Structural connectivity-based predictors of cognitive impairment in stroke patients attributable to aging

. 2023 ; 18 (4) : e0280892. [epub] 20230414

Jazyk angličtina Země Spojené státy americké Médium electronic-ecollection

Typ dokumentu časopisecké články, práce podpořená grantem

Perzistentní odkaz   https://www.medvik.cz/link/pmid37058495

Despite the rising global burden of stroke and its socio-economic implications, the neuroimaging predictors of subsequent cognitive impairment are still poorly understood. We address this issue by studying the relationship of white matter integrity assessed within ten days after stroke and patients' cognitive status one year after the attack. Using diffusion-weighted imaging, we apply the Tract-Based Spatial Statistics analysis and construct individual structural connectivity matrices by employing deterministic tractography. We further quantify the graph-theoretical properties of individual networks. The Tract-Based Spatial Statistic did identify lower fractional anisotropy as a predictor of cognitive status, although this effect was mostly attributable to the age-related white matter integrity decline. We further observed the effect of age propagating into other levels of analysis. Specifically, in the structural connectivity approach we identified pairs of regions significantly correlated with clinical scales, namely memory, attention, and visuospatial functions. However, none of them persisted after the age correction. Finally, the graph-theoretical measures appeared to be more robust towards the effect of age, but still were not sensitive enough to capture a relationship with clinical scales. In conclusion, the effect of age is a dominant confounder especially in older cohorts, and unless appropriately addressed, may falsely drive the results of the predictive modelling.

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Krishnamurthi RV, Ikeda T, Feigin VL. Global, Regional and Country-Specific Burden of Ischaemic Stroke, Intracerebral Haemorrhage and Subarachnoid Haemorrhage: A Systematic Analysis of the Global Burden of Disease Study 2017. Neuroepidemiology. 2020;54(2):171–179. doi: 10.1159/000506396 PubMed DOI

Kim J, Thayabaranathan T, Donnan GA, Howard G, Howard VJ, Rothwell PM, et al.. Global Stroke Statistics 2019. International Journal of Stroke. 2020; p. 1747493020909545. PubMed

WHO MONICA Project Principal Investigators. The world health organization monica project (monitoring trends and determinants in cardiovascular disease): A major international collaboration. Journal of Clinical Epidemiology. 1988;41(2):105–114. doi: 10.1016/0895-4356(88)90084-4 PubMed DOI

Davenport R J, Dennis M S, Wellwood I, Warlow C P. Complications After Acute Stroke. Stroke. 1996;27(3):415–420. PubMed

Jørgensen HS, Nakayama H, Raaschou HO, Olsen TS. Recovery of walking function in stroke patients: The copenhagen stroke study. Archives of Physical Medicine and Rehabilitation. 1995;76(1):27–32. doi: 10.1016/S0003-9993(95)80038-7 PubMed DOI

Barker William H, Mullooly John P. Stroke in a Defined Elderly Population, 1967-1985. Stroke. 1997;28(2):284–290. doi: 10.1161/01.STR.28.2.284 PubMed DOI

Langhorne P, Coupar F, Pollock A. Motor recovery after stroke: a systematic review. The Lancet Neurology. 2009;8(8):741–754. doi: 10.1016/S1474-4422(09)70150-4 PubMed DOI

Langhorne P, Bernhardt J, Kwakkel G. Stroke rehabilitation. The Lancet. 2011;377(9778):1693–1702. doi: 10.1016/S0140-6736(11)60325-5 PubMed DOI

Rosso C, Lamy JC. Prediction of motor recovery after stroke: being pragmatic or innovative? Current Opinion in Neurology. 2020;33(4):482–487. doi: 10.1097/WCO.0000000000000843 PubMed DOI

Stinear Cathy M, Byblow Winston D, Ackerley Suzanne J, Barber P Alan, Smith Marie-Claire. Predicting Recovery Potential for Individual Stroke Patients Increases Rehabilitation Efficiency. Stroke. 2017;48(4):1011–1019. doi: 10.1161/STROKEAHA.116.015790 PubMed DOI

