White Matter Integrity in the Splenium of the Corpus Callosum is Related to Successful Cognitive Aging and Partly Mediates the Protective Effect of an Ancestral Polymorphism in ADRB2

Penke, Lars; Maniega, Susana Muñoz; Houlihan, Lorna M.; Murray, Catherine; Gow, Alan J.; Clayden, Jonathan D.; Bastin, Mark E.; Wardlaw, Joanna M.; Deary, Ian J.

doi:10.1007/s10519-009-9318-4

Cited by 37 publications

(41 citation statements)

References 77 publications

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“…Consistent with the findings reported in our previous DTI reviews of cognitive aging (Madden et al, 2009a; Madden et al, 2012), recent DTI studies have also observed that increased white matter integrity (i.e., increased FA, decreased diffusivity) relates to better cognitive performance across groups of younger and older adults (Schulze et al, 2011; Coxon et al, 2012), within groups of older adults (Charlton et al, 2009; Penke et al, 2010a; Sexton et al, 2010; Yassa et al, 2010; Forstmann et al, 2011; Metzler-Baddeley et al, 2011; Ystad et al, 2011; Antonenko et al, 2012; Bosch et al, 2012; Brickman et al, 2012; He et al, 2012; Lockhart et al, 2012; Van Impe et al, 2012; Borghesani et al, 2013; Jacobs et al, 2013), and in groups that span childhood to later adulthood (Bendlin et al, 2010; Stamatakis et al, 2011; Salami et al, 2012; Samanez-Larkin et al, 2012; Sasson et al, 2012, 2013). …”

Section: White Matter Integrity Correlates With Cognition In Agingsupporting

confidence: 89%

“…After accounting for this age-related variance, their results still revealed significant relationships between white matter integrity and cognitive performance across younger and older adults (e.g., Coxon et al, 2012), lifespan samples (e.g., Bendlin et al, 2010; Salami et al, 2012; Samanez-Larkin et al, 2012; Sasson et al, 2012, 2013), and within older adults (e.g., Schiavone et al, 2009; Penke et al, 2010a; Penke et al, 2010b; Sexton et al, 2010; Brickman et al, 2012; Jacobs et al, 2013). Thus, age does not fully explain relationships between white matter integrity and cognitive performance.…”

Section: White Matter Integrity Correlates With Cognition In Agingmentioning

confidence: 99%

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Disconnected aging: Cerebral white matter integrity and age-related differences in cognition

2014

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Cognition arises as a result of coordinated processing among distributed brain regions and disruptions to communication within these neural networks can result in cognitive dysfunction. Cortical disconnection may thus contribute to the declines in some aspects of cognitive functioning observed in healthy aging. Diffusion tensor imaging (DTI) is ideally suited for the study of cortical disconnection as it provides indices of structural integrity within interconnected neural networks. The current review summarizes results of previous DTI aging research with the aim of identifying consistent patterns of age-related differences in white matter integrity, and of relationships between measures of white matter integrity and behavioral performance as a function of adult age. We outline a number of future directions that will broaden our current understanding of these brain-behavior relationships in aging. Specifically, future research should aim to (1) investigate multiple models of age-brain-behavior relationships; (2) determine the tract-specificity versus global effect of aging on white matter integrity; (3) assess the relative contribution of normal variation in white matter integrity versus white matter lesions to age-related differences in cognition; (4) improve the definition of specific aspects of cognitive functioning related to age-related differences in white matter integrity using information processing tasks; and (5) combine multiple imaging modalities (e.g., resting-state and task-related functional magnetic resonance imaging; fMRI) with DTI to clarify the role of cerebral white matter integrity in cognitive aging.

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Section: White Matter Integrity Correlates With Cognition In Agingsupporting

confidence: 89%

Section: White Matter Integrity Correlates With Cognition In Agingmentioning

confidence: 99%

Disconnected aging: Cerebral white matter integrity and age-related differences in cognition

