We have previously shown that WMH, regardless of location, primarily affect frontal lobe metabolism and function [3
]. This study was designed to elucidate the relationship between regional white matter integrity and metabolism. Our data show inter-regional positive correlations between FA and gray matter metabolism in the prefrontal, temporal and parietal regions. Our results suggest left prefrontal FA (MFWM and IFWM) is associated with left temporal and parietal metabolism. Further, left temporal FA correlated with left prefrontal metabolism. Finally, bilateral parietal FA correlated with bilateral temporal metabolism. Interestingly, more unilateral FA-metabolic associations were observed in the left hemisphere. To elucidate the potential importance of regional white matter FA, we regressed total white matter FA with metabolism. The same regions continually appear with low FA correlating to decreased metabolism (bilateral cuneus and insula, and left calcarine, precuneus, and lingual regions). This suggests the global mean FA measure is similar to the regional FA evaluations, however we did not find prefrontal metabolic associations with the global FA analysis. Therefore regional FA can aid in elucidating more regionally specific changes when investigating FA-metabolic associations.
The major white matter tracts that seem to be associated with metabolism are the longitudinal fasciculus and those associated with fronto-temporal connectivity. The superior longitudinal fasciculus (SLF) runs the span of the prefrontal cortex to the parietal lobe and the inferior longitudinal fasciculus connects the temporal to posterior brain regions. The SLF subdivides into four distinct components, SLFI, SLFII, SLFIII, and the arcuate fasciculus. Briefly, SLFI primarily connects the superior parietal to the premotor and prefrontal cortices, SLFII connects the inferior parietal to the dorsal premotor and prefrontal cortices, and the SLFIII connects the ventral parietal to ventral premotor and prefrontal cortices [35
]. Consistent with these known SLF relationships, our data illustrate lower FA in IFWM and MFWM is associated with parietal lobe hypometabolism, whereas the prefrontal FA relationship with temporal regional metabolism is most likely reflected via the complex fronto-temporal associations. The fronto-temporal associations are more complex and less understood. Several studies now show functional connectivity between frontal and temporal regions using functional imaging [21
] and the combination of functional and tensor imaging [22
]. Most notably, the cingulum bundle connects the posterior cingulate cortex/retrosplenial cortex (PCC/RSC) to the medial prefrontal cortex. Further, the caudal regions of the PCC/RSC also connect to the medial temporal region. Although the temporal and prefrontal regions have yet to reveal direct inter-connecting white matter pathways, there are likely to be pathways for information transfer between the two regions. Therefore, the parietal associations with both frontal and temporal areas may be indicative of SLF alterations, while the fronto-temporal relationships are probably a combination of white matter pathways, possibly those discussed earlier [21
]. While there are numerous white matter tracts that are putatively affected by aging and disease, DTI is most likely detecting the larger white matter bundles. As technology improves, our ability to detect the contributions of smaller white matter tracts will further aid in our understanding of the relationships between white and gray matter changes.
Both grey and white matter structures are crucial for cognition. Our data reveal associations between gray matter metabolism and white matter integrity in regions associated with specific cognitive function. The left prefrontal cortex is proposed to be involved in the encoding of novel information into episodic memory [36
]. Further, the temporoparietal regions are associated with specific cognitive tasks, such as the hippocampus being associated with episodic memory [37
] and the parietal regions associated with sensory function but also episodic memory [38
]. Our data show correlations between white matter integrity and gray matter metabolism for the prefrontal cortex, temporal and parietal regions. Importantly, these regions are also associated with the cognitive processes affected in AD and CVD, suggesting a link with white matter degeneration and gray matter hypometabolism.