Tatemichi TK, Desmond DW, Stern Y, Paik M, Sano M, Bagiella E. Cognitive impairment after stroke: frequency, patterns, and relationship to functional abilities. Journal of Neurology, Neurosurgery & Psychiatry. 1994;57(2):202–207. doi: 10.1136/jnnp.57.2.202 PubMed DOI PMC

Seghier ML, Patel E, Prejawa S, Ramsden S, Selmer A, Lim L, et al.. The PLORAS Database: A data repository for Predicting Language Outcome and Recovery After Stroke. NeuroImage. 2016;124:1208–1212. doi: 10.1016/j.neuroimage.2015.03.083 PubMed DOI PMC

Nys GMS, van Zandvoort MJE, de Kort PLM, Jansen BPW, Kappelle LJ, de Haan EHF. Restrictions of the Mini-Mental State Examination in acute stroke. Archives of Clinical Neuropsychology. 2005;20(5):623–629. doi: 10.1016/j.acn.2005.04.001 PubMed DOI

Nair Rd, Cogger H, Worthington E, Lincoln NB. Cognitive rehabilitation for memory deficits after stroke. Cochrane Database of Systematic Reviews. 2016;(9). doi: 10.1002/14651858.CD002293.pub3 PubMed DOI PMC

Loetscher T, Potter KJ, Wong D, Nair Rd. Cognitive rehabilitation for attention deficits following stroke. Cochrane Database of Systematic Reviews. 2019;(11). doi: 10.1002/14651858.CD002842.pub3 PubMed DOI PMC

Bowen A, Hazelton C, Pollock A, Lincoln NB. Cognitive rehabilitation for spatial neglect following stroke. Cochrane Database of Systematic Reviews. 2013;(7). doi: 10.1002/14651858.CD003586.pub3 PubMed DOI PMC

Cicerone KD, Goldin Y, Ganci K, Rosenbaum A, Wethe JV, Langenbahn DM, et al.. Evidence-Based Cognitive Rehabilitation: Systematic Review of the Literature From 2009 Through 2014. Archives of Physical Medicine and Rehabilitation. 2019;100(8):1515–1533. doi: 10.1016/j.apmr.2019.02.011 PubMed DOI

Rajsic S, Gothe H, Borba HH, Sroczynski G, Vujicic J, Toell T, et al.. Economic burden of stroke: a systematic review on post-stroke care. The European Journal of Health Economics. 2019;20(1):107–134. doi: 10.1007/s10198-018-0984-0 PubMed DOI

Wang Y, Liu G, Hong D, Chen F, Ji X, Cao G. White matter injury in ischemic stroke. Progress in neurobiology. 2016;141:45–60. doi: 10.1016/j.pneurobio.2016.04.005 PubMed DOI PMC

Schaapsmeerders Pauline, Tuladhar Anil M, Arntz Renate M, Sieske Franssen, Maaijwee Noortje A M, Rutten-Jacobs Loes C A, et al.. Remote Lower White Matter Integrity Increases the Risk of Long-Term Cognitive Impairment After Ischemic Stroke in Young Adults. Stroke. 2016;47(10):2517–2525. doi: 10.1161/STROKEAHA.116.014356 PubMed DOI

Dacosta-Aguayo R, Graña M, Fernández-Andújar M, López-Cancio E, Cáceres C, Bargalló N, et al.. Structural Integrity of the Contralesional Hemisphere Predicts Cognitive Impairment in Ischemic Stroke at Three Months. PLOS ONE. 2014;9(1):e86119. doi: 10.1371/journal.pone.0086119 PubMed DOI PMC

Etherton MR, Wu O, Cougo P, Giese AK, Cloonan L, Fitzpatrick KM, et al.. Integrity of normal-appearing white matter and functional outcomes after acute ischemic stroke. Neurology. 2017;88(18):1701–1708. doi: 10.1212/WNL.0000000000003890 PubMed DOI PMC

