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

Abstract

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.

Keywords: aging; cognition; diffusion tensor imaging; disconnection; magnetic resonance imaging; white matter integrity.

Figure 1

A classification of major white matter tracts within the brain visualized using diffusion tensor tractography and superimposed on medial and lateral views of the brain surface. Projection tracts that connect cortical to subcortical structures include: descending fibers projecting from the motor cortex to basal ganglia, midbrain motor nuclei (corticobulbar tract), and the spinal cord (pyramidal tract); ascending fibers from the thalamus to the cortical mantle (thalamic projections); and the fornix, which connects the medial temporal lobe to hypothalamic nuclei. Commissural tracts that connect the two hemispheres include: the corpus callosum, which is the largest white matter bundle and connects cortical regions within frontal, parietal, occipital and temporal lobes; and the anterior commissure, which connects the left and right amygdalae and ventromedial temporo-occipital cortex. Association tracts that run within each hemisphere connecting distal cortical areas include: the cingulum, which connects medial frontal, parietal, occipital, temporal and cingulate cortices; the arcuate/superior longitudinal fasciculus, which connects perisylvian frontal, parietal and temporal cortices; the uncinate fasciculus, which connects orbitofrontal to anterior and medial temporal lobes; the inferior longitudinal fasciculus, which connects the occipital and temporal lobes; and the inferior fronto-occipital fasciculus, which connects the orbital and lateral frontal cortices to occipital cortex. Reproduced with permission from Catani and Ffytche (2005).

Figure 2

Difference in fractional anisotropy (FA) from diffusion tensor imaging, for the genu (Panel A) and splenium (Panel B) of the corpus callosum, for younger and older adults. Data are voxelwise t maps of the younger > older FA difference score, across 23 younger adults (19–39 years of age) and 23 older adults (60–79 years of age). Participants were healthy, community-dwelling individuals without any sign of cognitive impairment on neuropsychological testing or history of cardiovascular disease (other than hypertension). Data are overlaid on a T1-weighted template, in radiological orientation (left = right). Age-related decline in FA is most prominent in the lateral segments of the genu. Authors' unpublished data.

Figure 3

Age-related differences in axial (Panel A) and radial (Panel B) diffusivity as a function of age group and region. Panel A reveals similar AD between younger (black bars) and older (gray bars) adults across all white matter regions, whereas Panel B reveals increased RD in older adults relative to younger adults in the genu of the corpus callosum, splenium–occipital region (Sp–Occ), and superior longitudinal fasciculus (SLF). Sp–Par = splenium–parietal. Reproduced with permission from Madden et al. (2009b).

Figure 4

Demonstration of the space of anisotropy decomposed into two orthogonal channels: fractional anisotropy (FA = R₂) and mode (= R₃ = K₃). Each glyph represents the shape of diffusion tensors with constant tensor norm rendered with superquadric glyphs. Increasing distance from the top left spherical glyph indicates increasing FA, whereas the angular deviation from the left edge indicates increasing mode as it transitions from planar anisotropic (mode = −1), to orthotropic (mode = 0), to linear anisotropic (mode = 1). Glyphs along constant radii (constrained to an arc) are of constant fractional anisotropy, but of varying mode. This figure explicitly demonstrates that increases in FA do not necessarily indicate increasing linear anisotropy. The space of FA and mode is correctly diagrammed as an isosceles triangle; note that isocontours of FA are orthogonal to isocontours of mode. Reproduced with permission from Ennis et al. (2006).

Figure 5

An anterior-posterior gradient of age-related difference in FA. A significant positive relationship was observed between the percent age group difference in FA (y-axis, calculated for each white matter cluster as FA in older adults minus FA in younger adults, divided by the average FA value for that cluster) and the location of white matter clusters (x-axis, measured as the average y coordinate for each white matter cluster in mm according to MNI space). As the regression line indicates, age-related decreases in FA were significantly larger in anterior (Panel B) versus posterior (Panel A) clusters. This relationship was significant for the frequently reported superior clusters (closed circle) with z coordinates above or traversing zero, but did not attain significance for inferior clusters (open circles). However, this anterior-posterior gradient was accompanied by significant variability, with two posterior clusters (left inferior sagittal stratum, right cerebellum) exhibiting the largest age group differences in FA. See original text for white matter clusters that correspond to each y coordinate. Reproduced with permission from Bennett et al. (2010).

Figure 6

Positive relationships between white matter tract integrity and cognitive performance. The left column presents 3D renderings of white matter tracts connecting subcortical nuclei to cortical resting state networks identified using independent components analysis (IC). Tracts include connections between thalamus (IC1) and anterior default mode network (IC6) (Row 1), inferior putamen (IC4) and anterior default mode network (Row 2), inferior putamen and dorsal attention network (IC9) (Row 3), inferior putamen and calcarine/visual/lingual cortices (IC17) (Row 4), and superior putamen (IC3) and left sensorimotor cortex (IC13) (Row 5). The number of participants displaying each connection is noted in the bottom right corner of each rendering. Scatterplots display pairwise correlations between FA from each tract and composite scores of executive function (middle column) and processing speed (right column). Least squares regression lines have been superimposed on the data, and correlation coefficients indicate significant effects at p < 0.01. Reproduced with permission from Ystad et al. (2011).

Figure 7

Relationships among age, fronto-striatal white matter tract integrity and probabilistic reward learning. White matter tracts connecting ventral tegmental area and nucleus accumbens (Panel A), nucleus accumbens and dorsomedial thalamus (Panel B), dorsomedial thalamus and medial prefrontal cortex (Panel C), and medial prefrontal cortex and nucleus accumbens (Panel D) are presented from a representative participant. Scatter plots display pairwise correlations between FA from each tract and either age (left) or probabilistic reward learning (right). The thalamocortical (Panel C) and corticostriatal (Panel D) tracts were significantly associated with both age and learning. Furthermore, a combined measure of thalamocorticostriatal (TCS) white matter integrity significantly mediated age differences in probabilistic reward learning (Panel E). Path coefficients are standardized βs. *p < 0.05, two-tailed. Reproduced with permission from Samanez-Larkin et al. (2012).

Figure 8

Schematic illustration of relationships among age (A), brain (B), and cognitive (C) variables (top). Four alternative models of correlations among these variables are presented, including a (1) brain-mediating model, in which white matter integrity mediates relationships between age and cognitive performance; (2) cognition-mediating model, in which cognitive performance mediates relationships between age and white matter integrity; (3) independent-variable model, in which white matter integrity and cognitive performance are independent and only related to each other through their relationship with age; and (4) additional-variable model, in which age, white matter integrity, and cognitive performance are related to each other through their relation to an unidentified third variable. Reproduced with permission from Salthouse (2011).

Figure 9

White matter lesion volume. Panel A: White matter lesions for individual adults 20, 48, and 65 years of age, in T2-weighted FLAIR images. Participants were healthy, community-dwelling individuals without any sign of cognitive impairment on neuropsychological testing or history of cardiovascular disease (other than hypertension). Lesions, as identified from a semi-automated program separating lesions from normal white matter appear in red. Panel B: Composite lesion maps for 23 younger adults (19–39 years of age), 19 middle-aged adults (40–59 years of age), and 16 older adults (60–79 years of age), overlaid on a T1-weighted template. Images are in radiological orientation (left = right). Color scale represents the number of individuals within each group exhibiting a lesion, per voxel. Authors' unpublished data.