Figures
Abstract
Homocysteine level can lead to adverse effects on the brain white matter through endothelial dysfunction, microstructural inflammation, and neurotoxin effects. Despite previously observed associations between elevated homocysteine and macroscopic structural brain changes, it is still unknown whether microstructural associations of homocysteine on brain tissue properties can be observed in healthy subjects with routine MRI. To this end, we investigated potential relationships between homocysteine levels and microstructural measures computed with diffusion tensor imaging (DTI) in a cohort of 338 healthy participants. Significant positive correlations were observed between homocysteine levels and diffusivity measures in the bilateral temporal WM, the brainstem, and the bilateral cerebellar peduncle. This is the first study demonstrating that DTI is sufficiently sensitive to relate microstructural WM properties to homocysteine levels in healthy subjects.
Citation: Hsu J-L, Chen W-H, Bai C-H, Leu J-G, Hsu C-Y, Viergever MA, et al. (2015) Microstructural White Matter Tissue Characteristics Are Modulated by Homocysteine: A Diffusion Tensor Imaging Study. PLoS ONE 10(2): e0116330. https://doi.org/10.1371/journal.pone.0116330
Academic Editor: Gaolang Gong, Beijing Normal University, CHINA
Received: September 9, 2014; Accepted: December 4, 2014; Published: February 18, 2015
Copyright: © 2015 Hsu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Data Availability: All relevant data are available from Figshare under the DOI http://dx.doi.org/10.6084/m9.figshare.1275179.
Funding: This work was supported by the National Science Council (NSC 101-2221-E-038 -006). The research of A.L. is supported by VIDI Grant 639.072.411 from the Netherlands Organization for Scientific Research (NWO). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: There are no actual or potential conflicts of interest including any financial, personal, or other relationships with other people or organizations within 3 years of beginning the work submitted that could inappropriately influence (bias) their work. No author’s institution has contracts relating to this research through which it or any other organization may stand to gain financially now or in the future. There are no other agreements of authors or their institutions that could be seen as involving a financial interest in this work. The data contained in the manuscript being submitted have not been previously published, have not been submitted elsewhere and will not be submitted elsewhere.
Introduction
The blood level of homocysteine, a sulfur-containing amino acid derived from dietary methionine, is known to increase with age, hypertension, smoking, and renal failure [1–3]. Serum total homocysteine level is associated with vascular risk factors such as hypertension, current smoking, previous cardiovascular disease, which are the composition items of the Framingham stroke risk profile [4, 5]. Homocysteine level may reflect the endothelial dysfunction and is related to inflammation response [4, 6, 7]. While severe hyperhomocysteinemia is rare, mild elevation of blood homocysteine level has been reported to occur in 5–7% of the general population [8]. Previous research suggests that there is a relationship between elevated homocysteine levels and brain morphological factors derived from magnetic resonance imaging (MRI), such as brain atrophy [9], reduced white matter (WM) volume [10], and WM hyperintensity (WMH) abnormalities [11, 12]. Only a few studies did not find any relationship between homocysteine levels and MRI markers [13, 14].
While associations between elevated homocysteine levels and macroscopic structural brain changes have been found, it is still unknown whether the microstructural brain tissue properties are affected in healthy subjects. Since such microstructural relationships may provide early and sensitive indicators of tissue abnormalities before the manifestation of gross morphological changes, we performed a baseline, cross-sectional study to explore the association between homocysteine level and WM microstructural organization in a large cohort of 338 healthy adults. To this end, diffusion tensor imaging (DTI) [15–17] data were acquired from an ethnically homogenous Asian population, in which subjects were carefully screened for medical and neurological conditions minimizing sources of variation that could affect the sensitivity of our imaging findings. In particular, we investigated whether the level of homocysteine was associated with specific diffusivity properties while controlling for various vascular risk factors.