Fernández-Andújar M, Doornink F, Dacosta-Aguayo R, Soriano-Raya JJ, Miralbell J, Bargalló N, et al.. Remote thalamic microstructural abnormalities related to cognitive function in ischemic stroke patients. Neuropsychology. 2014;28(6):984–996. doi: 10.1037/neu0000087 PubMed DOI

Keser Z, Meier E, Stockbridge M, Breining B, Sebastian R, Hillis A, et al.. Thalamic nuclei and thalamocortical pathways after left hemispheric stroke and their association with picture naming. Brain connectivity. 2021;11(7):553–565. doi: 10.1089/brain.2020.0831 PubMed DOI PMC

Bullmore E, Sporns O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience. 2009;10(3):186–198. doi: 10.1038/nrn2575 PubMed DOI

Achard S, Bullmore E. Efficiency and Cost of Economical Brain Functional Networks. PLOS Computational Biology. 2007;3(2):e17. doi: 10.1371/journal.pcbi.0030017 PubMed DOI PMC

Meunier D, Lambiotte R, Fornito A, Ersche K, Bullmore ET. Hierarchical modularity in human brain functional networks. Frontiers in Neuroinformatics. 2009;3. doi: 10.3389/neuro.11.037.2009 PubMed DOI PMC

Bullmore E, Sporns O. The economy of brain network organization. Nature Reviews Neuroscience. 2012;13(5):336–349. doi: 10.1038/nrn3214 PubMed DOI

Buldyrev SV, Parshani R, Paul G, Stanley HE, Havlin S. Catastrophic cascade of failures in interdependent networks. Nature. 2010;464(7291):1025–1028. doi: 10.1038/nature08932 PubMed DOI

Fornito A, Zalesky A, Breakspear M. The connectomics of brain disorders. Nature Reviews Neuroscience. 2015;16(3):159–172. doi: 10.1038/nrn3901 PubMed DOI

Bonilha L, Nesland T, Rorden C, Fillmore P, Ratnayake RP, Fridriksson J. Mapping Remote Subcortical Ramifications of Injury after Ischemic Strokes. Behavioural Neurology. 2014;2014:e215380. doi: 10.1155/2014/215380 PubMed DOI PMC

Yourganov G, Fridriksson J, Rorden C, Gleichgerrcht E, Bonilha L. Multivariate Connectome-Based Symptom Mapping in Post-Stroke Patients: Networks Supporting Language and Speech. Journal of Neuroscience. 2016;36(25):6668–6679. doi: 10.1523/JNEUROSCI.4396-15.2016 PubMed DOI PMC

Kuceyeski A, Navi BB, Kamel H, Raj A, Relkin N, Toglia J, et al.. Structural connectome disruption at baseline predicts 6-months post-stroke outcome. Human Brain Mapping. 2016;37(7):2587–2601. doi: 10.1002/hbm.23198 PubMed DOI PMC

Hope TMH, Leff AP, Price CJ. Predicting language outcomes after stroke: Is structural disconnection a useful predictor? NeuroImage: Clinical. 2018;19:22–29. doi: 10.1016/j.nicl.2018.03.037 PubMed DOI PMC

Saxena S, Keser Z, Rorden C, Bonilha L. Fridriksson J, Walker A, Hillis A. Disruptions of the human connectome associated with hemispatial neglect. Neurology: 2022;98:e107–e114. doi: 10.1212/WNL.0000000000013050 PubMed DOI PMC

Boccaletti S, Latora V, Moreno Y, Chavez M, Hwang DU. Complex networks: Structure and dynamics. Physics Reports. 2006;424(4):175–308. doi: 10.1016/j.physrep.2005.10.009 DOI

Rubinov M, Sporns O. Complex network measures of brain connectivity: uses and interpretations. NeuroImage. 2010;52:1059–69. doi: 10.1016/j.neuroimage.2009.10.003 PubMed DOI

Newman MEJ. Assortative Mixing in Networks. Physical Review Letters. 2002;89(20). doi: 10.1103/PhysRevLett.89.208701 PubMed DOI

