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Widespread Effects of Hyperintense Lesions on Cerebral White Matter Structure

¹ Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, DUMC Box 3903, Durham, NC 27710.
² Neuropsychiatric Imaging Research Laboratory, Duke University Medical Center, Durham, NC.
³ Present address: Department of Psychiatry, Inha University Hospital, Incheon, South Korea.
⁴ Department of Radiology, Duke University Medical Center, Durham, NC.

Received August 30, 2006; accepted after revision December 29, 2006.

 
Address correspondence to W. D. Taylor. (taylo066{at}mc.duke.edu).

Supported by National Institute of Mental Health grants K23 MH65939, P50 MH60451, and R01 MH54846.

Abstract

Top Abstract Introduction Materials and Methods Results Discussion References

 
OBJECTIVE. Hyperintense lesions are a common finding on neuroimaging and are associated not only with aging, medical illness, and some invasive medical procedures, but also with neurologic and psychiatric morbidity. We hypothesized that hyperintense lesions are associated with alterations in white matter structure beyond the visible lesion boundaries as assessed with diffusion tensor imaging (DTI).

SUBJECTS AND METHODS. Eighty-two neurologically intact older individuals completed brain MRI with DTI. DTI scans were analyzed using regions of interest placed in normal-appearing white matter to measure fractional anisotropy and diffusivity in the white matter of the frontal lobe, the genu of the corpus callosum, and the internal capsule. Hyperintense lesions volumes were measured separately in subcortical gray matter and anterior white matter through a semiautomated segmentation program. The relationship between lesion volumes and DTI measures was examined while controlling for patient age, patient sex, and total cerebral volume.

RESULTS. Greater anterior white matter lesion volumes were associated with higher diffusivity and lower anisotropy in the white matter of the dorsolateral prefrontal cortex and with higher diffusivity of the internal capsule and white matter lateral to the anterior cingulate cortex. Gray matter lesion volumes were associated with higher diffusivity in the genu of the corpus callosum and the internal capsule.

CONCLUSION. Ischemic hyperintense lesions are associated with widespread effects on the structure of the frontal lobe white matter and central white matter structures. This may reflect effects of lesions on neural circuits or identification of white matter changes that have not yet become visible on conventional MRI.

Keywords: brain • diffusion tensor imaging • geriatrics • MRI • neuroanatomy

Introduction

Top Abstract Introduction Materials and Methods Results Discussion References

 
Hyperintense lesions are bright regions in the brain parenchyma on FLAIR and on T2-weighted MRI [1]. These lesions are associated with increased patient age [2–4] and medical comorbidity, particularly vascular risk factors such as hypertension [5–7]. New development of these lesions may also be seen after invasive medical procedures, such as cardiac surgery [1]. Acute silent ischemic lesions detected initially only on diffusion-weighted imaging often progress to persistent findings of ischemic damage appearing as hyperintense lesions on T2-weighted images [1], and these persistent lesions exhibit patterns of diffusion measures similar to those seen with chronic stroke [8]. Autopsy studies also suggest that, in many cases, these lesions are of ischemic origin [9].

There is some debate about the clinical significance of these silent lesions. Clearly, most acute lesions developing from medical or surgical procedures do not result in obvious neurologic deficits [1], and chronic lesions can be seen in healthy older individuals in the absence of neurologic or psychiatric disorders [7, 10]. However, greater lesion burden is also associated with cognitive [6, 11, 12], motor [13], and psychiatric [14–16] morbidity.

One may account for these different clinical outcomes by hypothesizing that lesions that occur in specific regions [17] or that are of greater severity [12] will more likely produce neurologic or psychiatric symptoms through a broad effect on neural circuits. Theoretically, lesions impinging on neural circuits involved in mood regulation or cognition would affect parts of those circuits distant to the lesion itself due to direct damage to axons or indirect damage from remote effects of ischemic damage to subcortical structures, which may result in denervation or other broader effects. Alternatively, it is also possible that in some people, the severity of lesions visible on FLAIR and on T2-weighted imaging is the "tip of the iceberg," representing broader white matter disease that at the time of evaluation was not visible on conventional neuroimaging.