Materials and Methods
Subjects
The subjects in this study were recruited from a health-screening program in Taiwan. In total, 357 participants, aged 25–81 years, received detailed examinations, including physical and bed-side neurological examinations performed by neurologists, a biochemistry study, a chest X-ray, an electrocardiogram (EKG), and MRI scans. The blood tests included the assessment of hemoglobulin (Hb), mean cell volume, fasting glucose, creatinine, high-sensitivity CRP (hs-CRP), and total level of homocysteine. All participants were asked to fast overnight (≥ 8 hours) before blood samples were taken. The blood samples were analyzed using colorimetry by an automatic chemistry analyzer in an approved laboratory (UniCel DxC 800; Beckman Coulter). The level of homocysteine was determined with a standardized chemiluminescent microparticle immunoassay (CMIA) using the ARCHITECT i system (Abbott Laboratories, Abbott Park, IL). For each individual the body mass index (BMI) and Framingham Stroke Risk Profile (FSRP) score were also computed [5, 18]. Participants who had a history of major neurological diseases, cerebrovascular accidents, or those with manifestations of stroke, psychiatric conditions, or serious cardiovascular diseases were excluded. In the end, 338 participants were included for further analysis; 19 subjects were excluded: eleven participants had an ARWMC (age-related white matter changes) score > 2 as derived from the T2-weighted and FLAIR images [19], for seven participants the FSRP was not available, and one participant had an abnormally high homocysteine level of 23.5 μmol/L.
MRI scans
All participants received whole-brain MRI scans (Siemens, 1.5 T) at the same day of the clinical examinations. First, trans-axial T2-weighted scans, FLAIR images and high-resolution sagittal T1-weighted images were acquired. Next, a whole-brain DTI scans (TR/TE = 7600/82 ms, 3 mm slice thickness without gap, slice acquisition matrix = 128 x 128 with FOV = 256 x 256 mm2, 6/8 partial Fourier, NEX = 1, 55 slices, 12 gradient directions with b-value = 1000 s/mm2, and one b = 0 s/mm2 image) were acquired axially with a fat suppression sequence [16]. The Ethics Committee and Institutional Review Board of Shin Kong Wu Ho-Su Memorial Hospital approved the study. The written informed consent was provided by the participants. Detailed overview of the scanning parameters can be found in previous literature [20].
DTI processing
The DTI based processing steps performed in this work have been described previously in detail [20, 21]. In summary, the following steps were taken:
- All DTI data sets were corrected for eddy current induced geometric distortions and subject motion [22].
- The diffusion tensor model was fitted to the data with ExploreDTI (http://www.exploredti.com)[23] using a non-linear regression method. Fractional anisotropy (FA) and the mean (MD), axial (AD) and transverse (TD) diffusion measures were subsequently computed [24]. Note that these DTI based measures have been shown to be more sensitive to tissue abnormalities than the typical visual evaluation of WM hyperintensities observed in conventional MRI data [25–27].
- A population-based DTI atlas in MNI space was constructed [20, 28] to drive the tensor based affine [29] and non-affine [30] coregistration techniques. At the final transformation step, the “preservation of principal direction” strategy was applied to reorient the diffusion tensor [31].
Statistical analysis
All data analyses described below were conducted on the 338 participants and reviewed by the medical statistician of our institution. Distributions of FSRP, CRP and ARWMC-total score were positively skewed (p < 0.0001) and thus were subjected to log10-transformation to improve normality before being used for statistical model inferences. Pairwise correlation was used to explore the relations between the various clinical measures.
For the global DTI analysis, a linear regression model was used to explore the effect of homocysteine on the mean FA, MD, AD and TD values within the brain (combine global GM and WM volume), controlling for eight covariates (i.e., age, gender, BMI, log(FSRP), log(hs-CRP), Hb, creatinine and log(ARWMC-total score)). Statistical significance was set to p-value < 0.01.
To study the effect of homocysteine in more detail in terms of brain localization (i.e., which brain regions are involved), we also performed a voxel based analysis (VBA)[34] with spatially normalized DTI datasets (using the FA, MD, AD and TD maps, voxel size: 2x2x2 mm3, and FWHM = 6 mm Gaussian smoothing) [34]. To explore the effect of homocysteine on FA, MD, AD and TD measures, multiple regression models with the abovementioned eight covariates were used. The resulting T-statistic images were subsequently thresholded for significance: regions with family-wise error (FWE) corrected p-value < 0.01 with corresponding T-values above 5.2 and with cluster sizes larger than 200 voxels (voxel size: 2x2x2 mm3) were considered significant after correction for multiple independent comparisons. The significant regions were superimposed on the standardized MNI T1-weighted image and on the main fiber bundles, reconstructed from DTI template developed by Mori et al [35].