Colizza V, Flammini A, Serrano MA, Vespignani A. Detecting rich-club ordering in complex networks. Nature Physics. 2006;2(2):110–115. doi: 10.1038/nphys209 DOI

Carter AR, Shulman GL, Corbetta M. Why use a connectivity-based approach to study stroke and recovery of function? NeuroImage. 2012;62(4):2271–2280. doi: 10.1016/j.neuroimage.2012.02.070 PubMed DOI PMC

Lim JS, Kang DW. Stroke Connectome and Its Implications for Cognitive and Behavioral Sequela of Stroke. Journal of Stroke. 2015;17(3):256–267. doi: 10.5853/jos.2015.17.3.256 PubMed DOI PMC

Grefkes C, Fink GR. Connectivity-based approaches in stroke and recovery of function. The Lancet Neurology. 2014;13(2):206–216. doi: 10.1016/S1474-4422(13)70264-3 PubMed DOI

Horáková K, Štěpánková H, Bezdíček O, Kopeček M. Kontrolované učení ve starším věku. Československá psychologie (Czechoslovak Psychology). 2017;61(3):213–229.

Bezdicek O, Lukavsky J, Stepankova H, Nikolai T, Axelrod BN, Michalec J, et al.. The Prague Stroop Test: Normative standards in older Czech adults and discriminative validity for mild cognitive impairment in Parkinson’s disease. Journal of Clinical and Experimental Neuropsychology. 2015;37(8):794–807. doi: 10.1080/13803395.2015.1057106 PubMed DOI

Nikolai T, Stepankova H, Kopecek M, Sulc Z, Vyhnalek M, Bezdicek O. The Uniform Data Set, Czech Version: Normative Data in Older Adults from an International Perspective. Journal of Alzheimer’s Disease. 2018;61(3):1233–1240. doi: 10.3233/JAD-170595 PubMed DOI PMC

Drozdová K, Stepankova Georgi H, Lukavsky J, Bezdicek O, Kopecek M. Normative Data for the Rey- Osterrieth Complex Figure Test in Older Czech Adults. Česká a Slovenská neurologie a neurochirurgie. 2015;78/111:542–549.

Warrington EK, James M. The visual object and space perception battery. Bury St Edmunds: : Thames Valley Test Company; 1991.

Woolrich MW, Jbabdi S, Patenaude B, Chappell M, Makni S, Behrens T, et al.. Bayesian analysis of neuroimaging data in FSL. NeuroImage. 2009;45(1):S173–S186. doi: 10.1016/j.neuroimage.2008.10.055 PubMed DOI

Tournier JD, Smith R, Raffelt D, Tabbara R, Dhollander T, Pietsch M, et al.. MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation. NeuroImage. 2019;202:116137. doi: 10.1016/j.neuroimage.2019.116137 PubMed DOI

Veraart J, Novikov DS, Christiaens D, Ades-aron B, Sijbers J, Fieremans E. Denoising of diffusion MRI using random matrix theory. NeuroImage. 2016;142:394–406. doi: 10.1016/j.neuroimage.2016.08.016 PubMed DOI PMC

Veraart J, Fieremans E, Novikov DS. Diffusion MRI noise mapping using random matrix theory. Magnetic Resonance in Medicine. 2016;76(5):1582–1593. doi: 10.1002/mrm.26059 PubMed DOI PMC

Kellner E, Dhital B, Kiselev VG, Reisert M. Gibbs-ringing artifact removal based on local subvoxel-shifts. Magnetic Resonance in Medicine. 2016;76(5):1574–1581. doi: 10.1002/mrm.26054 PubMed DOI

Schilling K, Blaber J, Huo Y, Newton A, Hansen C, Nath V, et al.. Synthesized b0 for diffusion distortion correction (Synb0-DisCo) Magnetic resonance imaging. 2019;64:62–70. doi: 10.1016/j.mri.2019.05.008 PubMed DOI PMC

