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Journal articles on the topic 'White matter diffusion'

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Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

Thomason, Moriah E., and Paul M. Thompson. "Diffusion Imaging, White Matter, and Psychopathology." Annual Review of Clinical Psychology 7, no. 1 (April 27, 2011): 63–85. http://dx.doi.org/10.1146/annurev-clinpsy-032210-104507.

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2

Nørhøj Jespersen, Sune. "White matter biomarkers from diffusion MRI." Journal of Magnetic Resonance 291 (June 2018): 127–40. http://dx.doi.org/10.1016/j.jmr.2018.03.001.

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3

Wedeen, V. J., T. L. Davis, B. E. Lautrup, T. G. Reese, and B. R. Rosen. "Diffusion anisotropy and white matter tracts." NeuroImage 3, no. 3 (June 1996): S146. http://dx.doi.org/10.1016/s1053-8119(96)80148-0.

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4

Chen, H., and Y. Xu. "Diffusion properties of major white matter tracts in individuals white matter hyperintensity." Journal of the Neurological Sciences 405 (October 2019): 75. http://dx.doi.org/10.1016/j.jns.2019.10.358.

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5

KAMAGATA, KOJI, MASAAKI HORI, KOUHEI KAMIYA, MICHIMASA SUZUKI, AKIRA NISHIKORI, FUMITAKA KUMAGAI, MARIKO YOSHIDA, SHINSUKE KYOGOKU, and SHIGEKI AOKI. "Diffusion MR Imaging of White Matter Pathways." Juntendo Medical Journal 60, no. 2 (2014): 100–106. http://dx.doi.org/10.14789/jmj.60.100.

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6

Choudhri, Asim F., Eric M. Chin, Ari M. Blitz, and Dheeraj Gandhi. "Diffusion Tensor Imaging of Cerebral White Matter." Radiologic Clinics of North America 52, no. 2 (March 2014): 413–25. http://dx.doi.org/10.1016/j.rcl.2013.11.005.

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7

van der Lei, H. D. W., M. E. Steenweg, M. Bugiani, P. J. W. Pouwels, W. N. van Wieringen, and M. S. van der Knaap. "P18.3 Restricted diffusion in vanishing white matter." European Journal of Paediatric Neurology 15 (May 2011): S105. http://dx.doi.org/10.1016/s1090-3798(11)70363-4.

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8

Lazar, Mariana, David M. Weinstein, Jay S. Tsuruda, Khader M. Hasan, Konstantinos Arfanakis, M. Elizabeth Meyerand, Benham Badie, et al. "White matter tractography using diffusion tensor deflection." Human Brain Mapping 18, no. 4 (March 6, 2003): 306–21. http://dx.doi.org/10.1002/hbm.10102.

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9

Stoeter, Peter, Paulo Roberto Dellani, and Goran Vucurevic. "Diffusion Tensor Imaging of Cerebral White Matter*." Clinical Neuroradiology 18, no. 3 (August 2008): 155–62. http://dx.doi.org/10.1007/s00062-008-8019-3.

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10

Tench, C. R., P. S. Morgan, M. Wilson, and L. D. Blumhardt. "White matter mapping using diffusion tensor MRI." Magnetic Resonance in Medicine 47, no. 5 (April 22, 2002): 967–72. http://dx.doi.org/10.1002/mrm.10144.

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11

Tuch, David S., Jonathan J. Wisco, Mark H. Khachaturian, Leeland B. Ekstrom, Rolf Kötter, and Wim Vanduffel. "Q -ball imaging of macaque white matter architecture." Philosophical Transactions of the Royal Society B: Biological Sciences 360, no. 1457 (May 29, 2005): 869–79. http://dx.doi.org/10.1098/rstb.2005.1651.

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Diffusion-weighted magnetic resonance imaging holds substantial promise as a technique for non-invasive imaging of white matter (WM) axonal projections. For diffusion imaging to be capable of providing new insight into the connectional neuroanatomy of the human brain, it will be necessary to histologically validate the technique against established tracer methods such as horseradish peroxidase and biocytin histochemistry. The macaque monkey provides an ideal model for histological validation of the diffusion imaging method due to the phylogenetic proximity between humans and macaques, the gyrencephalic structure of the macaque cortex, the large body of knowledge on the neuroanatomic connectivity of the macaque brain and the ability to use comparable magnetic resonance acquisition protocols in both species. Recently, it has been shown that high angular resolution diffusion imaging (HARDI) can resolve multiple axon orientations within an individual imaging voxel in human WM. This capability promises to boost the accuracy of tract reconstructions from diffusion imaging. If the macaque is to serve as a model for histological validation of the diffusion tractography method, it will be necessary to show that HARDI can also resolve intravoxel architecture in macaque WM. The present study therefore sought to test whether the technique can resolve intravoxel structure in macaque WM. Using a HARDI method called q -ball imaging (QBI) it was possible to resolve composite intravoxel architecture in a number of anatomic regions. QBI resolved intravoxel structure in, for example, the dorsolateral convexity, the pontine decussation, the pulvinar and temporal subcortical WM. The paper concludes by reviewing remaining challenges for the diffusion tractography project.