Results
Descriptive statistics for clinical measures
Clinical and demographic characteristics of the 338 participants are shown in Table 1. In summary, the mean age of the study participants is 51.6 years and 41.4% of them were female. The mean FSRP score is 4.9%. The mean value of homocysteine is 8.6 μmol/L (range: 3.4–16.2 μmol/L) and only four participants (1.2%) have a homocysteine level above 15 μmol /L. Males have a higher homocysteine level than females (males: 9.6 ± 2.3 μmol /L v.s. females: 7.3 ± 1.9 μmol /L, p < 0.0001). The median ARWMC-total score is 0 (range: 0–8) and only twenty participants (5.8%) have an ARWMC-total score larger than 2, which reflects the beginning confluence of WM lesions at that particular brain region.
Pairwise correlations between age, gender, BMI, FSRP score, total level of homocysteine, hs-CRP, Hb, creatinine and ARWMC-total score are shown in Table 2. In summary, the level of homocysteine is positively correlated with BMI, FSRP score, Hb and creatinine.
Global analysis
In the global analysis of the DTI images, homocysteine level correlates positively with the mean brain MD, AD and TD (whole model R2 = 0.28, 0.24 and 0.29 respectively, all p-values < 0.0001) while adjusted for multiple covariates, including the vascular risk burden (measured by FSRP score) and WM lesions (measured by ARWMC-total score).
Voxel based analysis
In the DTI based VBA approach, there is no significant correlation between the level of homocysteine and FA. Five clusters, which are distributed in the bilateral frontal WM, anterior temporal WM, thalamus, mid brain, middle cerebellar peduncles, the pons, and the genus of the corpus callosum show a significant positive correlation between MD and the level of homocysteine (Fig. 1 and Table 3). In addition, in eight clusters in the bilateral frontal WM, anterior temporal WM, thalamus, mid brain, the left middle cerebellar peduncle, the pons, and the genus of corpus callosum, there is a significant positive correlation between AD and homocysteine (Fig. 2 and Table 4). Finally, homocysteine levels does not show any significant correlation with TD.
voxel-based DTI analysis showing a significant positive correlation between the level of homocysteine and MD in the bilateral cerebellar peduncles, the brainstem, the bilateral anterior temporal WM, and the genu of the corpus callosum (in red). The number indicates the z-axis coordinate in MNI space (unit in mm). R: right, L: left.
voxel-based DTI analysis showing a significant positive correlation between the level of homocysteine and the AD in the left cerebellar peduncle, the brainstem, the bilateral anterior temporal WM, and the genu of the corpus callosum (in red). The number indicates the z-axis coordinate in MNI space (unit in mm). R: right, L: left.
The main regions that showed significant correlations with MD and AD were located in four main WM fiber bundles: the inferior longitudinal fasciculus, the ponto-cerebellar tracts, the forceps minor, and the pyramidal tracts (Fig. 3).
T-value significance maps of the association between homocysteine and mean diffusivity (MD) (a-d) and axial diffusivity (AD) (e-h) were superimposed on the four major white matter tracts that were involved: the pyramidal tracts (a,e), ponto-cerebellar tracts (b,f), inferior longitudinal fasciculus (c,g), and forceps minor (d,h). Color coding from cold to hot represents the T-value derived from the significance maps.