View larger version (45K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 1 —Drawing shows brain parcellation for hyperintensity volume measurements. Axial plane was created along anterior commissure–posterior commissure (AC–PC) line. Next, coronal planes were created perpendicular to axial plane at anterior and posterior extents of corpus callosum. Finally, a third coronal plane was created at midpoint between first two coronal planes, dividing brain into anterior and posterior halves.

Materials and Methods

Top Abstract Introduction Materials and Methods Results Discussion References

 
Subjects
Subjects included in this study were recruited as nondepressed control subjects in studies of late-life depression, and their data have been previously used to examine group differences in DTI measures [18] and hyperintense lesion volumes [15]. These subjects were participants in the National Institute of Mental Health (NIMH) Conte Center for the Neuroscience of Depression, located at Duke University Medical Center, and were recruited from the Duke University Aging Center Subject Registry. They were 60 years old or older and community-dwelling and had a nonfocal neurologic examination; a Mini-Mental State Examination [19] score of 25 or higher; no history of neurologic illness, including clinically significant head trauma or CNS infection; and no evidence of a psychiatric disorder or history of addictions on the NIMH Diagnostic Interview Schedule (DIS) [20]. Exclusion criteria, in addition, included any contraindication to MRI.

All study procedures were explained to each participant, and those who provided written informed consent were enrolled. This study was approved by the Duke University Health System Institutional Review Board.

Comorbid hypertension was assessed through subject self-report. This assessment was derived from the NIMH Epidemiological Catchment Area Study [21].

MRI Acquisition
Subjects were imaged with a 1.5-T whole-body MRI system (Signa, GE Healthcare) using a standard head (volumetric) radiofrequency coil. The scanner alignment light was used to adjust the head tilt and rotation so that the axial plane lights passed across the canthomeatal line and the sagittal lights were aligned with the center of the nose. A rapid sagittal localizer scan was acquired to confirm alignment.

A set of dual-echo fast spin-echo acquisitions were obtained in the axial plane for morphometry. The pulse sequence parameters were a TR/TE of 4,000/135, 32-kHz (± 16 kHz) full imaging bandwidth, echo-train length of 16, a 256 x 256 matrix, 3-mm section thickness, 1 excitation, and a 20-cm field of view. The images were acquired in two separate acquisitions with a 3-mm gap between sections for each acquisition. The second acquisition was offset by 3 mm from the first so that the resulting data set consisted of contiguous sections.

The diffusion tensor images were acquired using a 2D echo-planar pulse sequence (TR/TE, 12,000/109). Following previously published directional schemes [22], images were acquired in each of six diffusion-weighted directions with a b value of 1,000 s/mm² and one additional image with a b value of 0 s/mm². The other sequence parameters included a 24 x 24 cm field of view, 90-kHz bandwidth, 1 excitation, 128 x 128 matrix, and a 5-mm slice thickness with a 2.5-mm slice gap.

Lesion and Brain Volume Measurements
Tissue segmentation and brain volume measurements were performed using a modified version of MRX software, created by GE Corporate Research and Development and originally modified by Brigham and Women's Hospital for image segmentation. This previously described semiautomated method [23] used the multiple MR contrasts available to identify different tissue classifications through a "seeding" process wherein a trained analyst manually selected pixels in each type of tissue—such as gray matter, white matter, CSF, lesions, or background—that was to be identified. The nonbrain tissue was stripped away through a masking procedure and the cerebral hemispheres were traced. Cerebrum was used as a proxy for total brain volume and did not include the brainstem or cerebellum.