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12

Andreone, N., M. Tansella, R. Cerini, A. Versace, G. Rambaldelli, C. Perlini, N. Dusi, et al. "Cortical white-matter microstructure in schizophrenia." British Journal of Psychiatry 191, no. 2 (August 2007): 113–19. http://dx.doi.org/10.1192/bjp.bp.105.020990.

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BackgroundSeveral, although not all, of the previous small diffusion-weighted imaging (DWI) studies have shown cortical white-matter disruption in schizophrenia.AimsTo investigate cortical white-matter microstructure with DWI in a large community-based sample of people with schizophrenia.MethodSixty-eight people with schizophrenia and 64 healthy controls underwent a session of DWI to obtain the apparent diffusion coefficient (ADC) of white-matter water molecules. Regions of interest were placed in cortical lobes.ResultsCompared with controls, the schizophrenia group had significantly greater ADCs in frontal, temporal and occipital white matter (analysis of covariance, P < 0.05).ConclusionsOur findings confirm the presence of cortical white-matter microstructure disruption in frontal and temporo-occipital lobes in the largest sample of people with schizophrenia thus for studied with this technique. Future brain imaging studies, together with genetic investigations, should further explore white-matter integrity and genes encoding myelin-related protein expression in people with first-episode schizophrenia and those at high risk of developing the disorder.

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13

Hu, An-Ming, Yan-Ling Ma, Yue-Xiu Li, Zai-Zhu Han, Nan Yan, and Yu-Mei Zhang. "Association between Changes in White Matter Microstructure and Cognitive Impairment in White Matter Lesions." Brain Sciences 12, no. 4 (April 7, 2022): 482. http://dx.doi.org/10.3390/brainsci12040482.

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This study investigated the characteristics of cognitive impairment in patients with white matter lesions (WMLs) caused by cerebral small vessel disease and the corresponding changes in WM microstructures. Diffusion tensor imaging (DTI) data of 50 patients with WMLs and 37 healthy controls were collected. Patients were divided into vascular cognitive impairment non-dementia and vascular dementia groups. Tract-based spatial statistics showed that patients with WMLs had significantly lower fractional anisotropy (FA) and higher mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) values throughout the WM areas but predominately in the forceps minor, forceps major (FMA), bilateral corticospinal tract, inferior fronto-occipital fasciculus, superior longitudinal fasciculus, inferior longitudinal fasciculus (ILF), and anterior thalamic radiation, compared to the control group. These fiber bundles were selected as regions of interest. There were significant differences in the FA, MD, AD, and RD values (p < 0.05) between groups. The DTI metrics of all fiber bundles significantly correlated with the Montreal Cognitive Assessment (p < 0.05), with the exception of the AD values of the FMA and ILF. Patients with WMLs showed changes in diffusion parameters in the main WM fiber bundles. Quantifiable changes in WM microstructure are the main pathological basis of cognitive impairment, and may serve as a biomarker of WMLs.

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14

Coutu, Jean-Philippe, J. Jean Chen, H. Diana Rosas, and David H. Salat. "Non-Gaussian water diffusion in aging white matter." Neurobiology of Aging 35, no. 6 (June 2014): 1412–21. http://dx.doi.org/10.1016/j.neurobiolaging.2013.12.001.

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15

Spees, William M., Tsen-Hsuan Lin, and Sheng-Kwei Song. "White-matter diffusion fMRI of mouse optic nerve." NeuroImage 65 (January 2013): 209–15. http://dx.doi.org/10.1016/j.neuroimage.2012.10.021.

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16

Lin, Ching-Po. "Diffusion MRI: A window for white matter study." Neuroscience Research 68 (January 2010): e15. http://dx.doi.org/10.1016/j.neures.2010.07.300.

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17

Mori, Susumu, Kenichi Oishi, and Andreia V. Faria. "White matter atlases based on diffusion tensor imaging." Current Opinion in Neurology 22, no. 4 (August 2009): 362–69. http://dx.doi.org/10.1097/wco.0b013e32832d954b.