2014

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“…The absolute FA values are sensitive to fiber coherence, myelination levels, and axonal integrity, and have been widely used as an index of white matter integrity (Thomason and Thompson, 2011). FA has emerged as a sensitive index of diffuse abnormalities in many brain disorders including Alzheimer’s disease (AD) (Clerx et al, 2012; Teipel et al, 2012); in many studies, it is related to cognitive performance (Penke et al, 2010a; Penke et al, 2010b) and is altered in numerous psychiatric disorders including major depressive disorder (Carballedo et al, 2012) and bipolar disorder (Barysheva et al, 2012; Sprooten et al, 2011). Patient-control differences in FA values are also among the most replicable and consistent neuroimaging findings in schizophrenia (Alba-Ferrara and de Erausquin, 2013; Friedman et al, 2008; Kochunov et al, 2012a; Mandl et al, 2012; Nazeri et al, 2012; Perez-Iglesias et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Multi-site study of additive genetic effects on fractional anisotropy of cerebral white matter: Comparing meta and megaanalytical approaches for data pooling

et al. 2014

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Combining datasets across independent studies can boost statistical power by increasing the numbers of observations and can achieve more accurate estimates of effect sizes. This is especially important for genetic studies where a large number of observations are required to obtain sufficient power to detect and replicate genetic effects. There is a need to develop and evaluate methods for joint-analytical analyses of rich datasets collected in imaging genetics studies. The ENIGMA-DTI consortium is developing and evaluating approaches for obtaining pooled estimates of heritability through meta-and mega-genetic analytical approaches, to estimate the general additive genetic contributions to the intersubject variance in fractional anisotropy (FA) measured from diffusion tensor imaging (DTI). We used the ENIGMA-DTI data harmonization protocol for uniform processing of DTI data from multiple sites. We evaluated this protocol in five family-based cohorts providing data from a total of 2248 children and adults (ages: 9–85) collected with various imaging protocols. We used the imaging genetics analysis tool, SOLAR-Eclipse, to combine twin and family data from Dutch, Australian and Mexican-American cohorts into one large “mega-family”. We showed that heritability estimates may vary from one cohort to another. We used two meta-analytical (the sample-size and standard-error weighted) approaches and a mega-genetic analysis to calculate heritability estimates across-population. We performed leave-one-out analysis of the joint estimates of heritability, removing a different cohort each time to understand the estimate variability. Overall, meta- and mega-genetic analyses of heritability produced robust estimates of heritability.

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“…Top associations from relevant GWAS studies were included in this analysis (Fornage, et al, 2011,Furney, et al, 2011,Lopez, et al, 2012), but the focus was on potentially functional coding polymorphisms previously associated with neuroimaging phenotypes (Braskie, et al, 2011,Chiang, et al, 2011,Godin, et al, 2009,Honea, et al, 2009,Jahanshad, et al, 2012,Kim, et al, 2013,Kohannim, et al, 2012,Kohara, et al, 2003,Penke, et al, 2010,Schmidt, et al, 2000,Schmidt, et al, 2001,Smith, et al, 2010,Sprooten, et al, 2011). The selected candidate SNPs were not associated with any of the neuroimaging phenotypes assessed in the DHS-Mind cohort, with no SNP reaching a Bonferroni corrected p-value threshold of p=0.0029.…”

Section: Resultsmentioning

confidence: 99%

Heritability and genetic association analysis of neuroimaging measures in the Diabetes Heart Study

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Cox

Hugenschmidt

et al. 2015

Neurobiology of Aging

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Patients with type 2 diabetes are at increased risk of age-related cognitive decline and dementia. Neuroimaging measures such as white matter lesion volume, brain volume, and fractional anisotropy may reflect the pathogenesis of these cognitive declines, and genetic factors may contribute to variability in these measures. This study examined multiple neuroimaging measures in 465 participants from 238 families with extensive genotype data in the type 2 diabetes enriched Diabetes Heart Study-Mind cohort. Heritability of these phenotypes and their association with candidate single nucleotide polymorphisms (SNPs) and SNP data from genome-and exome-wide arrays was explored. All neuroimaging measures analysed were significantly heritable (ĥ2 =0.55–0.99 in unadjusted models). Seventeen candidate SNPs (from 16 genes/regions) associated with neuroimaging phenotypes in prior studies showed no significant evidence of association. A missense variant (rs150706952, A432V) in PLEKHG4B from the exome-wide array was significantly associated with white matter mean diffusivity (p=3.66×10−7) and gray matter mean diffusivity (p=2.14×10−7). This analysis suggests genetic factors contribute to variation in neuroimaging measures in a population enriched for metabolic disease and other associated comorbidities.

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White Matter Integrity in the Splenium of the Corpus Callosum is Related to Successful Cognitive Aging and Partly Mediates the Protective Effect of an Ancestral Polymorphism in ADRB2

Cited by 37 publications

References 77 publications

Disconnected aging: Cerebral white matter integrity and age-related differences in cognition

Disconnected aging: Cerebral white matter integrity and age-related differences in cognition

Multi-site study of additive genetic effects on fractional anisotropy of cerebral white matter: Comparing meta and megaanalytical approaches for data pooling

Heritability and genetic association analysis of neuroimaging measures in the Diabetes Heart Study

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