From the DTI based VBA approach, it is evident that homocysteine shows a widespread positive correlation with MD. To investigate this finding in more detail, we calculated the mean FA, MD, AD and TD values in these regions of significant correlation. We used a linear regression model to explore the effect of homocysteine after adjusting for abovementioned eight covariates. Our results show that homocysteine is positively correlated with mean MD, AD and TD (whole-model p < 0.0001 and homocysteine effect p < 0.0001 for each parameter) (Fig. 4). The beta estimate of homocysteine in the regression model is highest for the AD (AD/MD/TD = 0.69/0.46/0.35, respectively). There is no association between the level of homocysteine and mean FA. The statistical power values for MD, AD and TD in the linear regression model are all above 95%; for the FA it is 53.8%.
in contrast with the FA, the MD, AD, and TD are positively correlated with homocysteine. The linear regression model has been adjusted for age, log-transformed FSRP, BMI, log-transformed hs-CRP, hemoglobin, creatinine, log-transformed ARWMC score, and gender. The trend slopes and significance p-values for homocysteine with respect to the DTI measures are indicated.
Discussion
In this work, we have investigated the association between homocysteine and DTI parameters in 338 healthy middle-aged participants. Compared to the more widely used morphological and volumetric analyses for studying macrostructural brain changes (based on T1-weighted MRI), we used DTI based analyses to probe the microstructural tissue properties. In our study design, we carefully controlled for numerous potentially confounding factors such as age, various vascular risk factors (including hypertension, diabetes mellitus, smoking, cardiovascular events history, and EKG features), and cerebral WMH scores.
This is the first study in humans that investigated the association between brain tissue properties derived from DTI and the level of homocysteine. To the best of our knowledge, only one animal study has been performed that has explored this relationship before. In that recent animal study, Willette et al. demonstrated that higher homocysteine levels are associated with lower WM volumes in the pons and the middle cerebellar peduncle, and that higher homocysteine levels have a positive association with the MD in the bilateral anterior cerebellum and the prefrontal regions [36]. They also showed that homocysteine has a significant negative correlation with the FA in the cerebellar WM immediately dorsal to the forth ventricle, which is related to the pontocerebellar fibers. Our findings are in line with their results inasmuch as the MD in the bilateral frontal WM, midbrain, middle cerebellar pedundles, and pons showing a significant positive correlation with the level of homocysteine. More specifically, from VBA analyses, our results demonstrate that the increased MD in the bilateral temporal WM and brain stem regions was mainly caused by an increase in the AD rather than the TD, even when adjusting for multiple vascular risk factors.
While elevated homocysteine levels have been reported in obsessive-compulsive disorder [37, 38], first-episode psychosis [39, 40], schizophrenia [41, 42] and Alzheimer’s dementia [12, 43], no direct link with microstructural properties of the fiber tracts involved has been made previously. In this work, however even within a healthy cohort population, there were significant associations between diffusion metrics and homocysteine levels for the inferior longitudinal fasciculus, the ponto-cerebellar tracts, the forceps minor, and the pyramidal tracts. As these findings represent a baseline reference, it can bring unique insight into the neural substrate of brain disorders.
The biological interpretation of why diffusivity changes are related to homocysteine is far from trivial. In general, diffusion rates are larger if the coherence of tissue organization is compromised. With previous literature indicating that (i) homocysteine can act as an excitatory neurotransmitter leading to oxidative stress, endothelial dysfunction, inflammation, and neuronal injury [44] and (ii) homocysteine is associated with axonal demyelination in periventricular and subcortical WM through the injury of oligodendrocytes [45–47], it is plausible that the observed diffusivity increases were caused by such mechanism of axonal degeneration/injury rather than demyelination [48, 49]. Although hs-CRP and homocysteine are both chronic low-grade inflammation markers [50, 51], only homocysteine showed a significant association in this work, suggesting that homocysteine would have a more specific association with regional cerebral WM microstructural properties than hs-CRP. As such, future studies may benefit from including other inflammation markers such as cytokines to further increase the specificity of regional findings.
Previous work relating macrostructural brain properties with level of homocysteine showed that homocysteine levels are higher with increased WM atrophy or with higher levels of WMH scores [9, 13, 52]. In this work, we do not find a significant association between homocysteine and WMH scores after adjusting for multiple covariates. This observation can be attributed to the fact that the subjects included in this work are relatively young compared to other investigations, and that their average level of homocysteine is relatively low.