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18

Pérez-Sánchez, S., J. M. López-Domínguez, J. Ardúan, and G. Izquierdo. "Diffusion tensor tractography in vanishing white matter disease." Neurología (English Edition) 26, no. 2 (2011): 122–23. http://dx.doi.org/10.1016/s2173-5808(11)70025-6.

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19

Savadjiev, Peter, Gordon L. Kindlmann, Sylvain Bouix, Martha E. Shenton, and Carl-Fredrik Westin. "Local white matter geometry from diffusion tensor gradients." NeuroImage 49, no. 4 (February 2010): 3175–86. http://dx.doi.org/10.1016/j.neuroimage.2009.10.073.

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20

Werring, D. J., C. A. Clark, A. G. Droogan, G. J. Barker, D. H. Miller, and A. J. Thompson. "Water diffusion is elevated in widespread regions of normal-appearing white matter in multiple sclerosis and correlates with diffusion in focal lesions." Multiple Sclerosis Journal 7, no. 2 (April 2001): 83–89. http://dx.doi.org/10.1177/135245850100700202.

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Pathological changes in the normal-appearing white matter in multiple sclerosis are well recognised, but their relationship to pathology in focal lesions is not well understood. Magnetic resonance diffusion imaging is sensitive to abnormalities in the integrity, size and geometry of water spaces in brain tissue. This study investigated the anatomical distribution of normal-appearing white matter diffusion abnormalities and their relationship to diffusion in focal lesions in multiple sclerosis (MS). The average apparent diffusion coefficient (ADCav) was measured by three-axis echoplanar diffusion imaging in normal-appearing white matter regions and lesions throughout the brain in 40 patients, and in white matter in 14 matched controls. The correlation between the ADCav in normal-appearing white matter and lesions was determined. In controls and patients, diffusion was highest in the corpus callosum. Patients had a higher mean ADCav than controls in widespread regions including the corpus callosum, cerebellar, temporal and occipital normal-appearing white matter. Mean normal-appearing white matter ADCav correlated strongly with mean lesion ADCav (r=0.67, P < 0.001). This study demonstrates that water diffusion is elevated in widespread areas of normal-appearing white matter in MS, and is correlated with diffusion in lesions. These findings suggest that the pathogenetic mechanisms causing tissue damage in lesions and normal-appearing white matter are at least partly linked.

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21

Tzarouchi, L. C., A. K. Zikou, N. Tsifetaki, L. G. Astrakas, S. Konitsiotis, P. Voulgari, A. Drosos, and M. I. Argyropoulou. "White Matter Water Diffusion Changes in Primary Sjogren Syndrome." American Journal of Neuroradiology 35, no. 4 (November 1, 2013): 680–85. http://dx.doi.org/10.3174/ajnr.a3756.

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22

Shimoji, K., O. Abe, T. Uka, H. Yasmin, K. Kamagata, K. Asahi, M. Hori, et al. "White Matter Alteration in Metabolic Syndrome: Diffusion tensor analysis." Diabetes Care 36, no. 3 (November 19, 2012): 696–700. http://dx.doi.org/10.2337/dc12-0666.

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23

Goghari, Vina M., Mavis Kusi, Mohammed K. Shakeel, Clare Beasley, Szabolcs David, Alexander Leemans, Alberto De Luca, and Louise Emsell. "Diffusion kurtosis imaging of white matter in bipolar disorder." Psychiatry Research: Neuroimaging 317 (November 2021): 111341. http://dx.doi.org/10.1016/j.pscychresns.2021.111341.

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24

Piantoni, G., S. S. Poil, K. Linkenkaer-Hansen, I. M. Verweij, J. R. Ramautar, E. J. W. Van Someren, and Y. D. Van Der Werf. "Individual Differences in White Matter Diffusion Affect Sleep Oscillations." Journal of Neuroscience 33, no. 1 (January 2, 2013): 227–33. http://dx.doi.org/10.1523/jneurosci.2030-12.2013.

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25

Kim, Dae-Jin, Seong-Yong Park, Jinna Kim, Dong Ha Lee, and Hae-Jeong Park. "Alterations of white matter diffusion anisotropy in early deafness." NeuroReport 20, no. 11 (July 2009): 1032–36. http://dx.doi.org/10.1097/wnr.0b013e32832e0cdd.

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26

Tillema, J. M., J. L. Leach, D. A. Krueger, and D. N. Franz. "Everolimus alters white matter diffusion in tuberous sclerosis complex." Neurology 78, no. 8 (January 18, 2012): 526–31. http://dx.doi.org/10.1212/wnl.0b013e318247ca8d.

