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Associations of White Matter Microstructure with Clinical and Demographic Characteristics in Heavy Drinkers.

Monnig MA, Yeo RA, Tonigan JS, McCrady BS, Thoma RJ, Sabbineni A, Hutchison KE - PLoS ONE (2015)

Bottom Line: A common white matter factor was created from fractional anisotropy (FA) values of five white matter tracts: body of corpus callosum, fornix, external capsule, superior longitudinal fasciculus, and cingulate gyrus.The effect of drinking frequency differed significantly for men and women, such that higher drinking frequency was linked to lower white matter factor scores in women but not in men.In conclusion, alcohol problem severity was a significant predictor of lower white matter FA in heavy drinkers, after controlling for duration of alcohol exposure.

View Article: PubMed Central - PubMed

Affiliation: Center for Alcohol and Addiction Studies, Brown University, Providence, Rhode Island, United States of America.

ABSTRACT
Damage to the brain's white matter is a signature injury of alcohol use disorders (AUDs), yet understanding of risks associated with clinical and demographic characteristics is incomplete. This study investigated alcohol problem severity, recent drinking behavior, and demographic factors in relation to white matter microstructure in heavy drinkers. Magnetic resonance imaging (MRI) scans, including diffusion tensor imaging (DTI), were collected from 324 participants (mean age = 30.9 ± 9.1 years; 30% female) who reported five or more heavy drinking episodes in the past 30 days. Drinking history and alcohol problem severity were assessed. A common white matter factor was created from fractional anisotropy (FA) values of five white matter tracts: body of corpus callosum, fornix, external capsule, superior longitudinal fasciculus, and cingulate gyrus. Previous research has implicated these tracts in heavy drinking. Structural equation modeling (SEM) analyses tested the hypothesis that, after controlling for duration of alcohol exposure, clinical and behavioral measures of alcohol use severity would be associated with lower white matter factor scores. Potential interactions with smoking status, gender, age, treatment-seeking status, and depression or anxiety symptoms also were tested. Controlling for number of years drinking, greater alcohol problem severity and recent drinking frequency were significantly associated with lower white matter factor scores. The effect of drinking frequency differed significantly for men and women, such that higher drinking frequency was linked to lower white matter factor scores in women but not in men. In conclusion, alcohol problem severity was a significant predictor of lower white matter FA in heavy drinkers, after controlling for duration of alcohol exposure. In addition, more frequent drinking contributed to lower FA in women but not men, suggesting gender-specific vulnerability to alcohol neurotoxicity.

No MeSH data available.


Related in: MedlinePlus

Tracts included in the White Matter Factor (WMF), shown on FSL’s FMRIB58 FA image.Color legend: external capsule, red; fornix, light blue; body of corpus callosum, yellow; cingulate gyrus, dark blue; superior longitudinal fasciculus, green.
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pone.0142042.g002: Tracts included in the White Matter Factor (WMF), shown on FSL’s FMRIB58 FA image.Color legend: external capsule, red; fornix, light blue; body of corpus callosum, yellow; cingulate gyrus, dark blue; superior longitudinal fasciculus, green.

Mentions: MRI scans were obtained on 3T Siemens Trio scanner. Localizer scans were acquired with an echo-planar, gradient-echo, pulse sequence (TR = 2000 ms, TE = 29 ms, flip angle = 75°) with a 12-channel head coil, parallel to the ventral surface of the participant’s orbitofrontal cortex. Each volume consisted of 33 axial slices (64x64 matrix, 3.75x3.75 mm2, 3.5 mm thickness, 1 mm gap). High-resolution T1-weighted MP-RAGE anatomical scans were acquired with TR = 2530 ms, TE = 1.64 ms, flip angle = 7°, 192 sagittal slices, 256x256 matrix, slice thickness = 1 mm). DTI scans were acquired with single-shot, spin-echo, echo-planar imaging along the AC/PC line with FOV = 256x256 mm, 128x128 matrix, slice thickness = 2mm (isotropic 2 mm resolution), NEX = 1, TE = 84 ms, and TR = 9000 ms. A multiple-channel radiofrequency channel coil was used with GRAPPA(X2), 30 gradient directions, b = 800 s/mm2, and b = 0 repeated 5 times. DTI preprocessing steps included quality check to exclude volumes with scanner noise, signal dropout, or excessive motion; motion eddy current correction; and adjustment of diffusion gradient directions. A DTI volume was excluded if the motion was more than 4 mm of root mean square displacement, and a subject’s dataset was excluded if more than 10% of gradient directions were dropped for any reason. All images were registered to a b = 0 s/mm2 image using 12 degrees of freedom, affine transformation with mutual information cost function. Image registration and transformation steps were performed with the FMRIB Software Library (FSL) Linear Image Registration Tool. Outlier detection and data pruning were done with a custom program written in IDL (www.ittvis.com). Dtifit was used to calculate diffusion tensor and FA maps. FA values were obtained using FMRIB’s tract-based spatial statistics [35]. FA images were aligned to the standard-space FMRIB58 FA image (1x1x1 mm) using FMRIB’s Nonlinear Image Registration Tool. After transformation to the target and affine transformation to MNI152 space, all FA images were merged into a single 4D image file from which the FA skeleton was calculated using a threshold value of 0.2. White matter tracts were defined using the Johns Hopkins University International Consortium for Brain Mapping (JHU-ICBM) DTI-81 atlas with highest probability thresholding at 25%. An FA value for each tract was obtained by averaging over voxels of the white matter skeleton located within that tract. FA values for the entire sample are shown in Table 2 and according to subgroup in S1 Table. Fig 2 shows the skeletonized white matter tracts included in WMF.


