A structural equation modeling investigation of age-related variance in executive function and DTI measured white matter damage

Clinical Neuroscience, St. George's University of London, Cranmer Terrace, London SW17 0RE, UK.
Neurobiology of aging (Impact Factor: 5.01). 05/2007; 29(10):1547-55. DOI: 10.1016/j.neurobiolaging.2007.03.017
Source: PubMed


Cognitive changes in normal aging have been explained by the frontal-executive hypothesis, but the assumptions made by this hypothesis concerning the neurobiological causes are still a matter of debate. Executive functions (EF) may activate neural networks that include disparate grey matter regions, and rely on the integrity of white matter connections. In 118 adults (50-90 years old) from the GENIE study, white matter integrity was measured using diffusion tensor imaging, and information processing speed, fluid intelligence and EF were assessed. A theory-driven structural equation model was developed to test associations between variables. The model was revised, removing non-significant paths. The adjusted model explained well the covariance in our data; and suggested that the reduction in white matter integrity associated with age directly affected only working memory. Fluid intelligence was mediated by all measured cognitive variables. The results suggest that white matter integrity may be particularly important for abilities activating complex neural networks, as occurs in working memory. Integration of the information processing speed and frontal-executive hypotheses may provide important information regarding common, unique, and mediating factors in cognitive aging.

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Available from: Christopher A Clark, Mar 31, 2015
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    • "Previous neuroscientific studies have shown that the dopaminergic system plays an important role in CPMDT, emotional control, and motivational state. A wide range of evidence has established the role of dopamine in motivation (Carlson, 2001). With regard to creativity or creative potential, recent neuroimaging studies have shown an association between CPMDT and dopamine receptor binding potential (De Manzano et al., 2010). "
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    ABSTRACT: Previous neuroscientific studies have shown that the dopaminergic system plays an important role in creative potential measured by divergent thinking (CPMDT), emotional control, and motivational state. However, although associations between two of these four components have been previously established (e.g., the association between CPMDT and emotional control, the association between CPMDT and motivational state, etc.), the interactions between these four remain unknown. The purpose of this study was to reveal these interactions using path analyses. The Taq1A polymorphism of the dopamine D2 receptor (DRD2) gene was used for this purpose. For measuring emotional intelligence (EI), we used the Japanese version of the Emotional Intelligence Scale. CPMDT was measured using the S-A creativity test. Motivational state was measured using the Vigor subscale of the Japanese version of the Profile of Mood Scale (POMS). Data from 766 healthy, right-handed individuals (426 men and 340 women; 20.7 ± 1.9 years of age) were used in this study. There were significant and robust positive relationships among measures of CPMDT, EI, and motivational state across sex. In addition, the polymorphism of the DRD2 gene was significantly associated with EI, specifically in females. Path analysis in females indicates that the model in which (a) the DRD2 polymorphism primarily facilitates EI, (b) EI in turn facilitates CPMDT and leads to a better motivational state, and (c) a better motivational state also directly facilitates CPMDT explains the data in the most accurate manner. This study suggested a comprehensive picture of the cascade of the associations among dopamine, EI, motivational state, and CPMDT at least in females.
    Frontiers in Psychology 07/2015; 6:912. DOI:10.3389/fpsyg.2015.00912 · 2.80 Impact Factor
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    • "These findings were recently corroborated in a neuroimaging study using SEM to structure the neural implications of aging effects on processing speed and working memory. Although white matter abnormality was only associated with decline in working memory, decline in processing speed was suggested to significantly impact other cognitive abilities (Charlton et al., 2008, however, see Penke and Deary, 2010 for a critique of their methodological approach). "
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    ABSTRACT: We examined the effects of normal aging on visual cognition in a sample of 112 healthy adults aged 60–75. A testbattery was designed to capture high-level measures of visual working memory and low-level measures of visuospatial attention and memory. To answer questions of how cognitive aging affects specific aspects of visual processing capacity, we used confirmatory factor analyses in Structural Equation Modeling (SEM; Model 2), informed by functional structures that were modeled with path analyses in SEM (Model 1). The results show that aging effects were selective to measures of visual processing speed compared to visual short-term memory (VSTM) capacity (Model 2). These results are consistent with some studies reporting selective aging effects on processing speed, and inconsistent with other studies reporting aging effects on both processing speed and VSTM capacity. In the discussion we argue that this discrepancy may be mediated by differences in age ranges, and variables of demography. The study demonstrates that SEM is a sensitive method to detect cognitive aging effects even within a narrow age-range, and a useful approach to structure the relationships between measured variables, and the cognitive functional foundation they supposedly represent.
    Frontiers in Psychology 01/2015; 5. DOI:10.3389/fpsyg.2014.01596 · 2.80 Impact Factor
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    • "Nordahl and co-workers used ARWMC as a marker for white matter degeneration to demonstrate that increases in both global and regional dorsal prefrontal cortex ARWMC volume were associated with decreases in prefrontal cortex activity and decreased activity in the posterior parietal and anterior cingulate cortex during working memory performance [24]. Charlton et al. performed MRI and cognitive testing in 84 middle-aged and elderly adults at baseline and after two years and showed a correlation of diffusion tensor imaging (DTI) in white matter histograms with a change in working memory function [25]. A more recent study found significant increases in fMRI activation in the left dorsal and ventral lateral prefrontal cortices with increased working memory load and also with increased age that correlated with DTI derived fractional anisotropy (FA) in frontal brain regions [26]. "
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    ABSTRACT: Subcortical age-related white matter changes (ARWMC) are a frequent finding in healthy elderly people suggested to cause secondary tissue changes and possibly affecting cognitive processes. We aimed to determine the influence of the extent of ARWMC load on attention and working memory processes in healthy elderly individuals. Fourteen healthy elderly subjects (MMSE >26; age 55-80 years) performed three fMRI tasks with increasing difficulty assessing alertness, attention (0-back), and working memory (2-back). We compared activation patterns in those with only minimal ARWMC (Fazekas 0-1) to those with moderate to severe ARWMC (Fazekas 2-3). During the fMRI experiments, the study population showed activation in brain areas typically involved in attention and working memory with a recruitment of cortical areas with increasing task difficulty. Subjects with higher lesion load showed a higher activation at all task levels with only sparse increase of signal with increasing complexity. In the lower lesion load group, rising task difficulty lead to a significant and widely distributed increase of activation. Although the number of patients included in the study is small, these findings suggest that even clinically silent ARWMC may affect cognitive processing and lead to compensatory activation during cognitive tasks. This can be interpreted as a reduction of functional reserve and may pose a risk for cognitive decline in these patients.
    PLoS ONE 08/2014; 9(8):e103359. DOI:10.1371/journal.pone.0103359 · 3.23 Impact Factor
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