; La Joie et al., 2010; Ziegler et al., 2011; Raz et al., 2014; Pereira et al., 2014; de Flores et al., 2015), while others only included elderly people (Wang et al., 2003, 2006; Frisoni et al., 2008; Apostolova et al., 2012; Khan et al., 2014; Wisse et al., 2014b). This point is particularly important as the effect of age on brain structures is known to be non-linear, with a strong regional specificity in the dynamic of the effects (Sowell et al., 2003; Fjell et al., 2014a,b). "
##### Article: Structural imaging of hippocampal subfields in healthy aging and Alzheimer's disease
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ABSTRACT: Hippocampal atrophy, as evidenced using magnetic resonance imaging (MRI), is one of the most validated, easily accessible and widely used biomarkers of Alzheimer's disease (AD). However, its imperfect sensitivity and specificity have highlighted the need to improve the analysis of MRI data. Based on neuropathological data showing a differential vulnerability of hippocampal subfields to AD processes, neuroimaging researchers have tried to capture corresponding morphological changes within the hippocampus. The present review provides an overview of the methodological developments that allow the assessment of hippocampal subfield morphology in vivo, and summarizes the results of studies looking at the effects of AD and normal aging on these structures. Most studies highlighted a focal atrophy of the CA1 subfield in the early (predementia or even preclinical) stages of AD, before atrophy becomes more widespread at the dementia stage, consistent with the pathological literature. Preliminary studies have indicated that looking at this focal atrophy pattern rather than standard whole hippocampus volumetry improves diagnostic accuracy at the Mild Cognitive Impairment (MCI) stage. However, controversies remain regarding changes in hippocampal subfield structure in normal aging and regarding correlations between specific subfield volume and memory abilities, very likely because of the strong methodological variability between studies. Overall, hippocampal subfield analysis has proven to be a promising technique in the study of AD. However, harmonization of segmentation protocols and studies on larger samples are needed to enable accurate comparisons between studies and to confirm the clinical utility of these techniques. Copyright © 2015. Published by Elsevier Ltd.
Neuroscience 08/2015; DOI:10.1016/j.neuroscience.2015.08.033 · 3.36 Impact Factor
• "Heterochronicity in growth and aging trajectories of regional brain volumes has been firmly established with quantitative neuroimaging (e.g., Abe et al., 2008; Giedd et al., 2010; Jernigan et al., 2001; Pfefferbaum et al., 1994; Raz and Rodrigue, 2006; Walhovd et al., 2011). Age-related effects across the adult span have shown areas especially vulnerable to aging, including prefrontal cortex and cerebellar hemispheres and those relatively resistant to aging, including motor, sensory, occipital cortices, corpus callosum, and ventral pons (e.g.,Good et al., 2001; Jernigan et al., 2001; Pfefferbaum et al., 2013; Raz and Rodrigue, 2006; Raz et al., 2005; Walhovd et al., 2011). These observations have been based largely on " crosssectional " studies, that is, data from healthy individuals of different ages examined once each, with the assumption that resulting age regressions reflect longitudinal change. "
##### Article: Cross-sectional vs. longitudinal estimates of age-related changes in the adult brain: Overlaps and discrepancies
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ABSTRACT: The healthy adult brain undergoes tissue volume decline with age, but contradictory findings abound regarding rate of change. To identify a source of this discrepancy, we present contrasting statistical approaches to estimate hippocampal volume change with age based on 200 longitudinally-acquired magnetic resonance imaging in 70 healthy adults, age 20-70 years, who had 2-5 magnetic resonance imaging collected over 6 months to 8 years. Linear mixed-effects modeling using volume trajectories over age for each subject revealed significantly negative slopes with aging after a linear decline with a suggestion of acceleration in older individuals. By contrast, general linear modeling using either the first observation only of each subject or all observations treated independently (thereby disregarding trajectories) indicated no significant correlation between volume and age. Entering a quadratic term into the linear model yielded a biologically plausible function that was not supported by longitudinal analysis. The results underscore the importance of analyses that incorporate the trajectory of individuals in the study of brain aging. Copyright © 2015 Elsevier Inc. All rights reserved.
Neurobiology of Aging 05/2015; 36(9). DOI:10.1016/j.neurobiolaging.2015.05.005 · 5.01 Impact Factor
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• "× ↓ ↓ ↓ ↓ [Walhovd et al., 2011] × ↓ [Well et al., 2007] × ↓ ↓ ↓ [Scahill et al., 2003] × ↓ [Svennerholm et al., 1997] × – [Johnson et al., 1984] × ↑ [Ho et al., 1980] × ↓ [Courchesne et al., 2000] × ↓ – [Kaye et al., 1997] × × ↓ ↓ ↓ ↓ ↓ ↓ ↓ [Sawabe et al., 2006] Mice B6C3F1 × ↑ ↑ ↑ ↑ ↑ ↑ – – [Marino, 2012] B6C3F1 × ↑ [Cameron et al., 1985] HLG × ↑ ↑ ↑ [Bergmann et al., 1995] FVB/NJ × ↑ ↑ ↑ ↑ [Carroll et al., 2011] White Swiss × ↑ ↑ ↑ ↑ ↑ [Webster et Liljegren, 1955] C57BL/Icrf a t × ↑ ↑ ↑ ↑ – ↑ ↓ ↓ [Rowlatt et al., 1976] C57BL/6J × ↑ [Cao et al., 2003; Fahlstrom et al., 2011; Fahlstrom et al., 2012; Glatt et al., 2007; Halloran et al., 2002] C57BL/6J × ↑ [Armstrong, 1990] C57BL/6J × – ↑ ↑ ↑ [Leto et al., 1971] C57BL/6, DBA/2 × ↑↓ [Ferguson et al., 2007] C57BL/6 × ↑ [Mirich et al., 2002] "
##### Article: Characterization of age-associated changes in peripheral organ and brain region weights in C57BL/6 mice
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ABSTRACT: In order to further understand age-related physiological changes and to have in deph reference values for C57BL/6 mice, we undertook a study to assess the effects of aging on peripheral organ weights, and brain region weights in wild type C57BL/6 male mice. Peripheral organs, body and brain region weights were collected from young (3-4 months), mid (12months), old (23-28 months) and very old (>30months) mice. Significant increases are observed with aging in body, liver, heart, kidney and spleen organ weights. A decrease in organ weight is observed with aging in liver and kidney only in the very old mice. In contrast, testes weight decreases with age. Within the brain, hippocampi, striata and olfactory bulbs weight decreases with age. These data further our knowledge of the anatomical and biological changes that occur with aging and provide reference values for physiological-based pharmacokinetic studies in C57BL/6 mice. Copyright © 2015. Published by Elsevier Inc.
Experimental Gerontology 01/2015; 63. DOI:10.1016/j.exger.2015.01.003 · 3.49 Impact Factor