Lesion areas were selected on the basis of a set of explicit rules [23]. These rules were developed from neuroanatomic guidelines, consultation with a neuroradiologist, and knowledge of the neuropathology of lesions. White matter lesions could occur in the deep white matter or could be periventricular; both were classified as white matter lesions on the segmented image. Subcortical gray matter lesions were defined as lesions within the caudate nucleus, putamen, globus pallidus, or thalamus. Regions likely to be the result of overlap with CSF rather than lesions were excluded. A summarizing program calculated the volume of each tissue type within each cerebral hemisphere by adding the number of voxels in each region and multiplying by the known voxel volume.

To acquire measures of white matter lesion volume in the anterior half of the brain, further methods were required [24]. First, an axial plane was created along the anterior commissure–posterior commissure line. Next, coronal planes were created perpendicular to the axial plane at the anterior and posterior extents of the corpus callosum. Finally, a third coronal plane was created at the midpoint between the first two coronal planes, dividing the brain into anterior and posterior halves. An example of placement of these planes is shown in Figure 1.

White matter lesion volume within each region was calculated. For these calculations, the anterior half of the brain included the frontal lobe, anterior portion of the temporal lobes (including amygdala and hippocampus), striatum, and thalamus. The posterior half of the brain included the parietal and occipital lobes and the posterior portion of the temporal lobes; in some individuals, posterior portions of the frontal lobe were also included in the posterior half of the brain. The white matter lesion volume in the anterior half was calculated and used for the current study. When compared with the actual midpoint as measured by examining the maximal anteroposterior distance, our method resulted in the posterior half being 55% of this difference and the anterior half being 45% of this difference.

View larger version (93K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 2A —Placement of regions of interest (ROIs). (Reprinted with permission from the Society of Biological Psychiatry: Bae JN, MacFall JR, Krishnan KR, Payne ME, Steffens DC, Taylor WD. Dorsolateral prefrontal cortex and anterior cingulate cortex white matter alterations in late-life depression. Biol Psychiatry 2006; 60:1356–1363 [18]) ROIs in dorsolateral prefrontal cortex are shown for fractional anisotropy (FA) image (A), apparent diffusion coefficient (ADC) image (B), and T2-weighted image (C).

View larger version (103K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 2B —Placement of regions of interest (ROIs). (Reprinted with permission from the Society of Biological Psychiatry: Bae JN, MacFall JR, Krishnan KR, Payne ME, Steffens DC, Taylor WD. Dorsolateral prefrontal cortex and anterior cingulate cortex white matter alterations in late-life depression. Biol Psychiatry 2006; 60:1356–1363 [18]) ROIs in dorsolateral prefrontal cortex are shown for fractional anisotropy (FA) image (A), apparent diffusion coefficient (ADC) image (B), and T2-weighted image (C).

View larger version (97K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 2C —Placement of regions of interest (ROIs). (Reprinted with permission from the Society of Biological Psychiatry: Bae JN, MacFall JR, Krishnan KR, Payne ME, Steffens DC, Taylor WD. Dorsolateral prefrontal cortex and anterior cingulate cortex white matter alterations in late-life depression. Biol Psychiatry 2006; 60:1356–1363 [18]) ROIs in dorsolateral prefrontal cortex are shown for fractional anisotropy (FA) image (A), apparent diffusion coefficient (ADC) image (B), and T2-weighted image (C).

View larger version (103K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 2D —Placement of regions of interest (ROIs). (Reprinted with permission from the Society of Biological Psychiatry: Bae JN, MacFall JR, Krishnan KR, Payne ME, Steffens DC, Taylor WD. Dorsolateral prefrontal cortex and anterior cingulate cortex white matter alterations in late-life depression. Biol Psychiatry 2006; 60:1356–1363 [18]) ROIs in corpus callosum are shown for FA (D), ADC (E), and T2-weighted (F) images.

