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10-05-2022 | Multiple Sclerosis | Original Article

Associations between corpus callosum damage, clinical disability, and surface-based homologous inter-hemispheric connectivity in multiple sclerosis

Authors: Andrew W. Russo, Kirsten E. Stockel, Sean M. Tobyne, Chanon Ngamsombat, Kristina Brewer, Aapo Nummenmaa, Susie Y. Huang, Eric C. Klawiter

Published in: Brain Structure and Function | Issue 9/2022

Abstract

Axonal damage in the corpus callosum is prevalent in multiple sclerosis (MS). Although callosal damage is associated with disrupted functional connectivity between hemispheres, it is unclear how this relates to cognitive and physical disability. We investigated this phenomenon using advanced measures of microstructural integrity in the corpus callosum and surface-based homologous inter-hemispheric connectivity (sHIC) in the cortex. We found that sHIC was significantly decreased in primary motor, somatosensory, visual, and temporal cortical areas in a group of 36 participants with MS (29 relapsing–remitting, 4 secondary progressive MS, and 3 primary-progressive MS) compared with 42 healthy controls (cluster level, p < 0.05). In participants with MS, global sHIC correlated with fractional anisotropy and restricted volume fraction in the posterior segment of the corpus callosum (r = 0.426, p = 0.013; r = 0.399, p = 0.020, respectively). Lower sHIC, particularly in somatomotor and posterior cortical areas, was associated with cognitive impairment and higher disability scores on the Expanded Disability Status Scale (EDSS). We demonstrated that higher levels of sHIC attenuated the effects of posterior callosal damage on physical disability and cognitive dysfunction, as measured by the EDSS and Brief Visuospatial Memory Test-Revised (interaction effect, p < 0.05). We also observed a positive association between global sHIC and years of education (r = 0.402, p = 0.018), supporting the phenomenon of “brain reserve” in MS. Our data suggest that preserved sHIC helps prevent cognitive and physical decline in MS.

Anticevic A, Dierker DL, Gillespie SK, Repovs G, Csernansky JG, Van Essen DC, Barch DM (2008) Comparing surface-based and volume-based analyses of functional neuroimaging data in patients with schizophrenia. Neuroimage 41(3):835–848. https://doi.org/10.1016/j.neuroimage.2008.02.052CrossRefPubMed

Barnard RO, Triggs M (1974) Corpus callosum in multiple sclerosis. J Neurol Neurosurg Psychiatry 37(11):1259–1264. https://doi.org/10.1136/jnnp.37.11.1259CrossRefPubMedPubMedCentral

Behrens TE, Berg HJ, Jbabdi S, Rushworth MF, Woolrich MW (2007) Probabilistic diffusion tractography with multiple fibre orientations: What can we gain? Neuroimage 34(1):144–155. https://doi.org/10.1016/j.neuroimage.2006.09.018CrossRefPubMed

Benedict RH, Cookfair D, Gavett R, Gunther M, Munschauer F, Garg N, Weinstock-Guttman B (2006) Validity of the minimal assessment of cognitive function in multiple sclerosis (MACFIMS). J Int Neuropsychol Soc 12(4):549–558. https://doi.org/10.1017/s1355617706060723CrossRefPubMed

Bergendal G, Martola J, Stawiarz L, Kristoffersen-Wiberg M, Fredrikson S, Almkvist O (2013) Callosal atrophy in multiple sclerosis is related to cognitive speed. Acta Neurol Scand 127(4):281–289. https://doi.org/10.1111/ane.12006CrossRefPubMed

Biswal B, Yetkin FZ, Haughton VM, Hyde JS (1995) Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med 34(4):537–541. https://doi.org/10.1002/mrm.1910340409CrossRefPubMed

Bodini B, Cercignani M, Khaleeli Z, Miller DH, Ron M, Penny S, Ciccarelli O (2013) Corpus callosum damage predicts disability progression and cognitive dysfunction in primary-progressive MS after five years. Hum Brain Mapp 34(5):1163–1172. https://doi.org/10.1002/hbm.21499CrossRefPubMed

Cutter GR, Baier ML, Rudick RA, Cookfair DL, Fischer JS, Petkau J, Willoughby E (1999) Development of a multiple sclerosis functional composite as a clinical trial outcome measure. Brain 122(Pt 5):871–882CrossRefPubMed