There are several methodological considerations in this study. The first one is the cross-sectional nature of our study design. Given that we measured the level of homocysteine only at one time point, we cannot demonstrate any causal effects of homocysteine on brain structure. Secondly, we did not measure the folate or vitamin B12 levels of our subjects. Previous work suggests that these measures may modulate the association between homocysteine and structural brain properties and, therefore, should be used as covariates during statistical analysis [10, 11, 52]. However, our participants did not have any history of medical illness, and with a mean hemoglobin equal to 14 mg/dl and the mean volume of red blood cells volume equal to 90 fl, a deficiency of vitamin B12 or folate is likely to be absent for the majority of our participants. Furthermore, given that several other studies did not adjust for folate and vitamen B12 levels and still found an association between homocysteine level and structural brain properties [9, 36, 47], it is likely that homocysteine can still genuinely affect the WM microstructure [10, 53]. Finally, it should be clear that DTI does not provide direct measures of “WM integrity” (e.g., see recent reviews by [54, 55]. Although DTI is sensitive to microstructural changes, its measures are heavily affected by partial voluming [31, 56] and “crossing fibers” [57–59], which makes it difficult to interpret the results in an unambiguous way.
Conclusions
We have shown that DTI is sufficiently sensitive to detect associations between homocysteine levels and diffusivity metrics in healthy subjects. Specifically, significant positive correlations were observed between homocysteine levels and diffusivity measures in the bilateral temporal WM, the brainstem, and the bilateral cerebellar peduncle. This is the first study demonstrating that microstructural WM properties are related to homocysteine levels in healthy human subjects.
Author Contributions
Conceived and designed the experiments: AL CHB JLH. Performed the experiments: MAV CYH JLH. Analyzed the data: CHB JLH. Contributed reagents/materials/analysis tools: WHC CHB JGL CYH. Wrote the paper: JLH.
References
- 1. Blom HJ, Smulders Y (2011) Overview of homocysteine and folate metabolism. With special references to cardiovascular disease and neural tube defects. Journal of inherited metabolic disease 34: 75–81. pmid:20814827
- 2. Newton LA, Sandhu K, Livingstone C, Leslie R, Davis J (2010) Clinical diagnostics for homocysteine: a rogue amino acid? Expert review of molecular diagnostics 10: 489–500. pmid:20465503
- 3.
Harold P, Adams J (2005) Handbook of Cerebrovascular Diseases. New York: Marcel Dekker.
- 4. Pikula A, Beiser AS, DeCarli C, Himali JJ, Debette S, et al. (2012) Multiple biomarkers and risk of clinical and subclinical vascular brain injury: the Framingham Offspring Study. Circulation 125: 2100–2107. pmid:22456473
- 5. Wolf PA, D’Agostino RB, Belanger AJ, Kannel WB (1991) Probability of stroke: a risk profile from the Framingham Study. Stroke; a journal of cerebral circulation 22: 312–318. pmid:2003301
- 6. da Cunha AA, Ferreira AG, Wyse AT (2010) Increased inflammatory markers in brain and blood of rats subjected to acute homocysteine administration. Metabolic brain disease 25: 199–206. pmid:20424906
- 7. Wiseman S, Marlborough F, Doubal F, Webb DJ, Wardlaw J (2014) Blood markers of coagulation, fibrinolysis, endothelial dysfunction and inflammation in lacunar stroke versus non-lacunar stroke and non-stroke: systematic review and meta-analysis. Cerebrovascular diseases 37: 64–75. pmid:24401164
- 8. Sachdev P (2004) Homocysteine, cerebrovascular disease and brain atrophy. Journal of the neurological sciences 226: 25–29. pmid:15537514
- 9. Rajagopalan P, Hua X, Toga AW, Jack CR Jr, Weiner MW, et al. (2011) Homocysteine effects on brain volumes mapped in 732 elderly individuals. Neuroreport 22: 391–395. pmid:21512418
- 10. Feng L, Isaac V, Sim S, Ng TP, Krishnan KR, et al. (2013) Associations between elevated homocysteine, cognitive impairment, and reduced white matter volume in healthy old adults. The American journal of geriatric psychiatry: official journal of the American Association for Geriatric Psychiatry 21: 164–172. pmid:23343490
