Microscopic fractional anisotropy outperforms multiple sclerosis lesion assessment and clinical outcome associations over standard fractional anisotropy tensor.
automated fiber quantification
cognitive outcome
diffusion tensor imaging
extended disability status scale
microscopic fractional anisotropy
multiple sclerosis
structural brain connectivity
Journal
Human brain mapping
ISSN: 1097-0193
Titre abrégé: Hum Brain Mapp
Pays: United States
ID NLM: 9419065
Informations de publication
Date de publication:
01 Jun 2024
01 Jun 2024
Historique:
revised:
19
04
2024
received:
11
12
2023
accepted:
25
04
2024
medline:
13
6
2024
pubmed:
13
6
2024
entrez:
13
6
2024
Statut:
ppublish
Résumé
We aimed to compare the ability of diffusion tensor imaging and multi-compartment spherical mean technique to detect focal tissue damage and in distinguishing between different connectivity patterns associated with varying clinical outcomes in multiple sclerosis (MS). Seventy-six people diagnosed with MS were scanned using a SIEMENS Prisma Fit 3T magnetic resonance imaging (MRI), employing both conventional (T1w and fluid-attenuated inversion recovery) and advanced diffusion MRI sequences from which fractional anisotropy (FA) and microscopic FA (μFA) maps were generated. Using automated fiber quantification (AFQ), we assessed diffusion profiles across multiple white matter (WM) pathways to measure the sensitivity of anisotropy diffusion metrics in detecting localized tissue damage. In parallel, we analyzed structural brain connectivity in a specific patient cohort to fully grasp its relationships with cognitive and physical clinical outcomes. This evaluation comprehensively considered different patient categories, including cognitively preserved (CP), mild cognitive deficits (MCD), and cognitively impaired (CI) for cognitive assessment, as well as groups distinguished by physical impact: those with mild disability (Expanded Disability Status Scale [EDSS] <=3) and those with moderate-severe disability (EDSS >3). In our initial objective, we employed Ridge regression to forecast the presence of focal MS lesions, comparing the performance of μFA and FA. μFA exhibited a stronger association with tissue damage and a higher predictive precision for focal MS lesions across the tracts, achieving an R-squared value of .57, significantly outperforming the R-squared value of .24 for FA (p-value <.001). In structural connectivity, μFA exhibited more pronounced differences than FA in response to alteration in both cognitive and physical clinical scores in terms of effect size and number of connections. Regarding cognitive groups, FA differences between CP and MCD groups were limited to 0.5% of connections, mainly around the thalamus, while μFA revealed changes in 2.5% of connections. In the CP and CI group comparison, which have noticeable cognitive differences, the disparity was 5.6% for FA values and 32.5% for μFA. Similarly, μFA outperformed FA in detecting WM changes between the MCD and CI groups, with 5% versus 0.3% of connections, respectively. When analyzing structural connectivity between physical disability groups, μFA still demonstrated superior performance over FA, disclosing a 2.1% difference in connectivity between regions closely associated with physical disability in MS. In contrast, FA spotted a few regions, comprising only 0.6% of total connections. In summary, μFA emerged as a more effective tool than FA in predicting MS lesions and identifying structural changes across patients with different degrees of cognitive and global disability, offering deeper insights into the complexities of MS-related impairments.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
e26706Subventions
Organisme : Instituto Carlos III (ISCIII)
Organisme : Plan Estatal de Investigación Científica y Técnica y de Innovación
ID : PI15/00587
Organisme : Plan Estatal de Investigación Científica y Técnica y de Innovación
ID : PI18/01030
Organisme : Plan Estatal de Investigación Científica y Técnica y de Innovación
ID : PI21/01189
Organisme : Red Española de Esclerosis Múltiple
ID : RD16/0015/0002
Organisme : Red Española de Esclerosis Múltiple
ID : RD16/0015/0003
Organisme : Fundación Merck Salud
Organisme : Esclerosis Múltiple España (EME)-Red Española de EM (REEM)
ID : 2023
Informations de copyright
© 2024 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.
Références
Andersen, K. W., Lasič, S., Lundell, H., Nilsson, M., Topgaard, D., Sellebjerg, F., Szczepankiewicz, F., Siebner, H. R., Blinkenberg, M., & Dyrby, T. B. (2020). Disentangling white‐matter damage from physiological fibre orientation dispersion in multiple sclerosis. Brain Communications, 2, fcaa077.
Bagnato, F., Franco, G., Li, H., Kaden, E., Ye, F., Fan, R., Chen, A., Alexander, D. C., Smith, S. A., Dortch, R., & Xu, J. (2019). Probing axons using multi‐compartmental diffusion in multiple sclerosis. Annals of Clinical Translational Neurology, 6, 1595–1605.