Evangelou N, Konz D, Esiri MM, Smith S, Palace J, Matthews PM (2000) Regional axonal loss in the corpus callosum correlates with cerebral white matter lesion volume and distribution in multiple sclerosis. Brain 123(Pt 9):1845–1849CrossRefPubMed

Fan Q, Witzel T, Nummenmaa A, Van Dijk KRA, Van Horn JD, Drews MK, Rosen BR (2016) MGH-USC human connectome project datasets with ultra-high b value diffusion MRI. Neuroimage 124(Pt B):1108–1114. https://doi.org/10.1016/j.neuroimage.2015.08.075CrossRefPubMed

Fan Q, Nummenmaa A, Witzel T, Ohringer N, Tian Q, Setsompop K, Huang SY (2020) Axon diameter index estimation independent of fiber orientation distribution using high-gradient diffusion MRI. Neuroimage 222:117197. https://doi.org/10.1016/j.neuroimage.2020.117197CrossRefPubMed

Fan Q, Polackal MN, Tian Q, Ngamsombat C, Nummenmaa A, Witzel T, Huang SY (2021) Scan-rescan repeatability of axonal imaging metrics using high-gradient diffusion MRI and statistical implications for study design. Neuroimage 240:118323. https://doi.org/10.1016/j.neuroimage.2021.118323CrossRefPubMed

Feinberg DA, Setsompop K (2013) Ultra-fast MRI of the human brain with simultaneous multi-slice imaging. J Magn Reson 229:90–100. https://doi.org/10.1016/j.jmr.2013.02.002CrossRefPubMedPubMedCentral

Fischl B, Salat DH, Busa E, Albert M, Dieterich M, Haselgrove C, Dale AM (2002) Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 33(3):341–355. https://doi.org/10.1016/s0896-6273(02)00569-xCrossRefPubMed

Fischl B, Rajendran N, Busa E, Augustinack J, Hinds O, Yeo BT, Zilles K (2008) Cortical folding patterns and predicting cytoarchitecture. Cereb Cortex 18(8):1973–1980. https://doi.org/10.1093/cercor/bhm225CrossRefPubMed

Fuchs TA, Benedict RHB, Bartnik A, Choudhery S, Li X, Mallory M, Dwyer MG (2019) Preserved network functional connectivity underlies cognitive reserve in multiple sclerosis. Hum Brain Mapp 40(18):5231–5241. https://doi.org/10.1002/hbm.24768CrossRefPubMedPubMedCentral

Ge Y, Law M, Johnson G, Herbert J, Babb JS, Mannon LJ, Grossman RI (2004) Preferential occult injury of corpus callosum in multiple sclerosis measured by diffusion tensor imaging. J Magn Reson Imaging 20(1):1–7. https://doi.org/10.1002/jmri.20083CrossRefPubMed

Govindarajan, K. A., Datta, S., Hasan, K. M., Choi, S., Rahbar, M. H., Cofield, S. S., Group, C. I (2015) Effect of in-painting on cortical thickness measurements in multiple sclerosis: a large cohort study. Hum Brain Mapp 36(10):3749–3760CrossRef

Granberg T, Martola J, Bergendal G, Shams S, Damangir S, Aspelin P, Kristoffersen-Wiberg M (2015) Corpus callosum atrophy is strongly associated with cognitive impairment in multiple sclerosis: results of a 17 year longitudinal study. Mult Scler 21(9):1151–1158. https://doi.org/10.1177/1352458514560928CrossRefPubMed

Greve DN, Van der Haegen L, Cai Q, Stufflebeam S, Sabuncu MR, Fischl B, Brysbaert M (2013) A surface-based analysis of language lateralization and cortical asymmetry. J Cogn Neurosci 25(9):1477–1492. https://doi.org/10.1162/jocn_a_00405CrossRefPubMedPubMedCentral

Hagler DJ, Saygin AP, Sereno MI (2006) Smoothing and cluster thresholding for cortical surface-based group analysis of fMRI data. Neuroimage 33(4):1093–1103. https://doi.org/10.1016/j.neuroimage.2006.07.036CrossRefPubMed