- 11. Wright CB, Paik MC, Brown TR, Stabler SP, Allen RH, et al. (2005) Total homocysteine is associated with white matter hyperintensity volume: the Northern Manhattan Study. Stroke; a journal of cerebral circulation 36: 1207–1211. pmid:15879345
- 12. Hooshmand B, Polvikoski T, Kivipelto M, Tanskanen M, Myllykangas L, et al. (2013) Plasma homocysteine, Alzheimer and cerebrovascular pathology: a population-based autopsy study. Brain: a journal of neurology 136: 2707–2716. pmid:23983028
- 13. Sachdev P, Parslow R, Salonikas C, Lux O, Wen W, et al. (2004) Homocysteine and the brain in midadult life: evidence for an increased risk of leukoaraiosis in men. Archives of neurology 61: 1369–1376. pmid:15364682
- 14. Kim SR, Choi SH, Ha CK, Park SG, Pyun HW, et al. (2009) Plasma Total Homocysteine Levels are not Associated with Medial Temporal Lobe Atrophy, but with White Matter Changes in Alzheimer’s Disease. Journal of clinical neurology 5: 85–90. pmid:19587815
- 15. Basser PJ, Mattiello J, LeBihan D (1994) MR diffusion tensor spectroscopy and imaging. Biophysical journal 66: 259–267. pmid:8130344
- 16. Jones DK, Leemans A (2011) Diffusion tensor imaging. Methods in molecular biology 711: 127–144. pmid:21279600
- 17. Tournier JD, Mori S, Leemans A (2011) Diffusion tensor imaging and beyond. Magnetic resonance in medicine: official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine 65: 1532–1556. pmid:21469191
- 18. D’Agostino RB, Wolf PA, Belanger AJ, Kannel WB (1994) Stroke risk profile: adjustment for antihypertensive medication. The Framingham Study. Stroke; a journal of cerebral circulation 25: 40–43. pmid:8266381
- 19. Wahlund LO, Barkhof F, Fazekas F, Bronge L, Augustin M, et al. (2001) A new rating scale for age-related white matter changes applicable to MRI and CT. Stroke; a journal of cerebral circulation 32: 1318–1322. pmid:11387493
- 20. Hsu JL, Van Hecke W, Bai CH, Lee CH, Tsai YF, et al. (2010) Microstructural white matter changes in normal aging: a diffusion tensor imaging study with higher-order polynomial regression models. NeuroImage 49: 32–43. pmid:19699804
- 21. Hsu JL, Leemans A, Bai CH, Lee CH, Tsai YF, et al. (2008) Gender differences and age-related white matter changes of the human brain: a diffusion tensor imaging study. NeuroImage 39: 566–577. pmid:17951075
- 22. Leemans A, Jones DK (2009) The B-matrix must be rotated when correcting for subject motion in DTI data. Magnetic resonance in medicine: official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine 61: 1336–1349. pmid:19319973
- 23.
Leemans A, Jeurissen B, Sijbers J, Jones D (2009) ExploreDTI: a graphical toolbox for processing, analyzing, and visualizing diffusion MR data. Proceedings of the 17th Annual Meeting of International Society for Magnetic Resonance in Medicine. Hawaii, USA. pp. 3536.
- 24. Pierpaoli C, Basser PJ (1996) Toward a quantitative assessment of diffusion anisotropy. Magnetic resonance in medicine: official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine 36: 893–906. pmid:8946355
- 25. Della Nave R, Foresti S, Pratesi A, Ginestroni A, Inzitari M, et al. (2007) Whole-brain histogram and voxel-based analyses of diffusion tensor imaging in patients with leukoaraiosis: correlation with motor and cognitive impairment. AJNR American journal of neuroradiology 28: 1313–1319. pmid:17698534
- 26. Maillard P, Fletcher E, Harvey D, Carmichael O, Reed B, et al. (2011) White matter hyperintensity penumbra. Stroke; a journal of cerebral circulation 42: 1917–1922. pmid:21636811
- 27. O’Sullivan M, Summers PE, Jones DK, Jarosz JM, Williams SC, et al. (2001) Normal-appearing white matter in ischemic leukoaraiosis: a diffusion tensor MRI study. Neurology 57: 2307–2310. pmid:11756617
- 28. Van Hecke W, Sijbers J, D’Agostino E, Maes F, De Backer S, et al. (2008) On the construction of an inter-subject diffusion tensor magnetic resonance atlas of the healthy human brain. NeuroImage 43: 69–80. pmid:18678261
- 29. Leemans A, Sijbers J, De Backer S, Vandervliet E, Parizel P (2005) Affine coregistration of diffusion tensor magnetic resonance images using mutual information. Lec Notes Comput Sci: 3708.