Andersson JL, Sotiropoulos SN. An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging. Neuroimage. 2016;125:1063–1078. doi: 10.1016/j.neuroimage.2015.10.019 PubMed DOI PMC

Smith SM, Jenkinson M, Johansen-Berg H, Rueckert D, Nichols TE, Mackay CE, et al.. Tract-based spatial statistics: Voxelwise analysis of multi-subject diffusion data. NeuroImage. 2006;31(4):1487–1505. doi: 10.1016/j.neuroimage.2006.02.024 PubMed DOI

Tax CMW, Jeurissen B, Vos SB, Viergever MA, Leemans A. Recursive calibration of the fiber response function for spherical deconvolution of diffusion MRI data. NeuroImage. 2014;86:67–80. doi: 10.1016/j.neuroimage.2013.07.067 PubMed DOI

Tournier JD, Calamante F, Connelly A. Robust determination of the fibre orientation distribution in diffusion MRI: Non-negativity constrained super-resolved spherical deconvolution. NeuroImage. 2007;35(4):1459–1472. doi: 10.1016/j.neuroimage.2007.02.016 PubMed DOI

Tournier JD, Calamante F, Connelly A. MRtrix: Diffusion tractography in crossing fiber regions. International Journal of Imaging Systems and Technology. 2012;22(1):53–66. doi: 10.1002/ima.22005 DOI

Sinha N, Wang Y, Dauwels J, Kaiser M, Thesen T, Forsyth R, et al.. Computer modelling of connectivity change suggests epileptogenesis mechanisms in idiopathic generalised epilepsy. NeuroImage: Clinical. 2019;21:101655. doi: 10.1016/j.nicl.2019.101655 PubMed DOI PMC

Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, Delcroix N, et al.. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. NeuroImage. 2002;15(1):273–289. doi: 10.1006/nimg.2001.0978 PubMed DOI

Smith SM, Nichols TE. Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference. Neuroimage. 2009;44(1):83–98. doi: 10.1016/j.neuroimage.2008.03.061 PubMed DOI

Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal statistical society: series B (Methodological). 1995;57(1):289–300. doi: 10.1111/j.2517-6161.1995.tb02031.x DOI

Rubinov M, Sporns O. Weight-conserving characterization of complex functional brain networks. NeuroImage. 2011;56(4):2068–2079. doi: 10.1016/j.neuroimage.2011.03.069 PubMed DOI

Alstott J, Panzarasa P, Rubinov M, Bullmore ET, Vértes PE. A Unifying Framework for Measuring Weighted Rich Clubs. Scientific Reports. 2014;4(1):7258. doi: 10.1038/srep07258 PubMed DOI PMC

Kuznetsova KA, Maniega SM, Ritchie SJ, Cox SR, Storkey AJ, Starr JM, et al.. Brain white matter structure and information processing speed in healthy older age. Brain Structure and Function. 2016;221(6):3223–3235. doi: 10.1007/s00429-015-1097-5 PubMed DOI PMC

Barnes J, Ridgway GR, Bartlett J, Henley SM, Lehmann M, Hobbs N, et al.. Head size, age and gender adjustment in MRI studies: a necessary nuisance? Neuroimage. 2010;53(4):1244–1255. doi: 10.1016/j.neuroimage.2010.06.025 PubMed DOI

Hyatt CS, Owens MM, Crowe ML, Carter NT, Lynam DR, Miller JD. The quandary of covarying: A brief review and empirical examination of covariate use in structural neuroimaging studies on psychological variables. Neuroimage. 2020;205:116225. doi: 10.1016/j.neuroimage.2019.116225 PubMed DOI

Fanny Munsch, Sharmila Sagnier, Julien Asselineau, Antoine Bigourdan, Guttmann Charles R, Sabrina Debruxelles, et al.. Stroke Location Is an Independent Predictor of Cognitive Outcome. Stroke. 2016;47(1):66–73. doi: 10.1161/STROKEAHA.115.011242 PubMed DOI