View larger version (93K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 2E —Placement of regions of interest (ROIs). (Reprinted with permission from the Society of Biological Psychiatry: Bae JN, MacFall JR, Krishnan KR, Payne ME, Steffens DC, Taylor WD. Dorsolateral prefrontal cortex and anterior cingulate cortex white matter alterations in late-life depression. Biol Psychiatry 2006; 60:1356–1363 [18]) ROIs in corpus callosum are shown for FA (D), ADC (E), and T2-weighted (F) images.

View larger version (94K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 2F —Placement of regions of interest (ROIs). (Reprinted with permission from the Society of Biological Psychiatry: Bae JN, MacFall JR, Krishnan KR, Payne ME, Steffens DC, Taylor WD. Dorsolateral prefrontal cortex and anterior cingulate cortex white matter alterations in late-life depression. Biol Psychiatry 2006; 60:1356–1363 [18]) ROIs in corpus callosum are shown for FA (D), ADC (E), and T2-weighted (F) images.

As previously described [18, 26], oval ROIs were used to measure the FA and ADC of each structure of interest; the same ROI was used for both measures (Fig. 2A, 2B, 2C, 2D, 2E, 2F, 2G, 2H, 2I). All ROIs were placed by a single analyst. ROIs were placed in each hemisphere on the axial slice and were placed on the ADC image, using the b = 0 s/mm² images and FA images to guide placement to ensure the ROIs were in the white matter and avoided gray matter and CSF. All ROIs were the same size (45.7 mm²) except for those measuring the internal capsule, which were 59.8 mm².

View larger version (103K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 2G —Placement of regions of interest (ROIs). (Reprinted with permission from the Society of Biological Psychiatry: Bae JN, MacFall JR, Krishnan KR, Payne ME, Steffens DC, Taylor WD. Dorsolateral prefrontal cortex and anterior cingulate cortex white matter alterations in late-life depression. Biol Psychiatry 2006; 60:1356–1363 [18]) ROIs in anterior cingulate cortex and internal capsule are shown for FA (G), ADC (H), and T2-weighted (I) images.

View larger version (99K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 2H —Placement of regions of interest (ROIs). (Reprinted with permission from the Society of Biological Psychiatry: Bae JN, MacFall JR, Krishnan KR, Payne ME, Steffens DC, Taylor WD. Dorsolateral prefrontal cortex and anterior cingulate cortex white matter alterations in late-life depression. Biol Psychiatry 2006; 60:1356–1363 [18]) ROIs in anterior cingulate cortex and internal capsule are shown for FA (G), ADC (H), and T2-weighted (I) images.

View larger version (99K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 2I —Placement of regions of interest (ROIs). (Reprinted with permission from the Society of Biological Psychiatry: Bae JN, MacFall JR, Krishnan KR, Payne ME, Steffens DC, Taylor WD. Dorsolateral prefrontal cortex and anterior cingulate cortex white matter alterations in late-life depression. Biol Psychiatry 2006; 60:1356–1363 [18]) ROIs in anterior cingulate cortex and internal capsule are shown for FA (G), ADC (H), and T2-weighted (I) images.

The superior and middle frontal gyri ROIs, used to assess the dorsolateral prefrontal cortex, were placed on the most inferior slice where both gyri were visible as separate structures in the white matter of the gyri, halfway between the precentral sulcus and the anterior boundary of the brain.

The anterior cingulate cortex (ACC) ROIs were placed on the most inferior slice where the anterior horns of the lateral ventricle were still visible; the ROIs were placed in each hemisphere in the white matter just lateral to the cingulate gyrus. This same slice was used for placement of the internal capsule ROIs, which were placed in the anterior limb just posterior to its bifurcation with the external capsule.

Finally, ROIs for the corpus callosum were placed on the slice ventral to the slice where it is divided by the longitudinal fissure; an ROI was placed in each hemisphere adjacent to the midline, and then the results were averaged to provide a composite measure.

Hyperintense lesions were avoided in all DTI ROI placements except in the internal capsule where any lesions present were included. Given the ROI placement protocol and the relative size of the anterior limb of the internal capsule, it was not possible to avoid lesions when present in the anterior limb of the internal capsule and still acquire the measures. Nine of the 82 subjects had lesions in this ROI; removal of these subjects from the data set did not substantively alter the results.