Hawellek DJ, Hipp JF, Lewis CM, Corbetta M, Engel AK (2011) Increased functional connectivity indicates the severity of cognitive impairment in multiple sclerosis. Proc Natl Acad Sci U S A 108(47):19066–19071. https://doi.org/10.1073/pnas.1110024108CrossRefPubMedPubMedCentral

Huang SY, Tobyne SM, Nummenmaa A, Witzel T, Wald LL, McNab JA, Klawiter EC (2016) Characterization of axonal disease in patients with multiple sclerosis using high-gradient-diffusion MR imaging. Radiology. https://doi.org/10.1148/radiol.2016151582CrossRefPubMed

Huang SY, Fan Q, Machado N, Eloyan A, Bireley JD, Russo AW, Klawiter EC (2019) Corpus callosum axon diameter relates to cognitive impairment in multiple sclerosis. Ann Clin Transl Neurol 6(5):882–892. https://doi.org/10.1002/acn3.760CrossRefPubMedPubMedCentral

Huang SY, Tian Q, Fan Q, Witzel T, Wichtmann B, McNab JA, Nummenmaa A (2020) High-gradient diffusion MRI reveals distinct estimates of axon diameter index within different white matter tracts in the in vivo human brain. Brain Struct Funct 225(4):1277–1291. https://doi.org/10.1007/s00429-019-01961-2CrossRefPubMed

Huang SY, Nummenmaa A, Witzel T, Duval T, Cohen-Adad J, Wald LL, McNab JA (2015) The impact of gradient strength on in vivo diffusion MRI estimates of axon diameter [research support, NIH, extramural research support, non-US gov’t]. Neuroimage 106:464–472. https://doi.org/10.1016/j.neuroimage.2014.12.008CrossRefPubMed

Innocenti GM (2009) Dynamic interactions between the cerebral hemispheres. Exp Brain Res 192(3):417–423. https://doi.org/10.1007/s00221-008-1484-8CrossRefPubMed

Jahanian H, Holdsworth S, Christen T, Wu H, Zhu K, Kerr AB, Zaharchuk G (2019) Advantages of short repetition time resting-state functional MRI enabled by simultaneous multi-slice imaging. J Neurosci Methods 311:122–132. https://doi.org/10.1016/j.jneumeth.2018.09.033CrossRefPubMed

Jo HJ, Lee JM, Kim JH, Shin YW, Kim IY, Kwon JS, Kim SI (2007) Spatial accuracy of fMRI activation influenced by volume- and surface-based spatial smoothing techniques. Neuroimage 34(2):550–564. https://doi.org/10.1016/j.neuroimage.2006.09.047CrossRefPubMed

Jo HJ, Lee JM, Kim JH, Choi CH, Gu BM, Kang DH, Kim SI (2008) Artificial shifting of fMRI activation localized by volume- and surface-based analyses. Neuroimage 40(3):1077–1089. https://doi.org/10.1016/j.neuroimage.2007.12.036CrossRefPubMed

Kamagata K, Zalesky A, Yokoyama K, Andica C, Hagiwara A, Shimoji K, Aoki S (2019) MR g-ratio-weighted connectome analysis in patients with multiple sclerosis. Sci Rep 9(1):13522. https://doi.org/10.1038/s41598-019-50025-2CrossRefPubMedPubMedCentral

Keil B, Blau JN, Biber S, Hoecht P, Tountcheva V, Setsompop K, Wald LL (2013) A 64-channel 3T array coil for accelerated brain MRI. Magn Reson Med 70(1):248–258. https://doi.org/10.1002/mrm.24427CrossRefPubMed

Kern KC, Sarcona J, Montag M, Giesser BS, Sicotte NL (2011) Corpus callosal diffusivity predicts motor impairment in relapsing-remitting multiple sclerosis: a TBSS and tractography study. Neuroimage 55(3):1169–1177. https://doi.org/10.1016/j.neuroimage.2010.10.077CrossRefPubMed

Klawiter EC, Schmidt RE, Trinkaus K, Liang HF, Budde MD, Naismith RT, Benzinger TL (2011) Radial diffusivity predicts demyelination in ex vivo multiple sclerosis spinal cords. Neuroimage 55(4):1454–1460. https://doi.org/10.1016/j.neuroimage.2011.01.007CrossRefPubMed