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27

Zolal, Amir, Ales Hejcl, Alberto Malucelli, Martina Novakova, Petr Vachata, Robert Bartos, Milous Derner, and Martin Sames. "Distant white-matter diffusion changes caused by tumor growth." Journal of Neuroradiology 40, no. 2 (May 2013): 71–80. http://dx.doi.org/10.1016/j.neurad.2012.05.006.

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28

Buchsbaum, M. S., C. Tang, D. Lu, M. Haznedar, E. A. Hazlett, and S. Atlas. "Diffusion tensor analysis of white matter pathways in schizophrenia." Biological Psychiatry 42, no. 1 (July 1997): 66S. http://dx.doi.org/10.1016/s0006-3223(97)87147-8.

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29

Baker, L. M., D. H. Laidlaw, T. E. Conturo, J. Hogan, Y. Zhao, X. Luo, S. Correia, et al. "White matter changes with age utilizing quantitative diffusion MRI." Neurology 83, no. 3 (June 13, 2014): 247–52. http://dx.doi.org/10.1212/wnl.0000000000000597.

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30

Ardekani, Babak A., Jay Nierenberg, Matthew J. Hoptman, Daniel C. Javitt, and Kelvin O. Lim. "MRI study of white matter diffusion anisotropy in schizophrenia." NeuroReport 14, no. 16 (November 2003): 2025–29. http://dx.doi.org/10.1097/00001756-200311140-00004.

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31

Rasmussen, Jr, I., N. I. Hoven, R. Nesvåg, T. R. Vangberg, E. Jonsson, S. Skare, and I. Agartz. "Diffusion tensor imaging of white matter changes in schizophrenia." Schizophrenia Research 98 (February 2008): 18. http://dx.doi.org/10.1016/j.schres.2007.12.034.

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32

Schonfeld, Amy Rothman. "Diffusion Tensor Imaging Links White Matter Lesions, Poor Gait." Clinical Neurology News 3, no. 11 (November 2007): 16. http://dx.doi.org/10.1016/s1553-3212(07)70337-0.

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33

Fuster, Andrea, Tom Dela Haije, Antonio Tristán-Vega, Birgit Plantinga, Carl-Fredrik Westin, and Luc Florack. "Adjugate Diffusion Tensors for Geodesic Tractography in White Matter." Journal of Mathematical Imaging and Vision 54, no. 1 (June 13, 2015): 1–14. http://dx.doi.org/10.1007/s10851-015-0586-8.

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34

Johnston, M. V. "Diffusion Tensor Imaging of White Matter and Developmental Outcome." PEDIATRICS 122, no. 3 (September 1, 2008): 656–57. http://dx.doi.org/10.1542/peds.2008-1604.

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35

Rokem, A., and F. Pestilli. "Measuring and modelling of diffusion and white-matter tracts." Journal of Vision 14, no. 10 (August 22, 2014): 1461. http://dx.doi.org/10.1167/14.10.1461.

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36

Concha, Luis, Christian Beaulieu, B. Matt Wheatley, and Donald W. Gross. "Bilateral White Matter Diffusion Changes Persist after Epilepsy Surgery." Epilepsia 48, no. 5 (May 2007): 931–40. http://dx.doi.org/10.1111/j.1528-1167.2007.01006.x.

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37

Lazar, Mariana, Jong Hoon Lee, and Andrew L. Alexander. "Axial asymmetry of water diffusion in brain white matter." Magnetic Resonance in Medicine 54, no. 4 (2005): 860–67. http://dx.doi.org/10.1002/mrm.20653.

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38

Shu, Ni, Jun Li, Kuncheng Li, Chunshui Yu, and Tianzi Jiang. "Abnormal diffusion of cerebral white matter in early blindness." Human Brain Mapping 30, no. 1 (January 2009): 220–27. http://dx.doi.org/10.1002/hbm.20507.

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39

Geng, Xiujuan, Elizabeth C. Prom-Wormley, Javier Perez, Thomas Kubarych, Martin Styner, Weili Lin, Michael C. Neale, and John H. Gilmore. "White Matter Heritability Using Diffusion Tensor Imaging in Neonatal Brains." Twin Research and Human Genetics 15, no. 3 (June 2012): 336–50. http://dx.doi.org/10.1017/thg.2012.14.

