Cerebral Blood Flow and Gray Matter Volume Covariance Patterns of Cognition in Aging

Cognitive Neuroscience Division of the Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University College of Physicians and Surgeons, New York, New York
Human Brain Mapping (Impact Factor: 5.97). 12/2013; 34(12). DOI: 10.1002/hbm.22142
Source: PubMed


Advancing age results in altered cognitive and neuroimaging-derived markers of neural integrity. Whether cognitive changes are the result of variations in brain measures remains unclear and relating the two across the lifespan poses a unique set of problems. It must be determined whether statistical associations between cognitive and brain measures truly exist and are not epiphenomenal due solely to their shared relationships with age. The purpose of this study was to determine whether cerebral blood flow (CBF) and gray matter volume (GMV) measures make unique and better predictions of cognition than age alone. Multivariate analyses identified brain-wide covariance patterns from 35 healthy young and 23 healthy older adults using MRI-derived measures of CBF and GMV related to three cognitive composite scores (i.e., memory, fluid ability, and speed/attention). These brain-cognitive relationships were consistent across the age range, and not the result of epiphenomenal associations with age and each imaging modality provided its own unique information. The CBF and GMV patterns each accounted for unique aspects of cognition and accounted for nearly all the age-related variance in the cognitive composite scores. The findings suggest that measures derived from multiple imaging modalities explain larger amounts of variance in cognition providing a more complete understanding of the aging brain. Hum Brain Mapp, 2012. © 2012 Wiley Periodicals, Inc.

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Available from: Jason Randall Steffener
    • "Our results also indicate that lower GMV in temporal/insular and occipital clusters correlated with slower processing speed, mainly independent of confounders. This is in line with previous studies that found negative associations between processing speed and GMV in similar regions (Eckert et al., 2010;Steffener et al., 2013), but see (Ferreira et al., 2014) for positive effects of lower regional GMV in certain frontal areas on reaction times. However also note that slower processing speed has been mainly attributed to alterations in white matter integrity (Madden et al., 2012Madden et al., , 2009Penke et al., 2010), which might explain a lack of significance of a mediating path between BMI and processing speed in our sample. "
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    ABSTRACT: Midlife obesity has been associated with increased dementia risk, yet reports on brain structure and function are mixed. We therefore assessed the effects of body mass index (BMI) on gray matter volume (GMV) and cognition in a well-characterized sample of community-dwelled older adults. GMV was measured using 3T-neuroimaging in 617 participants (258 women, 60-80 years, BMI 17-41 kg/m2). Also, cognitive performance and various confounders including hypertension, diabetes and APOE-genotype were assessed. A higher BMI correlated significantly with lower GMV in multiple brain regions, including (pre)frontal, temporal, insular and occipital cortex, thalamus, putamen, amygdala and cerebellum, even after adjusting for confounders. Also, lower GMV in prefrontal and thalamic areas partially mediated negative effects of (1) higher BMI and (2) higher age on memory performance. We here showed that a higher BMI in older adults is associated with widespread gray matter alterations, irrespective of obesity-related co-morbidities and other confounders. Our results further indicate that a higher BMI induces structural alterations that translate into subtle impairments in memory performance in aging.
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    • "In particular, in the last 2 decades, a lot of human studies have used structural MRI (sMRI) to show a decrease in gray matter volume and thickness with age [Allen et al., 2005; Kochunov et al., 2011; Lemaitre et al., 2012; Salat et al., 2004; Sowell et al., 2003; Terribilli et al., 2011]. These measures have been correlated with a decrease in performance on complex tasks in older adults [Sakai et al., 2012; Squarzoni et al., 2012; Steffener et al., 2012]. With the advent of advanced imaging techniques such as functional MRI (fMRI) and diffusion MRI (dMRI), a more detailed picture of the function and anatomy of the brain can be obtained. "
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    ABSTRACT: Many studies have observed altered neurofunctional and structural organization in the aging brain. These observations from functional neuroimaging studies show a shift in brain activity from the posterior to the anterior regions with aging (PASA model), as well as a decrease in cortical thickness, which is more pronounced in the frontal lobe followed by the parietal, occipital, and temporal lobes (retrogenesis model). However, very little work has been done using diffusion MRI (dMRI) with respect to examining the structural tissue alterations underlying these neurofunctional changes in the gray matter. Thus, for the first time, we propose to examine gray matter changes using diffusion MRI in the context of aging. In this work, we propose a novel dMRI based measure of gray matter “heterogeneity” that elucidates these functional and structural models (PASA and retrogenesis) of aging from the viewpoint of diffusion MRI. In a cohort of 85 subjects (all males, ages 15–55 years), we show very high correlation between age and “heterogeneity” (a measure of structural layout of tissue in a region-of-interest) in specific brain regions. We examine gray matter alterations by grouping brain regions into anatomical lobes as well as functional zones. Our findings from dMRI data connects the functional and structural domains and confirms the “retrogenesis” hypothesis of gray matter alterations while lending support to the neurofunctional PASA model of aging in addition to showing the preservation of paralimbic areas during healthy aging. Hum Brain Mapp, 2013. © 2013 Wiley Periodicals, Inc.
    Full-text · Article · Aug 2014 · Human Brain Mapping
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    • "This increase of 27 percentage points demonstrates the value of including measures of brain activity in attempting to explain age group differences in task performance. Some of the remaining variance in task performance may be attributable to differences the grey matter and cerebral blood flow (Steffener et al., 2012a). "
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    ABSTRACT: Advancing age affects both cognitive performance and functional brain activity and interpretation of these effects has led to a variety of conceptual research models without always explicitly linking the two effects. However, to best understand the multifaceted effects of advancing age, age differences in functional brain activity need to be explicitly tied to the cognitive task performance. This work hypothesized that age-related differences in task performance are partially explained by age-related differences in functional brain activity and formally tested these causal relationships. Functional MRI data was from groups of young and old adults engaged in an executive task-switching experiment. Analyses were voxel-wise testing of moderated-mediation and simple mediation statistical path models to determine whether age group, brain activity and their interaction explained task performance in regions demonstrating an effect of age group. Results identified brain regions whose age-related differences in functional brain activity significantly explained age-related differences in task performance. In all identified locations, significant moderated-mediation relationships resulted from increasing brain activity predicting worse (slower) task performance in older but not younger adults. Findings suggest that advancing age links task performance to the level of brain activity. The overall message of this work is that in order to understand the role of functional brain activity on cognitive performance, analysis methods should respect theoretical relationships. Namely, that age affects brain activity and brain activity is related to task performance.
    Full-text · Article · Mar 2014 · Frontiers in Aging Neuroscience
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