Resting state sensorimotor functional connectivity in multiple sclerosis inversely correlates with transcallosal motor pathway transverse diffusivity

Human Brain Mapping - Tập 29 Số 7 - Trang 818-827 - 2008
Mark J. Lowe1, Erik B. Beall1, Ken Sakaie1, Katherine Koenig1, Lael A. Stone2, Ruth Ann Marrie3, Micheal D. Phillips1
1Imaging Institute, Cleveland Clinic, Cleveland, Ohio
2Neurological Institute, Cleveland Clinic, Cleveland, Ohio
3Department of Internal Medicine, University of Manitoba, Winnipeg, Manitoba, Canada

Tóm tắt

AbstractRecent studies indicate that functional connectivity using low‐frequency BOLD fluctuations (LFBFs) is reduced between the bilateral primary sensorimotor regions in multiple sclerosis. In addition, it has been shown that pathway‐dependent measures of the transverse diffusivity of water in white matter correlate with related clinical measures of functional deficit in multiple sclerosis. Taken together, these methods suggest that MRI methods can be used to probe both functional connectivity and anatomic connectivity in subjects with known white matter impairment. We report the results of a study comparing anatomic connectivity of the transcallosal motor pathway, as measured with diffusion tensor imaging (DTI) and functional connectivity of the bilateral primary sensorimotor cortices (SMC), as measured with LFBFs in the resting state. High angular resolution diffusion imaging was combined with functional MRI to define the transcallosal white matter pathway connecting the bilateral primary SMC. Maps were generated from the probabilistic tracking employed and these maps were used to calculate the mean pathway diffusion measures fractional anisotropy 〈FA〉, mean diffusivity 〈MD〉, longitudinal diffusivity 〈λ1〉, and transverse diffusivity 〈λ2〉. These were compared with LFBF‐based functional connectivity measures (Fc) obtained at rest in a cohort of 11 multiple sclerosis patients and ∼10 age‐ and gender‐matched control subjects. The correlation between 〈FA〉 and Fc for MS patients was r = −0.63, P < 0.04. The correlation between all subjects 〈λ2〉 and Fc was r = 0.42, P < 0.05. The correlation between all subjects 〈λ2〉 and Fc was r = −0.50, P < 0.02. None of the control subject correlations were significant, nor were 〈FA〉, 〈λ1〉, or 〈MD〉 significantly correlated with Fc for MS patients. This constitutes the first in vivo observation of a correlation between measures of anatomic connectivity and functional connectivity using spontaneous LFBFs. Hum Brain Mapp, 2008. © 2008 Wiley‐Liss, Inc.

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Tài liệu tham khảo

Adams RD, 1997, Principles Of Neurology, 903

10.1016/0022-510X(79)90142-4

10.1016/j.biopsych.2005.02.021

10.1002/1522-2594(200010)44:4<583::AID-MRM12>3.0.CO;2-O

10.1016/j.neuroimage.2007.07.004

10.1016/j.neuroimage.2004.12.053

10.1097/00004647-199703000-00007

10.1002/(SICI)1099-1492(199706/08)10:4/5<165::AID-NBM454>3.0.CO;2-7

10.1002/mrm.1910340409

10.1097/00019052-200106000-00003

10.1007/BF03033380

10.1212/WNL.57.7.1248

10.1016/S0022-510X(02)00069-2

10.1002/(SICI)1097-0193(1999)7:1<38::AID-HBM4>3.0.CO;2-Q

Castriota Scanderbeg A, 2000, Demyelinating plaques in relapsing‐remitting and secondary‐progressive multiple sclerosis: Assessment with diffusion MR imaging, AJNR Am J Neuroradiol, 21, 862

10.1212/WNL.56.7.926

10.1016/S1053-8119(01)91441-7

10.1191/1352458504ms1053oa

Cordes D, 2001, Frequencies contributing to functional connectivity in the cerebral cortex in “resting‐state” data, AJNR Am J Neuroradiol, 22, 1326

10.1006/cbmr.1996.0014

10.1093/brain/122.10.1933

10.1073/pnas.0608961104

10.1002/1531-8249(200003)47:3<391::AID-ANA20>3.0.CO;2-J

10.1093/brain/120.3.393

10.1212/WNL.56.3.304

10.1046/j.1365-2990.1999.00205.x

10.1002/jmri.20083

10.1002/1522-2594(200007)44:1<162::AID-MRM23>3.0.CO;2-E

10.1073/pnas.0308627101

Guo AC, 2001, Analysis of normal‐appearing white matter in multiple sclerosis: comparison of diffusion tensor MR imaging and magnetization transfer imaging, AJNR Am J Neuroradiol, 22, 1893

10.1148/radiol.2223010311

10.1016/S1053-8119(03)00142-3

10.1523/JNEUROSCI.3408-06.2006

10.1016/j.neuroimage.2005.12.040

10.1016/j.neuroimage.2006.11.042

10.1002/jmri.10379

10.1038/nature02078

10.1111/j.1750-3639.1999.tb00547.x

10.1016/S0002-9440(10)64537-3

LandmanBA FarrellJAD PatelNL MoriS PrinceJL(2007):DTI Fiber Tracking: The Importance of Adjusting DTI Gradient Tables for Motion Correction. CATNAP‐‐A Tool to Simplify and Accelerate DTI Analysis. Paper presented at the Human Brain Mapping Chicago.

10.1073/pnas.1831638100

10.1093/cercor/13.4.422

10.1093/brain/123.2.308

10.1097/00004728-199905000-00025

10.1002/mrm.1910370514

10.1016/j.neuroimage.2006.04.208

10.1006/nimg.1997.0315

10.1148/radiol.2241011005

10.1016/j.neuroimage.2006.02.004

10.2214/ajr.175.3.1750821

10.1136/jnnp.2004.039032

10.1016/0028-3932(71)90067-4

10.1006/nimg.2002.1141

Press W, 1986, Numerical Recipes: The Art of Scientific Computing

10.1007/s00415-003-1024-1

10.1016/S0022-510X(01)00690-6

10.1016/j.neuroimage.2006.08.034

10.1212/WNL.54.5.1155

10.1016/j.neuroimage.2004.07.051

10.1016/j.nbd.2003.12.003

10.1016/j.neuroimage.2003.07.005

10.1006/nimg.2002.1267

10.1016/j.neuroimage.2005.01.028

Tievsky AL, 1999, Investigation of apparent diffusion coefficient and diffusion tensor anisotrophy in acute and chronic multiple sclerosis lesions, AJNR Am J Neuroradiol, 20, 1491

10.1056/NEJM199801293380502

10.1016/j.neuroimage.2005.09.024

10.1093/brain/123.8.1667

10.1212/WNL.52.8.1626

10.1136/jnnp.69.2.269