Bain, J. S., Yeatman, J. D., Schurr, R., Rokem, A., & Mezer, A. A. (2019). Evaluating arcuate fasciculus laterality measurements across dataset and tractography pipelines. Human Brain Mapping, 40, 3695–3711.
Bakshi, R., Thompson, A. J., Rocca, M. A., Pelletier, D., Dousset, V., Barkhof, F., Inglese, M., Guttmann, C. R. G., Horsfield, M. A., & Filippi, M. (2008). MRI in multiple sclerosis: Current status and future prospects. Lancet Neurology, 7, 615–625.
Basser, P. J., Mattiello, J., & LeBihan, D. (1994). Estimation of the effective self‐diffusion tensor from the NMR spin echo. Journal of Magnetic Resonance. Series B, 103, 247–254.
Basser, P. J., & Pierpaoli, C. (1996). Microstructural and physiological features of tissues elucidated by quantitative‐diffusion‐tensor MRI. Journal of Magnetic Resonance. Series B, 111, 209–219.
Calvi, A., Carrasco, F. P., Tur, C., Chard, D. T., Stutters, J., De Angelis, F., John, N., Williams, T., Doshi, A., Samson, R. S., MacManus, D., Gandini Wheeler‐Kingshott, C. A., Ciccarelli, O., Chataway, J., Barkhof, F., & MS SMART Investigators. (2022). Association of slowly expanding lesions on MRI with disability in people with secondary progressive multiple sclerosis. Neurology, 98, e1783–e1793.
Coll, L., Pareto, D., Carbonell‐Mirabent, P., Cobo‐Calvo, Á., Arrambide, G., Vidal‐Jordana, Á., Comabella, M., Castilló, J., Rodríguez‐Acevedo, B., Zabalza, A., Galán, I., Midaglia, L., Nos, C., Salerno, A., Auger, C., Alberich, M., Río, J., Sastre‐Garriga, J., Oliver, A., … Tur, C. (2023). Deciphering multiple sclerosis disability with deep learning attention maps on clinical MRI. NeuroImage: Clinical, 38, 103376.
Estrada‐López, M., Reguera‐García, M. M., Pérez Rivera, F. J., & Molina, A. J. (2020). Physical disability and personality traits in multiple sclerosis. Multiple Sclerosis and Related Disorders, 37, 101465.
Filippi, M., Preziosa, P., Banwell, B. L., Barkhof, F., Ciccarelli, O., De Stefano, N., Geurts, J. J. G., Paul, F., Reich, D. S., Toosy, A. T., Traboulsee, A., Wattjes, M. P., Yousry, T. A., Gass, A., Lubetzki, C., Weinshenker, B. G., & Rocca, M. A. (2019). Assessment of lesions on magnetic resonance imaging in multiple sclerosis: Practical guidelines. Brain, 142, 1858–1875.
Friedman, N. P., & Robbins, T. W. (2021). The role of prefrontal cortex in cognitive control and executive function. Neuropsychopharmacology, 47, 72–89.
Gabilondo, I., Martínez‐Lapiscina, E. H., Martínez‐Heras, E., Fraga‐Pumar, E., Llufriu, S., Ortiz, S., Bullich, S., Sepulveda, M., Falcon, C., Berenguer, J., Saiz, A., Sanchez‐Dalmau, B., & Villoslada, P. (2014). Trans‐synaptic axonal degeneration in the visual pathway in multiple sclerosis. Annals of Neurology, 75, 98–107.
Greve, D. N., & Fischl, B. (2009). Accurate and robust brain image alignment using boundary‐based registration. NeuroImage, 48, 63–72.
Jeurissen, B., Tournier, J.‐D., Dhollander, T., Connelly, A., & Sijbers, J. (2014). Multi‐tissue constrained spherical deconvolution for improved analysis of multi‐shell diffusion MRI data. NeuroImage, 103, 411–426.
Kaden, E., Kelm, N. D., Carson, R. P., Does, M. D., & Alexander, D. C. (2016). Multi‐compartment microscopic diffusion imaging. NeuroImage, 139, 346–359.
Klein, A., Ghosh, S. S., Bao, F. S., Giard, J., Häme, Y., Stavsky, E., Lee, N., Rossa, B., Reuter, M., Chaibub Neto, E., & Keshavan, A. (2017). Mindboggling morphometry of human brains. PLoS Computational Biology, 13, e1005350.
