Hostname: page-component-586b7cd67f-gb8f7 Total loading time: 0 Render date: 2024-11-24T08:38:20.424Z Has data issue: false hasContentIssue false

Brain Reserve in a Case of Cognitive Resilience to Severe Leukoaraiosis

Published online by Cambridge University Press:  16 June 2020

Dana M. Szeles
Affiliation:
Department of Neurology, Medical University of South Carolina, Charleston, SC29425, USA
Nicholas J. Milano
Affiliation:
Department of Neurology, Medical University of South Carolina, Charleston, SC29425, USA
Hunter J. Moss
Affiliation:
Department of Neurosciences, Medical University of South Carolina, Charleston, SC29425, USA
Maria Vittoria Spampinato
Affiliation:
Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC29425, USA
Jens H. Jensen
Affiliation:
Department of Neurosciences, Medical University of South Carolina, Charleston, SC29425, USA Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC29425, USA
Andreana Benitez*
Affiliation:
Department of Neurology, Medical University of South Carolina, Charleston, SC29425, USA Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC29425, USA
*
*Correspondence and reprint requests to: Andreana Benitez, PhD, 96 Jonathan Lucas St, MSC 606, Charleston, SC29425, USA; Tel. +1 (843) 876-2479; E-mail: [email protected]

Abstract

Objective:

Leukoaraiosis, or white matter rarefaction, is a common imaging finding in aging and is presumed to reflect vascular disease. When severe in presentation, potential congenital or acquired etiologies are investigated, prompting referral for neuropsychological evaluation in addition to neuroimaging. T2-weighted imaging is the most common magnetic resonance imaging (MRI) approach to identifying white matter disease. However, more advanced diffusion MRI techniques may provide additional insight into mechanisms that influence the abnormal T2 signal, especially when clinical presentations are discrepant with imaging findings.

Method:

We present a case of a 74-year-old woman with severe leukoaraoisis. She was examined by a neurologist, neuropsychologist, and rheumatologist, and completed conventional (T1, T2-FLAIR) MRI, diffusion tensor imaging (DTI), and advanced single-shell, high b-value diffusion MRI (i.e., fiber ball imaging [FBI]).

Results:

The patient was found to have few neurological signs, no significant cognitive impairment, a negative workup for leukoencephalopathy, and a positive antibody for Sjogren’s disease for which her degree of leukoaraiosis would be highly atypical. Tractography results indicate intact axonal architecture that was better resolved using FBI rather than DTI.

Conclusions:

This case illustrates exceptional cognitive resilience in the face of severe leukoaraiosis and the potential for advanced diffusion MRI to identify brain reserve.

Type
Case Report
Copyright
Copyright © INS. Published by Cambridge University Press, 2020