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Understanding genetic and environmental effects on white matter development in the first years of life is of great interest, as it provides insights into the etiology of neurodevelopmental disorders. In this study, the genetic and environmental effects on white matter were estimated using data from 173 neonatal twin subjects. Diffusion tensor imaging scans were acquired around 40 days after birth and were non-rigidly registered to a group-specific atlas and parcellated into 98 ROIs. A model of additive genetic, and common and specific environmental variance components was used to estimate overall and regional genetic and environmental contributions to diffusion parameters of fractional anisotropy, radial diffusivity, and axial diffusivity. Correlations between the regional heritability values and diffusion parameters were also examined. Results indicate that individual differences in overall white matter microstructure, represented by the average diffusion parameters over the whole brain, are heritable, and estimates are higher than found in studies in adults. Estimates of genetic and environmental variance components vary considerably across different white matter regions. Significant positive correlations between radial diffusivity heritability and radial diffusivity values are consistent with regional genetic variation being modulated by maturation status in the neonatal brain: the more mature the region is, the less genetic variation it shows. Common environmental effects are present in a few regions that tend to be characterized by low radial diffusivity. Results from the joint diffusion parameter analysis suggest that multivariate modeling approaches might be promising to better estimate maturation status and its relationship with genetic and environmental effects.

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40

Yang, Qiong, Xuebing Huang, Nan Hong, and Xin Yu. "White matter microstructural abnormalities in late-life depression." International Psychogeriatrics 19, no. 4 (March 9, 2007): 757–66. http://dx.doi.org/10.1017/s1041610207004875.

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Objective: To evaluate the location and the degree of white matter damage in late-life depression using diffusion tensor imaging (DTI).Methods: Thirty-one patients with late-life depression and 15 healthy volunteers matched for age, gender and years of education received conventional MRI (magnetic resonance imaging) and MR-diffusion tensor scanning. The fractional anisotropy (FA) values of white matter were measured respectively in frontal and temporal regions and the corpus callosum.Results: FA values were significantly decreased in the frontal (superior and middle frontal gyrus), and temporal (right parahippocampal gyrus) regions of elderly patients with depression compared with healthy controls.Conclusion: Microstructural changes in the frontal (superior and middle frontal gyrus) and temporal (right parahippocampal gyrus) areas are associated with late-life depression.

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41

Qiu, Ming-guo, Jing-na Zhang, Ye Zhang, Qi-yu Li, Bing Xie, and Jian Wang. "Diffusion Tensor Imaging-Based Research on Human White Matter Anatomy." Scientific World Journal 2012 (2012): 1–6. http://dx.doi.org/10.1100/2012/530432.

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The aim of this study is to investigate the white matter by the diffusion tensor imaging and the Chinese visible human dataset and to provide the 3D anatomical data of the corticospinal tract for the neurosurgical planning by studying the probabilistic maps and the reproducibility of the corticospinal tract. Diffusion tensor images and high-resolution T1-weighted images of 15 healthy volunteers were acquired; the DTI data were processed using DtiStudio and FSL software. The FA and color FA maps were compared with the sectional images of the Chinese visible human dataset. The probability maps of the corticospinal tract were generated as a quantitative measure of reproducibility for each voxel of the stereotaxic space. The fibers displayed by the diffusion tensor imaging were well consistent with the sectional images of the Chinese visible human dataset and the existing anatomical knowledge. The three-dimensional architecture of the white matter fibers could be clearly visualized on the diffusion tensor tractography. The diffusion tensor tractography can establish the 3D probability maps of the corticospinal tract, in which the degree of intersubject reproducibility of the corticospinal tract is consistent with the previous architectonic report. DTI is a reliable method of studying the fiber connectivity in human brain, but it is difficult to identify the tiny fibers. The probability maps are useful for evaluating and identifying the corticospinal tract in the DTI, providing anatomical information for the preoperative planning and improving the accuracy of surgical risk assessments preoperatively.

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42

Gross, Donald W., Alexandre Bastos, and Christian Beaulieu. "Diffusion Tensor Imaging Abnormalities in Focal Cortical Dysplasia." Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques 32, no. 4 (May 2005): 477–82. http://dx.doi.org/10.1017/s0317167100004479.