Klawiter EC, Ceccarelli A, Arora A, Jackson J, Bakshi S, Kim G, Neema M (2015) Corpus callosum atrophy correlates with gray matter atrophy in patients with multiple sclerosis. J Neuroimaging 25(1):62–67. https://doi.org/10.1111/jon.12124CrossRefPubMed

Lin SJ, Kolind S, Liu A, McMullen K, Vavasour I, Wang ZJ, McKeown MJ (2020) Both stationary and dynamic functional interhemispheric connectivity are strongly associated with performance on cognitive tests in multiple sclerosis. Front Neurol 11:407. https://doi.org/10.3389/fneur.2020.00407CrossRefPubMedPubMedCentral

Llufriu S, Blanco Y, Martinez-Heras E, Casanova-Molla J, Gabilondo I, Sepulveda M, Saiz A (2012) Influence of corpus callosum damage on cognition and physical disability in multiple sclerosis: a multimodal study. PLoS ONE 7(5):e37167. https://doi.org/10.1371/journal.pone.0037167CrossRefPubMedPubMedCentral

Lowe MJ, Beall EB, Sakaie KE, Koenig KA, Stone L, Marrie RA, Phillips MD (2008) Resting state sensorimotor functional connectivity in multiple sclerosis inversely correlates with transcallosal motor pathway transverse diffusivity. Hum Brain Mapp 29(7):818–827. https://doi.org/10.1002/hbm.20576CrossRefPubMedPubMedCentral

Mandelli ML, Vilaplana E, Brown JA, Hubbard HI, Binney RJ, Attygalle S, Gorno-Tempini ML (2016) Healthy brain connectivity predicts atrophy progression in non-fluent variant of primary progressive aphasia. Brain 139(Pt 10):2778–2791. https://doi.org/10.1093/brain/aww195CrossRefPubMedPubMedCentral

Manson SC, Palace J, Frank JA, Matthews PM (2006) Loss of interhemispheric inhibition in patients with multiple sclerosis is related to corpus callosum atrophy. Exp Brain Res 174(4):728–733. https://doi.org/10.1007/s00221-006-0517-4CrossRefPubMed

Nithianantharajah J, Hannan AJ (2009) The neurobiology of brain and cognitive reserve: mental and physical activity as modulators of brain disorders. Prog Neurobiol 89(4):369–382. https://doi.org/10.1016/j.pneurobio.2009.10.001CrossRefPubMed

Ozturk A, Smith SA, Gordon-Lipkin EM, Harrison DM, Shiee N, Pham DL, Reich DS (2010) MRI of the corpus callosum in multiple sclerosis: association with disability. Mult Scler 16(2):166–177. https://doi.org/10.1177/1352458509353649CrossRefPubMedPubMedCentral

Pasqua G, Tommasin S, Bharti K, Ruggieri S, Petsas N, Piervincenzi C, Pantano P (2020) Resting-state functional connectivity of anterior and posterior cerebellar lobes is altered in multiple sclerosis. Mult Scler. https://doi.org/10.1177/1352458520922770CrossRefPubMed

Penner IK, Aktas O (2017) Functional reorganization is a maladaptive response to injury–no. Mult Scler 23(2):193–194. https://doi.org/10.1177/1352458516679895CrossRefPubMed

Rocca MA, Filippi M (2017) Functional reorganization is a maladaptive response to injury–yes. Mult Scler 23(2):191–193. https://doi.org/10.1177/1352458516667242CrossRefPubMed

Rosen BQ, Halgren E (2021) A whole-cortex probabilistic diffusion tractography connectome. eNeuro. https://doi.org/10.1177/1352458516667242CrossRefPubMedPubMedCentral

Rubinov M, Sporns O (2010) Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52(3):1059–1069. https://doi.org/10.1016/j.neuroimage.2009.10.003CrossRefPubMed

Sarwar T, Ramamohanarao K, Zalesky A (2019) Mapping connectomes with diffusion MRI: deterministic or probabilistic tractography? Magn Reson Med 81(2):1368–1384. https://doi.org/10.1002/mrm.27471CrossRefPubMed

Schmierer K, Wheeler-Kingshott CA, Boulby PA, Scaravilli F, Altmann DR, Barker GJ, Miller DH (2007) Diffusion tensor imaging of post mortem multiple sclerosis brain. Neuroimage 35(2):467–477. https://doi.org/10.1016/j.neuroimage.2006.12.010CrossRefPubMed