Training and Reliability
For both DTI and volumetric measures, reliability was established by repeated measurements on multiple MR scans. For DTI, the same ROI was used for measures of both FA and ADC. Intraclass correlation coefficients (ICCs) of FA ROIs were as follows: left corpus callosum, 0.971; right corpus callosum, 0.934; left internal capsule, 0.996; right internal capsule, 0.908; left ACC, 0.910; right ACC, 0.992; left superior frontal gyrus, 0.991; right superior frontal gyrus, 0.985; left middle frontal gyrus, 0.975; and right middle frontal gyrus, 0.983.

The ICCs of ADC ROIs were as follows: left corpus callosum, 0.751; right corpus callosum, 0.799; left internal capsule, 0.934; right internal capsule, 0.949; left ACC, 0.945; right ACC, 0.957; left superior frontal gyrus, 0.978; right superior frontal gyrus, 0.987; left middle frontal gyrus, 0.931; and right middle frontal gyrus, 0.893.

The ICCs for lesion volumes were as follows: left cerebral gray matter lesions, 0.995; right cerebral gray matter lesions, 0.996; left cerebral white matter lesions, 0.988; and right cerebral white matter lesions, 0.994. The ICC for the total cerebrum volume was 0.998.

Statistical Analysis
Because we did not hypothesize that there would be any difference in results between the two hemispheres, the left and right hemisphere values for each DTI measure were combined to create an average value. Moreover, to reduce the number of comparisons, measures of the middle and superior frontal gyri were averaged to create a composite dorsolateral prefrontal cortex measure.

SAS software (version 8.2, SAS Institute) was used for all statistical analyses. General linear models examined first FA values then ADC values as the dependent variables, whereas patient age, patient sex, and total cerebral volume were independent variables. Age and sex have been shown to be associated with differences in anisotropy [27–29], and we have previously shown an association between total cerebral volume and some anisotropy measures [18]. White matter lesions and gray matter lesions were examined in separate models rather than the same model because these measures were highly correlated (Pearson's correlation coefficient = 0.51, p < 0.0001).

Because of the large number of comparisons, a Bonferroni correction was applied to the results. There were four regions, each measuring both FA and ADC, which were analyzed in two models (Table 1). These 16 comparisons result in an adjusted alpha of 0.0031.

As a secondary set of analyses, we incorporated the presence or absence of hypertension into the models. Hypertension was examined because we have previously associated its presence with greater hyperintense lesion volumes [15]. Other vascular risk factors such as diabetes were not included because of the small number of subjects reporting those illnesses (data not shown). The level of statistical significance was not further modified beyond the 0.0031 alpha for these exploratory analyses.

Results

Top Abstract Introduction Materials and Methods Results Discussion References

 
The sample consisted of 82 individuals. The mean age was 71.6 years (SD = 6.0 years), with a range of 60–85 years. Fifty-seven (69.5%) subjects were women. The group had a mean total white matter lesion volume of 6.5 mL (SD = 9.4 mL), with a mean anterior white matter lesion volume of 3.3 mL (SD = 3.5 mL) and a mean gray matter lesion volume of 0.21 mL (SD = 0.29 mL). Twenty-two (26.8%) subjects reported hypertension, whereas only six (7.3%) reported diabetes mellitus and seven (8.5%) reported heart disease. Unadjusted mean DTI measures for each region are displayed in Table 1.

The relationship between lesion volumes and both FA and ADC are detailed in Table 1; graphs display the relationship between DTI measures and anterior white matter lesion volume in Figure 3A, 3B, 3C, 3D, 3E, 3F, 3G, 3H and gray matter lesion volume in Figure 4A, 4B, 4C, 4D, 4E, 4F, 4G, 4H. All significant results for models examining FA exhibited negative associations: Increased lesion volumes were associated with lower FA values. Significant associations between ADC and lesion volumes were positive: ADC values increased with greater lesion volumes.