Kurtzke, J. F. (1983). Rating neurologic impairment in multiple sclerosis: An Expanded Disability Status Scale (EDSS). Neurology, 33, 1444–1452.
Landman, B. A., Wan, H., Bogovic, J. A., Bazin, P.‐L., & Prince, J. L. (2010). Resolution of crossing fibers with constrained compressed sensing using traditional diffusion tensor MRI. Proceedings of SPIE the International Society for Optical Engineering, 7623, 76231H.
Lassmann, H., Brück, W., & Lucchinetti, C. F. (2007). The immunopathology of multiple sclerosis: An overview. Brain Pathology, 17, 210–218.
Lipp, I., Parker, G. D., Tallantyre, E. C., Goodall, A., Grama, S., Patitucci, E., Heveron, P., Tomassini, V., & Jones, D. K. (2020). Tractography in the presence of multiple sclerosis lesions. NeuroImage, 209, 116471.
Llufriu, S., Blanco, Y., Martinez‐Heras, E., Casanova‐Molla, J., Gabilondo, I., Sepulveda, M., Falcon, C., Berenguer, J., Bargallo, N., Villoslada, P., Graus, F., Valls‐Sole, J., & Saiz, A. (2012). Influence of corpus callosum damage on cognition and physical disability in multiple sclerosis: A multimodal study. PLoS One, 7, e37167.
Llufriu, S., Martinez‐Heras, E., Solana, E., Sola‐Valls, N., Sepulveda, M., Blanco, Y., Martinez‐Lapiscina, E. H., Andorra, M., Villoslada, P., Prats‐Galino, A., & Saiz, A. (2017). Structural networks involved in attention and executive functions in multiple sclerosis. NeuroImage: Clinical, 13, 288–296.
Lublin, F. D., Reingold, S. C., Cohen, J. A., Cutter, G. R., Sørensen, P. S., Thompson, A. J., Wolinsky, J. S., Balcer, L. J., Banwell, B., Barkhof, F., Bebo, B., Jr., Calabresi, P. A., Clanet, M., Comi, G., Fox, R. J., Freedman, M. S., Goodman, A. D., Inglese, M., Kappos, L., … Polman, C. H. (2014). Defining the clinical course of multiple sclerosis: The 2013 revisions. Neurology, 83, 278–286.
Martinez‐Heras, E., Grussu, F., Prados, F., Solana, E., & Llufriu, S. (2021). Diffusion‐weighted imaging: Recent advances and applications. Seminars in Ultrasound, CT, and MR, 42, 490–506.
Martínez‐Heras, E., Solana, E., Prados, F., Andorrà, M., Solanes, A., López‐Soley, E., Montejo, C., Pulido‐Valdeolivas, I., Alba‐Arbalat, S., Sola‐Valls, N., Sepúlveda, M., Blanco, Y., Saiz, A., Radua, J., & Llufriu, S. (2020). Characterization of multiple sclerosis lesions with distinct clinical correlates through quantitative diffusion MRI. NeuroImage: Clinical, 28, 102411.
Martinez‐Heras, E., Solana, E., Vivó, F., Lopez‐Soley, E., Calvi, A., Alba‐Arbalat, S., Schoonheim, M. M., Strijbis, E. M., Vrenken, H., Barkhof, F., Rocca, M. A., Filippi, M., Pagani, E., Groppa, S., Fleischer, V., Dineen, R. A., Bellenberg, B., Lukas, C., Pareto, D., … Llufriu, S. (2023). Diffusion‐based structural connectivity patterns of multiple sclerosis phenotypes. Journal of Neurology, Neurosurgery, and Psychiatry, 94, 916–923.
Martínez‐Heras, E., Varriano, F., Prčkovska, V., Laredo, C., Andorrà, M., Martínez‐Lapiscina, E. H., Calvo, A., Lampert, E., Villoslada, P., Saiz, A., Prats‐Galino, A., & Llufriu, S. (2015). Improved framework for tractography reconstruction of the optic radiation. PLoS One, 10, e0137064.
Meijer, K. A., Steenwijk, M. D., Douw, L., Schoonheim, M. M., & Geurts, J. J. G. (2020). Long‐range connections are more severely damaged and relevant for cognition in multiple sclerosis. Brain, 143, 150–160.
Parmenter, B. A., Weinstock‐Guttman, B., Garg, N., Munschauer, F., & Benedict, R. H. B. (2007). Screening for cognitive impairment in multiple sclerosis using the symbol digit modalities test. Multiple Sclerosis, 13, 52–57.
Patenaude, B., Smith, S. M., Kennedy, D. N., & Jenkinson, M. (2011). A Bayesian model of shape and appearance for subcortical brain segmentation. NeuroImage, 56, 907–922.