Associations of White Matter Microstructure with Clinical and Demographic Characteristics in Heavy Drinkers.

Monnig MA, Yeo RA, Tonigan JS, McCrady BS, Thoma RJ, Sabbineni A, Hutchison KE - PLoS ONE (2015)

Tracts included in the White Matter Factor (WMF), shown on FSL’s FMRIB58 FA image.Color legend: external capsule, red; fornix, light blue; body of corpus callosum, yellow; cingulate gyrus, dark blue; superior longitudinal fasciculus, green.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4631485&req=5

pone.0142042.g002: Tracts included in the White Matter Factor (WMF), shown on FSL’s FMRIB58 FA image.Color legend: external capsule, red; fornix, light blue; body of corpus callosum, yellow; cingulate gyrus, dark blue; superior longitudinal fasciculus, green.
Mentions: MRI scans were obtained on 3T Siemens Trio scanner. Localizer scans were acquired with an echo-planar, gradient-echo, pulse sequence (TR = 2000 ms, TE = 29 ms, flip angle = 75°) with a 12-channel head coil, parallel to the ventral surface of the participant’s orbitofrontal cortex. Each volume consisted of 33 axial slices (64x64 matrix, 3.75x3.75 mm2, 3.5 mm thickness, 1 mm gap). High-resolution T1-weighted MP-RAGE anatomical scans were acquired with TR = 2530 ms, TE = 1.64 ms, flip angle = 7°, 192 sagittal slices, 256x256 matrix, slice thickness = 1 mm). DTI scans were acquired with single-shot, spin-echo, echo-planar imaging along the AC/PC line with FOV = 256x256 mm, 128x128 matrix, slice thickness = 2mm (isotropic 2 mm resolution), NEX = 1, TE = 84 ms, and TR = 9000 ms. A multiple-channel radiofrequency channel coil was used with GRAPPA(X2), 30 gradient directions, b = 800 s/mm2, and b = 0 repeated 5 times. DTI preprocessing steps included quality check to exclude volumes with scanner noise, signal dropout, or excessive motion; motion eddy current correction; and adjustment of diffusion gradient directions. A DTI volume was excluded if the motion was more than 4 mm of root mean square displacement, and a subject’s dataset was excluded if more than 10% of gradient directions were dropped for any reason. All images were registered to a b = 0 s/mm2 image using 12 degrees of freedom, affine transformation with mutual information cost function. Image registration and transformation steps were performed with the FMRIB Software Library (FSL) Linear Image Registration Tool. Outlier detection and data pruning were done with a custom program written in IDL (www.ittvis.com). Dtifit was used to calculate diffusion tensor and FA maps. FA values were obtained using FMRIB’s tract-based spatial statistics [35]. FA images were aligned to the standard-space FMRIB58 FA image (1x1x1 mm) using FMRIB’s Nonlinear Image Registration Tool. After transformation to the target and affine transformation to MNI152 space, all FA images were merged into a single 4D image file from which the FA skeleton was calculated using a threshold value of 0.2. White matter tracts were defined using the Johns Hopkins University International Consortium for Brain Mapping (JHU-ICBM) DTI-81 atlas with highest probability thresholding at 25%. An FA value for each tract was obtained by averaging over voxels of the white matter skeleton located within that tract. FA values for the entire sample are shown in Table 2 and according to subgroup in S1 Table. Fig 2 shows the skeletonized white matter tracts included in WMF.

Bottom Line: A common white matter factor was created from fractional anisotropy (FA) values of five white matter tracts: body of corpus callosum, fornix, external capsule, superior longitudinal fasciculus, and cingulate gyrus.The effect of drinking frequency differed significantly for men and women, such that higher drinking frequency was linked to lower white matter factor scores in women but not in men.In conclusion, alcohol problem severity was a significant predictor of lower white matter FA in heavy drinkers, after controlling for duration of alcohol exposure.

View Article: PubMed Central - PubMed

Affiliation: Center for Alcohol and Addiction Studies, Brown University, Providence, Rhode Island, United States of America.

ABSTRACT
Damage to the brain's white matter is a signature injury of alcohol use disorders (AUDs), yet understanding of risks associated with clinical and demographic characteristics is incomplete. This study investigated alcohol problem severity, recent drinking behavior, and demographic factors in relation to white matter microstructure in heavy drinkers. Magnetic resonance imaging (MRI) scans, including diffusion tensor imaging (DTI), were collected from 324 participants (mean age = 30.9 ± 9.1 years; 30% female) who reported five or more heavy drinking episodes in the past 30 days. Drinking history and alcohol problem severity were assessed. A common white matter factor was created from fractional anisotropy (FA) values of five white matter tracts: body of corpus callosum, fornix, external capsule, superior longitudinal fasciculus, and cingulate gyrus. Previous research has implicated these tracts in heavy drinking. Structural equation modeling (SEM) analyses tested the hypothesis that, after controlling for duration of alcohol exposure, clinical and behavioral measures of alcohol use severity would be associated with lower white matter factor scores. Potential interactions with smoking status, gender, age, treatment-seeking status, and depression or anxiety symptoms also were tested. Controlling for number of years drinking, greater alcohol problem severity and recent drinking frequency were significantly associated with lower white matter factor scores. The effect of drinking frequency differed significantly for men and women, such that higher drinking frequency was linked to lower white matter factor scores in women but not in men. In conclusion, alcohol problem severity was a significant predictor of lower white matter FA in heavy drinkers, after controlling for duration of alcohol exposure. In addition, more frequent drinking contributed to lower FA in women but not men, suggesting gender-specific vulnerability to alcohol neurotoxicity.

No MeSH data available.


Related in: MedlinePlus