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Abraham, H. M. A., Wolfson, L., Moscufo, N., Guttmann, C. R. G., Kaplan, R. F., & White, W. B. (2016). Cardiovascular risk factors and small vessel disease of the brain: blood pressure, white matter lesions, and functional decline in older persons. Journal of Cerebral Blood Flow and Metabolism: Official Journal of the International Society of Cerebral Blood Flow and Metabolism, 36(1), 132142.CrossRefGoogle ScholarPubMed
Campbell, J. S. W. & Pike, G. B. (2014). Potential and limitations of diffusion MRI tractography for the study of language. Brain and Language, 131, 6573.CrossRefGoogle Scholar
Cox, S. R., Lyall, D. M., Ritchie, S. J., Bastin, M. E., Harris, M. A., Buchanan, C. R., … Deary, I. J. (2019). Associations between vascular risk factors and brain MRI indices in UK Biobank. European Heart Journal, 40(28), 22902300.CrossRefGoogle ScholarPubMed
Di Donato, I., Bianchi, S., De Stefano, N., Dichgans, M., Dotti, M. T., Duering, M., … Markus, H. S. (2017). Cerebral Autosomal Dominant Arteriopathy with Subcortical Infarcts and Leukoencephalopathy (CADASIL) as a model of small vessel disease: update on clinical, diagnostic, and management aspects. BMC Medicine, 15(1), 41.CrossRefGoogle ScholarPubMed
Duning, T., Kugel, H., & Knecht, S. (2005). Excellent cognitive performance despite massive cerebral white matter changes. Neuroradiology, 47(10), 749752.CrossRefGoogle ScholarPubMed
Dyrby, T. B., Innocenti, G. M., Bech, M., & Lundell, H. (2018). Validation strategies for the interpretation of microstructure imaging using diffusion MRI. NeuroImage, 182, 6279.CrossRefGoogle ScholarPubMed
Fieremans, E. & Lee, H.-H. (2018). Physical and numerical phantoms for the validation of brain microstructural MRI: a cookbook. NeuroImage, 182, 3961.CrossRefGoogle ScholarPubMed
Filley, C. M., McConnell, B. V., & Anderson, C. A. (2017). The expanding prominence of toxic leukoencephalopathy. The Journal of Neuropsychiatry and Clinical Neurosciences, 29(4), 308318.CrossRefGoogle ScholarPubMed
Frisoni, G. B., Galluzzi, S., Pantoni, L., & Filippi, M. (2007). The effect of white matter lesions on cognition in the elderly—small but detectable. Nature Reviews Neurology, 3(11), 620.Google ScholarPubMed
Hageman, A. T. M., Gabreels, F. J. M., De Jong, J. G. N., Gabreels-Festen, A. A. W. M., Van den Berg, C. J. M. G., Van Oost, B. A., & Wevers, R. A. (1995). Clinical symptoms of adult metachromatic leukodystrophy and arylsulfatase a pseudodeficiency. Archives of Neurology, 52(4), 408413.CrossRefGoogle ScholarPubMed
Jeerakathil, T., Wolf, P. A., Beiser, A., Massaro, J., Seshadri, S., D’Agostino, R. B., & DeCarli, C. (2004). Stroke risk profile predicts white matter hyperintensity volume: the Framingham Study. Stroke, 35(8), 18571861.CrossRefGoogle ScholarPubMed
Jensen, J. H., Glenn, G. R., & Helpern, J. A. (2016). Fiber ball imaging. Neuroimage, 124, 824833.CrossRefGoogle ScholarPubMed
Jones, D. K. & Cercignani, M. (2010). Twenty-five pitfalls in the analysis of diffusion MRI data. NMR in Biomedicine, 23(7), 803820.CrossRefGoogle ScholarPubMed
Joutel, A., Corpechot, C., Ducros, A., & Vahedi, K. (1996). Notch3 mutations in CADASIL, a hereditary adult-onset condition causing stroke and dementia. Nature, 383(6602), 707.CrossRefGoogle ScholarPubMed
Kaup, A. R., Xia, F., Launer, L. J., Sidney, S., Nasrallah, I., Erus, G., … Yaffe, K. (2018). Occupational cognitive complexity in earlier adulthood is associated with brain structure and cognitive health in midlife: the CARDIA study. Neuropsychology, 32(8), 895905.CrossRefGoogle ScholarPubMed
Köhler, W., Curiel, J., & Vanderver, A. (2018). Adulthood leukodystrophies. Nature Reviews Neurology, 14(2), 94105.CrossRefGoogle ScholarPubMed
Le Bihan, D. (2013). Apparent diffusion coefficient and beyond: What diffusion MR imaging can tell us about tissue structure. Radiology, 268(2), 318322.CrossRefGoogle ScholarPubMed