Reid, L. B., Sale, M. V., Cunnington, R., Mattingley, J. B., & Rose, S. E. (2017). Brain changes following four weeks of unimanual motor training: Evidence from fMRI‐guided diffusion MRI tractography. Human Brain Mapping, 38, 4302–4312.
Sbardella, E., Tona, F., Petsas, N., & Pantano, P. (2013). DTI measurements in multiple sclerosis: Evaluation of brain damage and clinical implications. Multiple Sclerosis International, 2013, 671730.
Sepulcre, J., Masdeu, J. C., Goñi, J., Arrondo, G., Vélez de Mendizábal, N., Bejarano, B., & Villoslada, P. (2009). Fatigue in multiple sclerosis is associated with the disruption of frontal and parietal pathways. Multiple Sclerosis, 15, 337–344.
Shine, J. M., Lewis, L. D., Garrett, D. D., & Hwang, K. (2023). The impact of the human thalamus on brain‐wide information processing. Nature Reviews. Neuroscience, 24, 416–430.
Smith, R. E., Tournier, J.‐D., Calamante, F., & Connelly, A. (2012). Anatomically‐constrained tractography: Improved diffusion MRI streamlines tractography through effective use of anatomical information. NeuroImage, 62, 1924–1938.
Solana, E., Martinez‐Heras, E., Casas‐Roma, J., Calvet, L., Lopez‐Soley, E., Sepulveda, M., Sola‐Valls, N., Montejo, C., Blanco, Y., Pulido‐Valdeolivas, I., Andorra, M., Saiz, A., Prados, F., & Llufriu, S. (2019). Modified connectivity of vulnerable brain nodes in multiple sclerosis, their impact on cognition and their discriminative value. Scientific Reports, 9, 20172.
Solana, E., Martinez‐Heras, E., Martinez‐Lapiscina, E. H., Sepulveda, M., Sola‐Valls, N., Bargalló, N., Berenguer, J., Blanco, Y., Andorra, M., Pulido‐Valdeolivas, I., Zubizarreta, I., Saiz, A., & Llufriu, S. (2018). Magnetic resonance markers of tissue damage related to connectivity disruption in multiple sclerosis. NeuroImage: Clinical, 20, 161–168.
Strober, L. B., Bruce, J. M., Arnett, P. A., Alschuler, K. N., Lebkuecher, A., Di Benedetto, M., Cozart, J., Thelen, J., Guty, E., & Roman, C. (2020). A new look at an old test: Normative data of the symbol digit modalities test‐oral version. Multiple Sclerosis and Related Disorders, 43, 102154.
Sumowski, J. F., Benedict, R., Enzinger, C., Filippi, M., Geurts, J. J., Hamalainen, P., Hulst, H., Inglese, M., Leavitt, V. M., Rocca, M. A., Rosti‐Otajarvi, E. M., & Rao, S. (2018). Cognition in multiple sclerosis: State of the field and priorities for the future. Neurology, 90, 278–288.
Thompson, A. J., Banwell, B. L., Barkhof, F., Carroll, W. M., Coetzee, T., Comi, G., Correale, J., Fazekas, F., Filippi, M., Freedman, M. S., Fujihara, K., Galetta, S. L., Hartung, H. P., Kappos, L., Lublin, F. D., Marrie, R. A., Miller, A. E., Miller, D. H., Montalban, X., … Cohen, J. A. (2018). Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria. Lancet Neurology, 17, 162–173.
Tournier, J.‐D., Smith, R., Raffelt, D., Tabbara, R., Dhollander, T., Pietsch, M., Christiaens, D., Jeurissen, B., Yeh, C.‐H., & Connelly, A. (2019). MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation. NeuroImage, 202, 116137.
Wasserthal, J., Neher, P., & Maier‐Hein, K. H. (2018). TractSeg—Fast and accurate white matter tract segmentation. NeuroImage, 183, 239–253.
Wiegell, M. R., Larsson, H. B., & Wedeen, V. J. (2000). Fiber crossing in human brain depicted with diffusion tensor MR imaging. Radiology, 217, 897–903.
Yeatman, J. D., Dougherty, R. F., Myall, N. J., Wandell, B. A., & Feldman, H. M. (2012). Tract profiles of white matter properties: Automating fiber‐tract quantification. PLoS One, 7, e49790.
Zhang, J., Cortese, R., De Stefano, N., & Giorgio, A. (2021). Structural and functional connectivity substrates of cognitive impairment in multiple sclerosis. Frontiers in Neurology, 12, 671894.