View larger version (9K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 3A —Graphs show regional diffusion tensor imaging measures by anterior white matter (WM) lesion volume. ADC = apparent diffusion coefficient. Fractional anisotropy (FA) does not have units; because of space considerations, y-axis points are presented without decimal (e.g., 0.500 = 500 on graph).

View larger version (9K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 3B —Graphs show regional diffusion tensor imaging measures by anterior white matter (WM) lesion volume. ADC = apparent diffusion coefficient. Fractional anisotropy (FA) does not have units; because of space considerations, y-axis points are presented without decimal (e.g., 0.500 = 500 on graph).

View larger version (9K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 3C —Graphs show regional diffusion tensor imaging measures by anterior white matter (WM) lesion volume. ADC = apparent diffusion coefficient. Fractional anisotropy (FA) does not have units; because of space considerations, y-axis points are presented without decimal (e.g., 0.500 = 500 on graph).

View larger version (9K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 3D —Graphs show regional diffusion tensor imaging measures by anterior white matter (WM) lesion volume. ADC = apparent diffusion coefficient. Fractional anisotropy (FA) does not have units; because of space considerations, y-axis points are presented without decimal (e.g., 0.500 = 500 on graph).

View larger version (8K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 3E —Graphs show regional diffusion tensor imaging measures by anterior white matter (WM) lesion volume. ADC = apparent diffusion coefficient. Fractional anisotropy (FA) does not have units; because of space considerations, y-axis points are presented without decimal (e.g., 0.500 = 500 on graph).

View larger version (8K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 3F —Graphs show regional diffusion tensor imaging measures by anterior white matter (WM) lesion volume. ADC = apparent diffusion coefficient. Fractional anisotropy (FA) does not have units; because of space considerations, y-axis points are presented without decimal (e.g., 0.500 = 500 on graph).

View larger version (8K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 3G —Graphs show regional diffusion tensor imaging measures by anterior white matter (WM) lesion volume. ADC = apparent diffusion coefficient. Fractional anisotropy (FA) does not have units; because of space considerations, y-axis points are presented without decimal (e.g., 0.500 = 500 on graph).

View larger version (9K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 3H —Graphs show regional diffusion tensor imaging measures by anterior white matter (WM) lesion volume. ADC = apparent diffusion coefficient. Fractional anisotropy (FA) does not have units; because of space considerations, y-axis points are presented without decimal (e.g., 0.500 = 500 on graph).

View larger version (10K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 4A —Graphs show regional diffusion tensor imaging measures by anterior gray matter (GM) lesion volume. ADC = apparent diffusion coefficient. Fractional anisotropy (FA) does not have units; because of space considerations, y-axis points are presented without decimal (e.g., 0.500 = 500 on graph).

View larger version (10K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 4B —Graphs show regional diffusion tensor imaging measures by anterior gray matter (GM) lesion volume. ADC = apparent diffusion coefficient. Fractional anisotropy (FA) does not have units; because of space considerations, y-axis points are presented without decimal (e.g., 0.500 = 500 on graph).

View larger version (10K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 4C —Graphs show regional diffusion tensor imaging measures by anterior gray matter (GM) lesion volume. ADC = apparent diffusion coefficient. Fractional anisotropy (FA) does not have units; because of space considerations, y-axis points are presented without decimal (e.g., 0.500 = 500 on graph).

View larger version (10K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 4D —Graphs show regional diffusion tensor imaging measures by anterior gray matter (GM) lesion volume. ADC = apparent diffusion coefficient. Fractional anisotropy (FA) does not have units; because of space considerations, y-axis points are presented without decimal (e.g., 0.500 = 500 on graph).

View larger version (9K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 4E —Graphs show regional diffusion tensor imaging measures by anterior gray matter (GM) lesion volume. ADC = apparent diffusion coefficient. Fractional anisotropy (FA) does not have units; because of space considerations, y-axis points are presented without decimal (e.g., 0.500 = 500 on graph).