- 30. Van Hecke W, Leemans A, D’Agostino E, De Backer S, Vandervliet E, et al. (2007) Nonrigid coregistration of diffusion tensor images using a viscous fluid model and mutual information. IEEE transactions on medical imaging 26: 1598–1612. pmid:18041274
- 31. Alexander AL, Hasan KM, Lazar M, Tsuruda JS, Parker DL (2001) Analysis of partial volume effects in diffusion-tensor MRI. Magnetic resonance in medicine: official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine 45: 770–780. pmid:11323803
- 32. Smith SM (2002) Fast robust automated brain extraction. Human brain mapping 17: 143–155. pmid:12391568
- 33. Otsu N (1979) A Threshold Selection Method from Gray-Level Histograms. IEEE Transactions on Systems, Man and Cybernetics 9: 62–66. pmid:10242122
- 34. Ashburner J, Friston KJ (1999) Nonlinear spatial normalization using basis functions. Human brain mapping 7: 254–266. pmid:10408769
- 35. Mori S, Oishi K, Jiang H, Jiang L, Li X, et al. (2008) Stereotaxic white matter atlas based on diffusion tensor imaging in an ICBM template. NeuroImage 40: 570–582. pmid:18255316
- 36. Willette AA, Gallagher C, Bendlin BB, McLaren DG, Kastman EK, et al. (2012) Homocysteine, neural atrophy, and the effect of caloric restriction in rhesus monkeys. Neurobiology of aging 33: 670–680. pmid:20691506
- 37. Zarei M, Mataix-Cols D, Heyman I, Hough M, Doherty J, et al. (2011) Changes in gray matter volume and white matter microstructure in adolescents with obsessive-compulsive disorder. Biological psychiatry 70: 1083–1090. pmid:21903200
- 38. Atmaca M, Tezcan E, Kuloglu M, Kirtas O, Ustundag B (2005) Serum folate and homocysteine levels in patients with obsessive-compulsive disorder. Psychiatry and clinical neurosciences 59: 616–620. pmid:16194269
- 39. Ruef A, Curtis L, Moy G, Bessero S, Badan Ba M, et al. (2012) Magnetic resonance imaging correlates of first-episode psychosis in young adult male patients: combined analysis of grey and white matter. Journal of psychiatry & neuroscience: JPN 37: 305–312. pmid:25608023
- 40. Ayesa-Arriola R, Perez-Iglesias R, Rodriguez-Sanchez JM, Mata I, Gomez-Ruiz E, et al. (2012) Homocysteine and cognition in first-episode psychosis patients. European archives of psychiatry and clinical neuroscience 262: 557–564. pmid:22382435
- 41. Clark KA, Nuechterlein KH, Asarnow RF, Hamilton LS, Phillips OR, et al. (2011) Mean diffusivity and fractional anisotropy as indicators of disease and genetic liability to schizophrenia. Journal of psychiatric research 45: 980–988. pmid:21306734
- 42. Geller V, Friger M, Sela BA, Levine J (2013) Elevated homocysteine level in siblings of patients with schizophrenia. Psychiatry research 210: 769–772. pmid:24051177
- 43. O’Dwyer L, Lamberton F, Bokde AL, Ewers M, Faluyi YO, et al. (2011) Using diffusion tensor imaging and mixed-effects models to investigate primary and secondary white matter degeneration in Alzheimer’s disease and mild cognitive impairment. Journal of Alzheimer’s disease: JAD 26: 667–682. pmid:21694456
- 44. McCully KS (2009) Chemical pathology of homocysteine. IV. Excitotoxicity, oxidative stress, endothelial dysfunction, and inflammation. Annals of clinical and laboratory science 39: 219–232. pmid:19667406