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ABSTRACT:Purpose:Focal cortical dysplasia (FCD) is one of the most common underlying pathologic substrates in patients with medically intractable epilepsy. While magnetic resonance imaging (MRI) evidence of FCD is an important predictor of good surgical outcome, conventional MRI is not sensitive enough to detect all lesions. Previous reports of diffusion tensor imaging (DTI) abnormalities in FCD suggest the potential of DTI in the detection of FCD. The purpose of this study was to study subcortical white matter underlying small lesions of FCD using DTI.Methods:Five patients with medically intractable epilepsy and FCD were investigated. Diffusion tensor imaging images were acquired (20 contiguous 3mm thick axial slices) with maps of fractional anisotropy (FA), trace apparent diffusion coefficient (trace/3 ADC), and principal eigenvalues (ADC parallel and ADC perpendicular to white matter tracts) being calculated for each slice. Region of interest analysis was used to compare subcortical white matter ipsilateral and contralateral to the lesion.Results:Three subjects with FCD associated with underlying white matter hyperintensities on T2 weighted MRI were observed to have increased trace/3 ADC, reduced fractional anisotropy and increased perpendicular water diffusivity which was greater than the relative increase in the parallel diffusivity. No DTI abnormalities were identified in two patients with FCD without white matter hyperintensities on conventional T2-weighted MRI.Conclusions:While DTI abnormalities in FCD with obvious white matter involvement are consistent with micro-structural degradation of the underlying subcortical white matter, DTI changes were not identified in FCD lesions with normal appearing white matter.

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43

Bellani, M., and P. Brambilla. "Diffusion imaging studies of white matter integrity in bipolar disorder." Epidemiology and Psychiatric Sciences 20, no. 2 (April 4, 2011): 137–40. http://dx.doi.org/10.1017/s2045796011000229.

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Diffusion tensor imaging (DTI) is a neuroimaging technique with a potential to elucidate white matter integrity. Recently, it has been used in the field of psychiatry to further understand the pathophysiology of major diseases, including bipolar disorder (BD). This review sought to focus on existing DTI findings on white matter organization in BD.

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44

Elliott, CA, V. Mehta, and V. Ramaswamy. "P.122 Restricted diffusion of white matter in infants with subdural hematoma." Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques 43, S2 (June 2016): S48. http://dx.doi.org/10.1017/cjn.2016.221.

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Background: Inflicted head injury is a major cause of infant morbidity and mortality. The extent of traumatic brain injury in infants is often best characterized by diffusion weighted magnetic resonance imaging. In this cases series we describe four infants aged 6-19 months, with small unilateral subdural hematomas secondary to abusive head trauma accompanied by extensive areas of restricted diffusion weighted imaging isolated to the cerebral white matter. Methods: Retrospective, single-centre case series of four children with small unilateral subdural hematomas with early and delayed MR imaging with diffusion weighted imaging. Results: In three cases there was acute diffusion restriction ispilateral to the subdural, while in one case diffusion restriction was present bilaterally. All patients had multiple seizures and bilateral multilayered retinal hemorrhages. After non-surgical treatment, all patients survived albeit with significant motor and cognitive deficits and significant cortical atrophy on long-term followup imaging. Conclusions: These four cases highlight that relatively small subdural hematomas following child abuse can manifest with extensive white matter injury only evident at early stages with diffusion weighted imaging. We propose that selective white matter injury as a result of either reperfusion or axonal degeneration in response to the initial insult accounts for this novel pattern of infantile traumatic brain injury.

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45

Noriega-Gonzalez, David C., Jesús Crespo, Francisco Ardura, Juan Calabia-del Campo, Carlos Alberola-Lopez, Rodrigo de Luis-García, Alberto Caballero-García, and Alfredo Córdova. "Cerebral White Matter Connectivity in Adolescent Idiopathic Scoliosis: A Diffusion Magnetic Resonance Imaging Study." Children 9, no. 7 (July 10, 2022): 1023. http://dx.doi.org/10.3390/children9071023.

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Adolescent idiopathic scoliosis (AIS) is characterized by the radiographic presence of a frontal plane curve, with a magnitude greater than 10° (Cobb technique). Diffusion MRI can be employed to assess the cerebral white matter. The aim of this study was to analyze, by means of MRI, the presence of any alteration in the connectivity of cerebral white matter in AIS patients. In this study, 22 patients with AIS participated. The imaging protocol consisted in T1 and diffusion-weighted acquisitions. Based on the information from one of the diffusion acquisitions, a whole brain tractography was performed with the MRtrix tool. Tractography is a method to deduce the trajectory of fiber bundles through the white matter based on the diffusion MRI data. By combining cortical segmentation with tractography, a connectivity matrix of size 84 × 84 was constructed using FA (fractional anisotropy), and the number of streamlines as connectomics metrics. The results obtained support the hypothesis that alterations in cerebral white matter connectivity in patients with adolescent idiopathic scoliosis (AIS) exist. We consider that the application of diffusion MRI, together with transcranial magnetic stimulation neurophysiologically, is useful to search the etiology of AIS.