View larger version (9K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 4F —Graphs show regional diffusion tensor imaging measures by anterior gray matter (GM) lesion volume. ADC = apparent diffusion coefficient. Fractional anisotropy (FA) does not have units; because of space considerations, y-axis points are presented without decimal (e.g., 0.500 = 500 on graph).

View larger version (9K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 4G —Graphs show regional diffusion tensor imaging measures by anterior gray matter (GM) lesion volume. ADC = apparent diffusion coefficient. Fractional anisotropy (FA) does not have units; because of space considerations, y-axis points are presented without decimal (e.g., 0.500 = 500 on graph).

View larger version (10K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 4H —Graphs show regional diffusion tensor imaging measures by anterior gray matter (GM) lesion volume. ADC = apparent diffusion coefficient. Fractional anisotropy (FA) does not have units; because of space considerations, y-axis points are presented without decimal (e.g., 0.500 = 500 on graph).

On examination of the graphs, it appeared there were a small number of outliers with higher anterior white matter lesion volume or gray matter lesion volume than was seen in most of the sample. To determine whether they were contributing to the majority of our results, we removed subjects with an anterior white matter lesion volume of 15 mL or greater or a gray matter lesion volume of 1.0 mL or greater. The data for five subjects were thus removed, for an adjusted sample size of 77 individuals.

In the analyses in which the outliers were removed, most comparisons that were originally significant after the Bonferroni correction remained significant at that adjusted alpha. Thus, white matter lesion volume remained significantly associated with internal capsule and dorsolateral prefrontal cortex ADC and with dorsolateral prefrontal cortex FA. However, the relationship between white matter lesion volume and ACC ADC values was no longer statistically significant at the adjusted alpha (F_1,76 = 3.98, p = 0.0500). Likewise, after adjustment for multiple comparisons, gray matter lesion volume continued to be significantly associated with the ADC of the internal capsule, but not the corpus callosum (F_1,76 = 5.10, p = 0.0270).

The exploratory analyses, wherein we also included presence or absence of hypertension as a dependent variable, did not substantively change the findings between lesion volumes and DTI measures reported in Table 1. In no model was hypertension associated with a DTI measure at the adjusted significance level.

Discussion

Top Abstract Introduction Materials and Methods Results Discussion References

 
The primary finding of this study is that increased severity of both gray matter lesions and white matter lesions is associated with widespread changes to white matter structure beyond the bounds of hyperintense lesions that are detected on conventional MRI. The presence of hypertension did not mediate this effect. A similar relationship between lesion volumes and DTI measures of lesion volumes has been previously reported for multiple sclerosis [30], but to our knowledge this report is the first to examine this issue in hyperintense lesions seen in an older population.

White matter lesions, defined for this study as occurring in either the deep white matter or the periventricular white matter, are associated with changes in white matter structure in regions distant to the visible boundaries of the lesions. White matter lesion volume was associated with lower FA and higher ADC values of the dorsolateral prefrontal cortex white matter and with higher ADC values of the internal capsule and white matter of the ACC, although the association with the ACC white matter does not reach the adjusted significance level when the outliers are removed.

Subcortical gray matter lesions were primarily associated with the ADC of the internal capsule and the genu of the corpus callosum. Given the proximity and association of these gray matter regions with these white matter structures, this finding is not surprising. However, there also appears to be an association between gray matter lesion volume and ADC of the ACC white matter. Although this does not reach the adjusted level of statistical significance, this finding is intriguing because it supports the theory that frontal subcortical circuits [31] are particularly vulnerable to aging [27]. The relationship between gray matter lesions and ACC white matter DTI measures should be studied further to test for a further relationship with mood or cognitive symptoms.