Stark DE, Margulies DS, Shehzad ZE, Reiss P, Kelly AM, Uddin LQ, Milham MP (2008) Regional variation in interhemispheric coordination of intrinsic hemodynamic fluctuations. J Neurosci 28(51):13754–13764. https://doi.org/10.1523/JNEUROSCI.4544-08.2008CrossRefPubMedPubMedCentral

Sumowski JF, Wylie GR, Leavitt VM, Chiaravalloti ND, DeLuca J (2013) Default network activity is a sensitive and specific biomarker of memory in multiple sclerosis. Mult Scler 19(2):199–208. https://doi.org/10.1177/1352458512448267CrossRefPubMed

Tian Q, Fan Q, Witzel T, Polackal MN, Ohringer NA, Ngamsombat C, Huang SY (2022) Comprehensive diffusion MRI dataset for in vivo human brain microstructure mapping using 300 mT/m gradients. Sci Data 9(1):7. https://doi.org/10.1038/s41597-021-01092-6CrossRefPubMedPubMedCentral

Tobyne SM, Boratyn D, Johnson JA, Greve DN, Mainero C, Klawiter EC (2016) A surface-based technique for mapping homotopic interhemispheric connectivity: development, characterization, and clinical application. Hum Brain Mapp 37(8):2849–2868. https://doi.org/10.1002/hbm.23214CrossRefPubMedPubMedCentral

Tona F, Petsas N, Sbardella E, Prosperini L, Carmellini M, Pozzilli C, Pantano P (2014) Multiple sclerosis: altered thalamic resting-state functional connectivity and its effect on cognitive function. Radiology 271(3):814–821. https://doi.org/10.1148/radiol.14131688CrossRefPubMed

Tucholka A, Fritsch V, Poline JB, Thirion B (2012) An empirical comparison of surface-based and volume-based group studies in neuroimaging. Neuroimage 63(3):1443–1453. https://doi.org/10.1016/j.neuroimage.2012.06.019CrossRefPubMed

Veraart J, Nunes D, Rudrapatna U, Fieremans E, Jones DK, Novikov DS, Shemesh N (2020) Nonivasive quantification of axon radii using diffusion MRI. Elife. https://doi.org/10.7554/eLife.49855CrossRefPubMedPubMedCentral

Zhang J, Wang J, Wu Q, Kuang W, Huang X, He Y, Gong Q (2011) Disrupted brain connectivity networks in drug-naive, first-episode major depressive disorder. Biol Psychiatry 70(4):334–342. https://doi.org/10.1016/j.biopsych.2011.05.018CrossRefPubMed

Zhou J, Gennatas ED, Kramer JH, Miller BL, Seeley WW (2012) Predicting regional neurodegeneration from the healthy brain functional connectome. Neuron 73(6):1216–1227. https://doi.org/10.1016/j.neuron.2012.03.004CrossRefPubMedPubMedCentral

Zhou Y, Milham M, Zuo XN, Kelly C, Jaggi H, Herbert J, Ge Y (2013) Functional homotopic changes in multiple sclerosis with resting-state functional MR imaging. AJNR Am J Neuroradiol 34(6):1180–1187. https://doi.org/10.3174/ajnr.A3386CrossRefPubMedPubMedCentral

Zito G, Luders E, Tomasevic L, Lupoi D, Toga AW, Thompson PM, Tecchio F (2014) Inter-hemispheric functional connectivity changes with corpus callosum morphology in multiple sclerosis. Neuroscience 266:47–55. https://doi.org/10.1016/j.neuroscience.2014.01.039CrossRefPubMed

Title: Associations between corpus callosum damage, clinical disability, and surface-based homologous inter-hemispheric connectivity in multiple sclerosis
Authors: Andrew W. Russo
Kirsten E. Stockel
Sean M. Tobyne
Chanon Ngamsombat
Kristina Brewer
Aapo Nummenmaa
Susie Y. Huang
Eric C. Klawiter
Publication date: 10-05-2022
Publisher: Springer Berlin Heidelberg
Keyword: Multiple Sclerosis
Published in: Brain Structure and Function / Issue 9/2022
Print ISSN: 1863-2653
Electronic ISSN: 1863-2661
DOI: https://doi.org/10.1007/s00429-022-02498-7

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