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46

Herwerth, Marina, Benedikt J. Schwaiger, Kornelia Kreiser, Bernhard Hemmer, and Rüdiger Ilg. "Adult-onset vanishing white matter disease as differential diagnosis of primary progressive multiple sclerosis: A case report." Multiple Sclerosis Journal 21, no. 5 (August 18, 2014): 666–68. http://dx.doi.org/10.1177/1352458514546515.

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We report the case of a 42-year-old woman with a slowly progressive cerebellar syndrome. In contrast to a relatively mild clinical presentation, the magnetic resonance imaging (MRI) showed extensive leukencephalopathy with cystic degeneration. Initially primary progressive multiple sclerosis (PPMS) was suspected. Additional diffusion-weighted imaging revealed restricted diffusion in the white matter lesions with a reduced apparent diffusion coefficient. Genetic testing showed vanishing white matter disease (VWM) with c.260C>T EIF2B3 mutation. In conclusion, in cases with relatively mild symptoms and extensive white matter lesions, adult-onset VWM should be considered as differential diagnosis of PPMS and diffusion-weighted imaging may be helpful to identify suspected cases.

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Farquharson, Shawna, J. Donald Tournier, Fernando Calamante, Gavin Fabinyi, Michal Schneider-Kolsky, Graeme D. Jackson, and Alan Connelly. "White matter fiber tractography: why we need to move beyond DTI." Journal of Neurosurgery 118, no. 6 (June 2013): 1367–77. http://dx.doi.org/10.3171/2013.2.jns121294.

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Object Diffusion-based MRI tractography is an imaging tool increasingly used in neurosurgical procedures to generate 3D maps of white matter pathways as an aid to identifying safe margins of resection. The majority of white matter fiber tractography software packages currently available to clinicians rely on a fundamentally flawed framework to generate fiber orientations from diffusion-weighted data, namely diffusion tensor imaging (DTI). This work provides the first extensive and systematic exploration of the practical limitations of DTI-based tractography and investigates whether the higher-order tractography model constrained spherical deconvolution provides a reasonable solution to these problems within a clinically feasible timeframe. Methods Comparison of tractography methodologies in visualizing the corticospinal tracts was made using the diffusion-weighted data sets from 45 healthy controls and 10 patients undergoing presurgical imaging assessment. Tensor-based and constrained spherical deconvolution–based tractography methodologies were applied to both patients and controls. Results Diffusion tensor imaging–based tractography methods (using both deterministic and probabilistic tractography algorithms) substantially underestimated the extent of tracks connecting to the sensorimotor cortex in all participants in the control group. In contrast, the constrained spherical deconvolution tractography method consistently produced the biologically expected fan-shaped configuration of tracks. In the clinical cases, in which tractography was performed to visualize the corticospinal pathways in patients with concomitant risk of neurological deficit following neurosurgical resection, the constrained spherical deconvolution–based and tensor-based tractography methodologies indicated very different apparent safe margins of resection; the constrained spherical deconvolution–based method identified corticospinal tracts extending to the entire sensorimotor cortex, while the tensor-based method only identified a narrow subset of tracts extending medially to the vertex. Conclusions This comprehensive study shows that the most widely used clinical tractography method (diffusion tensor imaging–based tractography) results in systematically unreliable and clinically misleading information. The higher-order tractography model, using the same diffusion-weighted data, clearly demonstrates fiber tracts more accurately, providing improved estimates of safety margins that may be useful in neurosurgical procedures. We therefore need to move beyond the diffusion tensor framework if we are to begin to provide neurosurgeons with biologically reliable tractography information.

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Reginold, William, Angela C. Luedke, Justine Itorralba, Juan Fernandez-Ruiz, Omar Islam, and Angeles Garcia. "Altered Superficial White Matter on Tractography MRI in Alzheimer's Disease." Dementia and Geriatric Cognitive Disorders Extra 6, no. 2 (June 22, 2016): 233–41. http://dx.doi.org/10.1159/000446770.

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Background/Aims: Superficial white matter provides extensive cortico-cortical connections. This tractography study aimed to assess the diffusion characteristics of superficial white matter tracts in Alzheimer's disease. Methods: Diffusion tensor 3T magnetic resonance imaging scans were acquired in 24 controls and 16 participants with Alzheimer's disease. Neuropsychological test scores were available in some participants. Tractography was performed by the Fiber Assignment by Continuous Tracking (FACT) method. The superficial white matter was manually segmented and divided into frontal, parietal, temporal and occipital lobes. The mean diffusivity (MD), radial diffusivity (RD), axial diffusivity (AxD) and fractional anisotropy (FA) of these tracts were compared between controls and participants with Alzheimer's disease and correlated with available cognitive tests while adjusting for age and white matter hyperintensity volume. Results: Alzheimer's disease was associated with increased MD (p = 0.0011), increased RD (p = 0.0019) and increased AxD (p = 0.0017) in temporal superficial white matter. In controls, superficial white matter was associated with the performance on the Montreal Cognitive Assessment, Stroop and Trail Making Test B tests, whereas in Alzheimer's disease patients, it was not associated with the performance on cognitive tests. Conclusion: Temporal lobe superficial white matter appears to be disrupted in Alzheimer's disease.