Zhao L, Biesbroek JM, Shi L, Liu W, Kuijf HJ, Chu WW, et al.. Strategic infarct location for post-stroke cognitive impairment: A multivariate lesion-symptom mapping study. Journal of Cerebral Blood Flow & Metabolism. 2018;38(8):1299–1311. doi: 10.1177/0271678X17728162 PubMed DOI PMC

Nys G, Van Zandvoort M, De Kort P, Jansen B, De Haan E, Kappelle L. Cognitive disorders in acute stroke: prevalence and clinical determinants. Cerebrovascular Diseases. 2007;23(5-6):408–416. doi: 10.1159/000101464 PubMed DOI

Jaillard A, Grand S, Le Bas JF, Hommel M. Predicting cognitive dysfunctioning in nondemented patients early after stroke. Cerebrovascular Diseases. 2010;29(5):415–423. doi: 10.1159/000289344 PubMed DOI

Zamboni G, Griffanti L, Jenkinson M, Mazzucco S, Li L, Küker W, et al.. White matter imaging correlates of early cognitive impairment detected by the montreal cognitive assessment after transient ischemic attack and minor stroke. Stroke. 2017;48(6):1539–1547. doi: 10.1161/STROKEAHA.116.016044 PubMed DOI

Del Gaizo J, Fridriksson J, Yourganov G, Hillis AE, Hickok G, Misic B, et al.. Mapping language networks using the structural and dynamic brain connectomes. Eneuro. 2017;4(5). doi: 10.1523/ENEURO.0204-17.2017 PubMed DOI PMC

van der Flier Wiesje M, van Straaten Elizabeth C W, Frederik Barkhof, Ana Verdelho, Sofia Madureira, Leonardo Pantoni, et al.. Small Vessel Disease and General Cognitive Function in Nondisabled Elderly. Stroke. 2005;36(10):2116–2120. doi: 10.1161/01.STR.0000179092.59909.42 PubMed DOI

Beaudet G, Tsuchida A, Petit L, Tzourio C, Caspers S, Schreiber J, et al.. Age-Related Changes of Peak Width Skeletonized Mean Diffusivity (PSMD) Across the Adult Lifespan: A Multi-Cohort Study Frontiers in Psychiatry. 2020;342. doi: 10.3389/fpsyt.2020.00342 PubMed DOI PMC

Behler A, Kassubek J, Muller H. Age-related alterations in DTI metrics in the human brain—consequences for age correction Frontiers in aging neuroscience. 2021;300. doi: 10.3389/fnagi.2021.682109 PubMed DOI PMC

Faizy T, Thaler C, Broocks G, Flottmann F, Leischner H, Kniep H, et al.. The Myelin Water Fraction Serves as a Marker for Age-Related Myelin Alterations in the Cerebral White Matter—A Multiparametric MRI Aging Study Frontiers in Neuroscience. 2022;14. doi: 10.3389/fnins.2020.00136 PubMed DOI PMC

Molloy C, Nugent S, Bokde A, Alterations in Diffusion Measures of White Matter Integrity Associated with Healthy Aging. The Journals of Gerontology: Series A. 2021;76(6):945–954. doi: 10.1093/gerona/glz289 PubMed DOI

Pustina D, Coslett HB, Ungar L, Faseyitan OK, Medaglia JD, Avants B, et al.. Enhanced estimations of post-stroke aphasia severity using stacked multimodal predictions. Human Brain Mapping. 2017;38(11):5603–5615. doi: 10.1002/hbm.23752 PubMed DOI PMC

Shi L, Wang D, Chu WC, Liu S, Xiong Y, Wang Y, et al.. Abnormal organization of white matter network in patients with no dementia after ischemic stroke. PloS one. 2013;8(12):e81388. doi: 10.1371/journal.pone.0081388 PubMed DOI PMC

Zhang J, Zhang Y, Wang L, Sang L, Yang J, Yan R, et al.. Disrupted structural and functional connectivity networks in ischemic stroke patients. Neuroscience. 2017;364:212–225. doi: 10.1016/j.neuroscience.2017.09.009 PubMed DOI

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