- 45. Vermeer SE, van Dijk EJ, Koudstaal PJ, Oudkerk M, Hofman A, et al. (2002) Homocysteine, silent brain infarcts, and white matter lesions: The Rotterdam Scan Study. Annals of neurology 51: 285–289. pmid:11891822
- 46. Wong A, Mok V, Fan YH, Lam WW, Liang KS, et al. (2006) Hyperhomocysteinemia is associated with volumetric white matter change in patients with small vessel disease. Journal of neurology 253: 441–447. pmid:16267639
- 47. Ford AH, Garrido GJ, Beer C, Lautenschlager NT, Arnolda L, et al. (2012) Homocysteine, grey matter and cognitive function in adults with cardiovascular disease. PloS one 7: e33345. pmid:22413017
- 48. Song SK, Sun SW, Ramsbottom MJ, Chang C, Russell J, et al. (2002) Dysmyelination revealed through MRI as increased radial (but unchanged axial) diffusion of water. NeuroImage 17: 1429–1436. pmid:12414282
- 49. Song SK, Yoshino J, Le TQ, Lin SJ, Sun SW, et al. (2005) Demyelination increases radial diffusivity in corpus callosum of mouse brain. NeuroImage 26: 132–140. pmid:15862213
- 50. Group TS (2013) Lipid and inflammatory cardiovascular risk worsens over 3 years in youth with type 2 diabetes: the TODAY clinical trial. Diabetes care 36: 1758–1764. pmid:23704675
- 51. Moreto F, de Oliveira EP, Manda RM, Torezan GA, Teixeira O, et al. (2013) Pathological and behavioral risk factors for higher serum C-reactive protein concentrations in free-living adults—a Brazilian community-based study. Inflammation 36: 15–25. pmid:22865000
- 52. Seshadri S, Wolf PA, Beiser AS, Selhub J, Au R, et al. (2008) Association of plasma total homocysteine levels with subclinical brain injury: cerebral volumes, white matter hyperintensity, and silent brain infarcts at volumetric magnetic resonance imaging in the Framingham Offspring Study. Archives of neurology 65: 642–649. pmid:18474741
- 53. Selhub J, Jacques PF, Wilson PW, Rush D, Rosenberg IH (1993) Vitamin status and intake as primary determinants of homocysteinemia in an elderly population. JAMA: the journal of the American Medical Association 270: 2693–2698. pmid:8133587
- 54. Deprez S, Billiet T, Sunaert S, Leemans A (2013) Diffusion tensor MRI of chemotherapy-induced cognitive impairment in non-CNS cancer patients: a review. Brain imaging and behavior 7: 409–435. pmid:23329357
- 55. Jones DK, Knosche TR, Turner R (2013) White matter integrity, fiber count, and other fallacies: the do’s and don’ts of diffusion MRI. NeuroImage 73: 239–254. pmid:22846632
- 56. Vos SB, Jones DK, Viergever MA, Leemans A (2011) Partial volume effect as a hidden covariate in DTI analyses. NeuroImage 55: 1566–1576. pmid:21262366
- 57. Wheeler-Kingshott CA, Cercignani M (2009) About “axial” and “radial” diffusivities. Magnetic resonance in medicine: official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine 61: 1255–1260. pmid:19253405
- 58. Vos SB, Jones DK, Jeurissen B, Viergever MA, Leemans A (2012) The influence of complex white matter architecture on the mean diffusivity in diffusion tensor MRI of the human brain. NeuroImage 59: 2208–2216. pmid:22005591
- 59. Jeurissen B, Leemans A, Tournier JD, Jones DK, Sijbers J (2013) Investigating the prevalence of complex fiber configurations in white matter tissue with diffusion magnetic resonance imaging. Human brain mapping 34: 2747–2766. pmid:22611035