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Chabriat, Hugues, Nicolas Molko, Sabina Pappata, Jean Francois Mangin, Cyril Poupon, Antoinette Jobert, Denis Le Bihan, and Marie-Germaine Bousser. "Thalamic microstructural alterations secondary to white-matter damage in CADASIL: evidence from diffusion tensor imaging study." Stroke 32, suppl_1 (January 2001): 342. http://dx.doi.org/10.1161/str.32.suppl_1.342-a.

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P18 Background and Purpose: Variable microstructural changes have been reported within the white-matter in CADASIL. Lacunar infarcts are frequently observed in the subcortical grey matter. However, whether microstructural tissular alterations are also present within the non-infarcted putamen (NIP) or thalamus (NIT), remains unknown. Methods: We used diffusion tensor imaging, a MRI method highly sensitive to the cerebral microstructure, in 20 CADASIL patients and 12 age-matched controls. Both Trace(D) and anisotropy (volume-ratio index) of diffusion were measured within the NIP and NIT. In addition, diffusion parameters and the load of small infarcts were calculated within the white-matter of the centrum semi-ovale. A one way ANOVA was performed to compare the diffusion parameters between patients and controls. Thereafter, correlations between the significant results in our patients and the diffusion parameters in the white-matter or the MMSE scores were tested. Results: A significant increase in Trace(D) and decrease in anisotropy were observed in both the NIP and NIT in our patients. Conversely to the results in the NIP, only significant in the presence of associated putaminal infarcts, the diffusion changes in the NIT were significant both in the presence and absence of associated thalamic infarcts. The asymmetry indices of Trace(D) in the NIT and in the white-matter were strongly correlated. The diffusion increase in the NIT was also positively correlated both to Trace(D) and to the load of small deep infarcts within the centrum semi-ovale. Elsewhere, the diffusion increase in the NIT, but not in the NIP, was negatively correlated to the MMSE score in our patients. Conclusions: These results suggest that microstructural alterations are present in both the NIP and NIT in CADASIL. In the NIT, microstructural alterations appear secondary to remote lesions of thalamo-cortical pathways. Diffusion measured in the NIT mirror both the degree of white-matter tissular damage and the clinical severity in CADASIL. This study highlights the importance of secondary neurodegeneration in a typical model of “pure vascular dementia”.

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Baird, Benjamin, Matthew Cieslak, Jonathan Smallwood, Scott T. Grafton, and Jonathan W. Schooler. "Regional White Matter Variation Associated with Domain-specific Metacognitive Accuracy." Journal of Cognitive Neuroscience 27, no. 3 (March 2015): 440–52. http://dx.doi.org/10.1162/jocn_a_00741.

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The neural mechanisms that mediate metacognitive ability (the capacity to accurately reflect on one's own cognition and experience) remain poorly understood. An important question is whether metacognitive capacity is a domain-general skill supported by a core neuroanatomical substrate or whether regionally specific neural structures underlie accurate reflection in different cognitive domains. Providing preliminary support for the latter possibility, recent findings have shown that individual differences in metacognitive ability in the domains of memory and perception are related to variation in distinct gray matter volume and resting-state functional connectivity. The current investigation sought to build on these findings by evaluating how metacognitive ability in these domains is related to variation in white matter microstructure. We quantified metacognitive ability across memory and perception domains and used diffusion spectrum imaging to examine the relation between high-resolution measurements of white matter microstructure and individual differences in metacognitive accuracy in each domain. We found that metacognitive accuracy for perceptual decisions and memory were uncorrelated across individuals and that metacognitive accuracy in each domain was related to variation in white matter microstructure in distinct brain areas. Metacognitive accuracy for perceptual decisions was associated with increased diffusion anisotropy in white matter underlying the ACC, whereas metacognitive accuracy for memory retrieval was associated with increased diffusion anisotropy in the white matter extending into the inferior parietal lobule. Together, these results extend previous findings linking metacognitive ability in the domains of perception and memory to variation in distinct brain structures and connections.

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