As noted previously, there are more FA associations with the left hemisphere. Our FDG-PET regression analysis with MMSE revealed a predominant left hemispheric hypometabolism, which would consequently affect our FA associations. Left hemispheric dominance, especially within the temporoparietal regions, has been observed in many metabolic studies and is obviously dependent on the composition of the cohort and, when relevant, the covariate used in the analysis [15
]. Our metabolic correlations with MMSE also support our restricted FA associations between the left IFWM and temporal regions. The left IFWM FA associations with the temporal regions but not with proximal frontal regions are probably due to the preservation of frontal metabolism in this cohort. There is a small region in the left prefrontal cortex that correlates to MMSE, however the frontal regions are quite preserved with respect to metabolism. This is not surprising as frontal hypometabolism is usually observed in progressed AD and therefore, we suspect is not prevalent enough in this sample to correlate with the FA of the frontal lobe. Lastly, we did investigate between group FA differences across white matter ROI’s. As anticipated, the trend of FA values was AD < CIND < Normal. Due to the insufficient sample sizes for between group analyses, we have restricted our interpretation to represent trends and will not further speculate on these results.
We do note the striatum and thalamic regions correlated with some white matter ROI’s. These subcortical regions are atrophic in dementia, and may be in close proximity to lacunar infarctions. In addition, the thalamus has numerous divergent cortical projections. These are potential reasons that may explain the subcortical-cortical correlations within this cohort.
Our corpus callosum data correspond well with the current literature, in that, the posterior region was more associated with larger areas of metabolic change [8
]. The splenium had the greatest association with metabolic decline. This is in concert with the numerous parietal, occipital and temporal regional associations and is not surprising as the posterior portion of the brain is more affected in AD.
There are at least two possible interpretations of these results. First, the degeneration of neuronal cell bodies causes reduced metabolism in the affected gray matter region and that the degradation of associated axons is responsible for the decreased fractional anisotropy detected in the associated white matter tracts. An alternative interpretation is that the pathology begins in the white matter tracts, and that disconnection between cortical regions is responsible for reduced glucose uptake. The first interpretation would support the underlying pathology of AD, with gray matter changes or atrophy causing white matter alterations. While the second interpretation would support the underlying pathology of CVD, with white matter changes causing gray matter alterations. In this study we had neither the sample size nor all of the data necessary to convincingly test these alternative explanations, however, our data provide evidence of the complex and intriguing relationship between white matter integrity and gray matter function.
The major limitation of this study is the small sample size. However, the correlations we have shown are relatively strong and we hope this will initiate more studies of white matter integrity and metabolism. Another limitation is the multiple comparisons of this voxel based study limit interpretation of the results. Although we did not correct for multiple comparisons, we did restrict our findings to a cluster size of 100 or larger. This inhibits finding smaller significant areas, but it minimizes the chance of type I error. Because our results revealed large areas previously associated with CVD and AD, it seems unlikely that they are a consequence of false discovery. It is possible that the FA-metabolic correlations could be due to individual trends (i.e. white and gray matter alterations that are not related and just coincident events or trends within the dataset) rather than reflecting coherent relationships, thus we anticipate future studies will aid in our understanding of how metabolism and white matter potentially interact in aging and dementia. A further limitation of this study results from the need to coregister the DTI to the T1 image. The DTI images were acquired using an echoplanar sequence which has considerably more geometric distortion than the T1 image; thus, there may not be exact correspondence between the DTI and T1 images. To address this limitation, we drew small regions in the white matter for the parietal and temporal regions directly on the DTI image. These regions could be drawn with relative reliability on the DTI, where we were unable to reliably draw the IFWM and MFWM. We then extracted the FA data from the parietal and temporal DTI regions of interest and used them in the same SPM analyses as we did with the original T1 ROI’s. The resulting significant regions were extremely similar and did not alter our overall results. This confirms the distortion, in our sample, was negligible and the T1 ROI’s were appropriately placed on the DTI post coregistration. Finally, DTI is a relatively new imaging technique and will aid in elucidating disease progression as the technology continues to improve. We anticipate this paper will initiate more investigations of white matter tracts and associated regional metabolism.
Our data reveal as white matter tracts degenerate, associated cortices also show lower metabolism. Therefore, white matter integrity and gray matter metabolism are intimately associated. This association is probably based on several factors such as a loss of white matter innervation to the gray matter, or loss of cortical neurons and axons resulting in white matter degeneration. The relationships are interesting but complex, and with further investigation these associations could facilitate our understanding of aging and neurodegeneration.