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Review
. 2009 Dec;19(4):415-35.
doi: 10.1007/s11065-009-9113-2. Epub 2009 Aug 25.

Cerebral white matter integrity and cognitive aging: contributions from diffusion tensor imaging

David J Madden  1 , Ilana J Bennett, Allen W Song
Affiliations
Review

Cerebral white matter integrity and cognitive aging: contributions from diffusion tensor imaging

David J Madden et al. Neuropsychol Rev. 2009 Dec.

Abstract

The integrity of cerebral white matter is critical for efficient cognitive functioning, but little is known regarding the role of white matter integrity in age-related differences in cognition. Diffusion tensor imaging (DTI) measures the directional displacement of molecular water and as a result can characterize the properties of white matter that combine to restrict diffusivity in a spatially coherent manner. This review considers DTI studies of aging and their implications for understanding adult age differences in cognitive performance. Decline in white matter integrity contributes to a disconnection among distributed neural systems, with a consistent effect on perceptual speed and executive functioning. The relation between white matter integrity and cognition varies across brain regions, with some evidence suggesting that age-related effects exhibit an anterior-posterior gradient. With continued improvements in spatial resolution and integration with functional brain imaging, DTI holds considerable promise, both for theories of cognitive aging and for translational application.

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Figures

Figure 1
Figure 1
Examples of regions of interest (ROIs) used in the analysis of the diffusion tensor imaging (DTI) data. Panel A = raw tensor image, with increasing fractional anisotropy (FA) represented as increasing brightness; Panel B = tensor images with maximum value for fractional anisotropy set to a lower threshold. Lowering the maximal FA threshold decreases the potential contribution of variation in FA to the ROI definition. PCF = pericallosal frontal; GNU = genu of corpus callosum; SPN = splenium of corpus callosum; CIN = cingulum bundle; ASL = anterior portion of the superior longitudinal fasciculus; PSL = posterior portion of the superior longitudinal fasciculus. Figure modified from N.-k. Chen et al. (2009).
Figure 2
Figure 2
Example of a white matter skeleton used in tract-based spatial statistic (TBSS) analyses. The white matter skeleton (red) represents the center of tracts common to all participants. It is superimposed on the mean diffusion image, which was created by averaging aligned FA diffusion images from each individual in the group. Figure modified from Bennett et al. (2009). © Human Brain Mapping and Wiley-Liss, Inc., 2009.
Figure 3
Figure 3
Examples of deterministic tractography. Fiber tracts (in red) generated by target and source region placement, for a single participant. The orange areas are target regions, the green areas are source regions, and the blue areas are exclusion regions. All of these regions are operator defined, for each participant, using anatomical boundaries. The fiber tracking algorithm estimates tracts that pass through the target regions from the source regions, eliminating any fibers terminating in the exclusion regions. The approximate locations of output fiber tracts are illustrated by overlaying on a single-slice T1-weighted image. Panel A = genu; Panel B = splenium-occipital; Panel C = splenium-parietal; Panel D = superior longitudinal fasciculus. Figure modified from Madden et al. (2009). © Journal of Cognitive Neuroscience and MIT Press, 2009.
Figure 4
Figure 4
Mean FA as a function of age group and interval along the tract. Error bars represent 1 SE. Panel A = genu and superior longitudinal fasciculus; Panel B = splenium-occipital and splenium-parietal. For genu and splenium, the tracts are oriented left-right, with 0 = axial midline. For the superior longitudinal fasciculus, the tracts are oriented anterior-posterior and 0 = central sulcus. Below the mean FA data, t-values are plotted for the age group comparison at each point along the tract. The dotted line represents the significant t value for p < .05, two-tailed. Figure modified from Madden et al. (2009). © Journal of Cognitive Neuroscience and MIT Press, 2009.
Figure 5
Figure 5
Relation between choice reaction time and FA, for younger and older adults. ALC = anterior limb of internal capsule. Figure modified from Madden et al. (2004). © Neuroimage and Elsevier, 2004.
Figure 6
Figure 6
FA maps (top) acquired with sensitivity encoding (SENSE) DTI and base image (bottom, with anatomical reference shown in red). Panel A = without correction; Panel B = with ΔB0 correction; Panel C = with ΔB0 and eddy current correction. The artificially high FA values at the edge, frontal lobe, and gray/white matter boundary are completely removed with correction in Panel C. The technique also showed improved distortion correction and higher signal-to-noise ratio (by 19.8%), compared to the DTI acquisition using a double-refocused spin-echo sequence (Panel D). Figure modified from Truong et al. (2008). © Neuroimage and Elsevier, 2008.
Figure 7
Figure 7
High resolution self-navigated interleaved spiral (SNAILS) DTI with an in-plane resolution of 390 × 390 μm2. Panel A = FA map; Panel B = color-coded FA map with red representing the direction of anterior-posterior, green representing the direction of left-right and blue representing the direction of superior-inferior; Panel C = myelin stained brain section in a similar location obtained from the Yakovlev collection (National Museum of Health and Medicine, Washington, DC); Panels D, E, and F = an enlarged ROI indicated by the rectangular boxes in A, B and C showing the FA map, the color-coded FA map and the brain section respectively. Substructures of the internal capsule are shown clearly in the FA maps that match the tissue morphology (arrow). Figure modified from Liu et al. (2009). © Magnetic Resonance in Medicine and Wiley-Liss, Inc., 2009.
Figure 8
Figure 8
Relation between white matter integrity (fractional anisotropy) in the genu of the corpus callosum and intrinsic functional connectivity (in networks connected with inferior frontal gyri), for older adults. Figure modified from N.-k. Chen et al. (2009).
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