Maillard, P., Carmichael, O. T., Reed, B., Mungas, D., & DeCarli, C. (2015). Cooccurrence of vascular risk factors and late-life white-matter integrity changes. Neurobiology of Aging, 36(4), 16701677.CrossRefGoogle ScholarPubMed
McCoy, S. S. & Baer, A. N. (2017). Neurological complications of Sjögren’s syndrome: diagnosis and management. Current Treatment Options in Rheumatology, 3(4), 275288.CrossRefGoogle ScholarPubMed
Morgen, K., McFarland, H. F., & Pillemer, S. R. (2004). Central nervous system disease in primary Sjogrens syndrome: The role of magnetic resonance imaging. Seminars in Arthritis and Rheumatism, 34(3), 623630.CrossRefGoogle ScholarPubMed
Moss, H., McKinnon, E. T., Glenn, G. R., Helpern, J. A., & Jensen, J. H. (2019). Optimization of data acquisition and analysis for fiber ball imaging. NeuroImage, 200, 690703.CrossRefGoogle ScholarPubMed
Nozaki, H., Nishizawa, M., & Onodera, O. (2014). Features of cerebral autosomal recessive arteriopathy with subcortical infarcts and leukoencephalopathy. Stroke, 45(11), 34473453.CrossRefGoogle ScholarPubMed
Nyberg, L., Lövdén, M., Riklund, K., Lindenberger, U., & Bäckman, L. (2012). Memory aging and brain maintenance. Trends in Cognitive Sciences, 16(5), 292305.CrossRefGoogle ScholarPubMed
Potvin, O., Dieumegarde, L., & Duchesne, S. (2017). Normative morphometric data for cerebral cortical areas over the lifetime of the adult human brain. NeuroImage, 156, 315339.CrossRefGoogle ScholarPubMed
Ross, E. D., Hansel, S. L., Orbelo, D. M., & Monnot, M. (2005). Relationship of leukoaraiosis to cognitive decline and cognitive aging. Cognitive and Behavioral Neurology, 18(2), 8997.CrossRefGoogle ScholarPubMed
Smith, E. E., Saposnik, G., Biessels, G. J., Doubal, F. N., Fornage, M., Gorelick, P. B., … Seshadri, S. (2017). Prevention of stroke in patients with silent cerebrovascular disease: a scientific statement for healthcare professionals from the American Heart Association/American Stroke Association. Stroke, 48(2), e44e71.CrossRefGoogle ScholarPubMed
Solé-Padullés, C., Bartrés-Faz, D., Junqué, C., Vendrell, P., Rami, L., Clemente, I. C., … Barrios, M. (2009). Brain structure and function related to cognitive reserve variables in normal aging, mild cognitive impairment and Alzheimer’s disease. Neurobiology of Aging, 30(7), 11141124.CrossRefGoogle ScholarPubMed
Stern, Y., Arenaza-Urquijo, E. M., Bartrés-Faz, D., Belleville, S., Cantilon, M., Chetelat, G., … Reserve, Resilience and Protective Factors PIA Empirical Definitions and Conceptual Frameworks Workgroup. (2018). Whitepaper: Defining and investigating cognitive reserve, brain reserve, and brain maintenance. Alzheimer’s & Dementia: The Journal of the Alzheimer’s Association. doi: 10.1016/j.jalz.2018.07.219Google Scholar
Suzuki, H., Gao, H., Bai, W., Evangelou, E., Glocker, B., O’Regan, D. P., … Matthews, P. M. (2017). Abnormal brain white matter microstructure is associated with both pre-hypertension and hypertension. PLOS ONE, 12(11), e0187600.CrossRefGoogle ScholarPubMed
Vanderver, A., Prust, M., Tonduti, D., Mochel, F., Hussey, H. M., Helman, G., … Rodriguez, D. (2015). Case definition and classification of leukodystrophies and leukoencephalopathies. Molecular Genetics and Metabolism, 114(4), 494500.CrossRefGoogle ScholarPubMed
Wardlaw, J. M., Smith, C., & Dichgans, M. (2019). Small vessel disease: mechanisms and clinical implications. The Lancet Neurology, 18(7), 684696.CrossRefGoogle ScholarPubMed
Wolf, M. & Bowers, P. G. (1999). The double-deficit hypothesis for the developmental dyslexias. Journal of Educational Psychology, 91(3), 415.CrossRefGoogle Scholar
Supplementary material: File

Szeles et al. supplementary material

Szeles et al. supplementary material

Download Szeles et al. supplementary material(File)
File 21.5 KB