When interpreting these results, it is important to consider these hyperintense lesions may have a number of different causes. Hyperintense lesions in older individuals exhibit findings on DTI similar to the DTI changes seen in chronic stroke [8], and larger lesions tend to be ischemic in origin [32]. However, small lesions may be due to perivascular dilatation [32]. There may be other causes as well, including demyelination, past trauma, or past neurologic infections, although our entry criteria should have reduced the chances of these causes contributing to our findings.

The clinical implications of these findings are not clear and need further examination. We have approached this study with the theory that white matter lesions and gray matter lesions disrupt broader neural circuits and produce changes distant to the lesions themselves. However, our methods do not allow us to fully test this theory and will be dependent on more sophisticated methods, such as DTI-based fiber tract mapping [33–35]. Other interpretations of our findings are also possible, such as that we are detecting white matter changes that are unrelated to currently visible lesions, but the DTI changes are themselves secondary to cerebral ischemia but not yet at a level of severity to allow visualization on T2-weighted or other high-resolution images. Regardless, the changes in white matter are broader than can be seen on conventional neuroimaging. More work is needed to determine how these changes may progress over time and how they may be related to relevant clinic outcomes.

In this study, we found more widespread relationships between lesions and ADC than between lesions and FA. The ADC is proportional to the mean diffusion and, like FA, is dependent on the strength of the diffusion. However, unlike FA, it does not reflect the directional restriction or organization of water diffusion. Thus, ADC may serve as a surrogate for white matter fiber density [36], whereas FA reflects fiber orientation and organization. In the current study, the cause of this relationship may have a stronger effect on fiber density, but the effect on fiber organization is more limited.

One limitation of our study is poor reliability scores for the ADC values of the corpus callosum measure; this should be considered in the context that the same ROI was used to measure both corpus callosum ADC and FA values, with the FA values having good reliability. This suggests there may be more variability in callosal ADC values than we see in anisotropy or other regional measures. This lower variability in callosal anisotropy may explain why we did not identify a significant relationship between corpus callosum FA values and white matter lesion volumes. Despite the corpus callosum's role in connecting neocortical fibers from homologous regions of the cerebrum, it may be that enough undamaged fibers persist despite widespread hyperintensities; these healthy fibers would likely continue to be highly organized, resulting in continued high anisotropy.

Another limitation of this study includes limits in the acquisition parameters. These methods would benefit from acquisition parameters with improved signal-to-noise ratios, acquisitions with thinner slices, and the use of alternative sequences such as FLAIR to assess hyperintense lesions. In addition, the ROI approach does not assess whole-brain white matter changes. Our DTI method was limited to the frontal lobe and central white matter structures; we cannot draw conclusions about the relationship between hyperintense lesions and DTI measures of the white matter of other lobes. Other approaches, such as using voxel-based methods of image analysis, could help address this issue, but such approaches are not without their own difficulties [37].

Finally, our method of assessing lesion volumes does not adhere to established regional boundaries and does not allow us to discretely localize lesion volumes in particular lobes or regions. However, our parcellation method does include only the majority of the frontal lobe and anterior portions of the temporal lobe, so it is superior to whole-brain measures of lesion volume. The application of newer methods based on established anatomy [38] could help address this issue in the future.

A limitation related to the clinical assessment is the evaluation of hypertension, which was assessed only by subject self-report. Given that hypertension may be asymptomatic, it is possible individuals had undetected hypertension. Future studies should include objective measures of blood pressure to assess true prevalence and severity.

In summary, using an ROI-based approach, we found widespread associations between anterior white matter lesion volume and DTI measures. Gray matter lesion volumes were primarily associated with ADC values of central white matter structures. Further work is needed to examine how lesions in specific regions may be associated with regional DTI changes and how change in these measures over time may be related in normal aging or critical clinical outcomes. Future studies could benefit from technologic advances, such as using DTI-based fiber mapping techniques [35] to identify specific fiber tracts and to correlate lesion volumes along those tracts with changes in diffusion measures and functions of the brain regions connected by those tracts.

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