Fractional anisotropy changes in Alzheimer’s disease depend on the underlying fiber tract architecture: a multiparametric DTI study using joint independent component analysis
Open accessAbstract
Diffusion tensor imaging (DTI) allows the simultaneous measurement of several diffusion indices that provide complementary information on the substrate of white matter alterations in neurodegenerative diseases. These indices include fractional anisotropy (FA) as measure of fiber tract integrity, and the mode of anisotropy (Mode) reflecting differences in the shape of the diffusion tensor. We used a multivariate approach based on joint independent component analysis of FA and Mode in a large sample of 138 subjects with Alzheimer's disease (AD) dementia, 37 subjects with cerebrospinal fluid biomarker positive mild cognitive impairment (MCI-AD), and 153 healthy elderly controls from the European DTI Study on Dementia to comprehensively study alterations of microstructural white matter integrity in AD dementia and predementia AD. We found a parallel decrease of FA and Mode in intracortically projecting fiber tracts, and a parallel increase of FA and Mode in the corticospinal tract in AD patients compared to controls. Subjects with MCI-AD showed a similar, but spatially more restricted pattern of diffusion changes. Our findings suggest an early axonal degeneration in intracortical projecting fiber tracts in dementia and predementia stages of AD. An increase of Mode, parallel to an increase of FA, in the corticospinal tract suggests a more linear shape of diffusion due to loss of crossing fibers along relatively preserved cortico-petal and cortico-fugal fiber tracts in AD. Supporting this interpretation, we found three populations of fiber tracts, namely cortico-petal and cortico-fugal, commissural, and intrahemispherically projecting fiber tracts, in the peak area of parallel FA and Mode increase.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
DTI alterations observed in the corticospinal tract in the current study cannot be unambiguously attributed to that tract and may be due to alterations in crossing fibres such as the superior longitudinal fasciculus or corpus callosum.
“DTI alterations such as those observed in the corticospinal tract in the current study should be interpreted with caution, as the current TBSS approach cannot be unambiguously attributed to that tract, but potentially are because of alterations in crossing fibres such as the superior longitudinal fasciculus or corpus callosum (Douaud et al., 2011; Teipel et al., 2014; Mito et al., 2018).”
From White matter diffusion alterations precede symptom onset in autosomal dominant Alzheimer’s disease (2018) · cites this paper as Teipel et al., 2014
Voxel-based analysis utilising DTI models does not adequately estimate orientationally-sensitive diffusivity measures such as axial and radial diffusivity (AD and RD).
“Therefore, apart from the limited area of white matter occupied by coherent fiber tracts, voxel-based analysis utilising DTI models does not adequately estimate orientationally-sensitive diffusivity measures, such as axial and radial diffusivity (AD and RD) [12, 13].”
From Differentiating axonal loss and demyelination in chronic MS lesions: A novel approach using single streamline diffusivity analysis (2021) · cites this paper as [12]
Cites
Folstein, M. F., Folstein, S. E., & McHugh, P. R. (1975) · Journal of Psychiatric Research · doi:10.1016/0022-3956(75)90026-6 · Cited by 88,282 · see citing works
McKhann, G., Drachman, D., Folstein, M., Katzman, R., Price, D., & Stadlan, E. M. (1984) · Neurology · doi:10.1212/wnl.34.7.939 · Cited by 27,915 · see citing works
McKhann, G. M., Knopman, D. S., Chertkow, H., Hyman, B. T., Jack, C. R., Kawas, C. H., Klunk, W. E., Koroshetz, W. J., Manly, J. J., Mayeux, R., et al. (2011) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2011.03.005 · Cited by 18,657 · see citing works
Advances in functional and structural MR image analysis and implementation as FSL
Smith, S. M., Jenkinson, M., Woolrich, M. W., Beckmann, C. F., Behrens, T. E. J., Johansen-Berg, H., Bannister, P. R., De Luca, M., Drobnjak, I., Flitney, D. E., et al. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.07.051 · Cited by 13,630 · see citing works
The Clinical Dementia Rating (CDR): Current version and scoring rules
Morris, J. C. (1993) · Neurology · doi:10.1212/wnl.43.11.2412-a · Cited by 9,882 · see citing works
A fast diffeomorphic image registration algorithm
Ashburner, J. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.07.007 · Cited by 8,122 · see citing works
Mild cognitive impairment as a diagnostic entity
Petersen, R. C. (2004) · Journal of Internal Medicine · doi:10.1111/j.1365-2796.2004.01388.x · Cited by 7,864 · see citing works
Moms, J. C., Heyman, A., Mohs, R. C., Hughes, J. P., van Belle, G., Fillenbaum, G., Mellits, E. D., & Clark, C. (1989) · Neurology · doi:10.1212/wnl.39.9.1159 · Cited by 4,130 · see citing works
Hua, K., Zhang, J., Wakana, S., Jiang, H., Li, X., Reich, D. S., Calabresi, P. A., Pekar, J. J., van Zijl, P. C. M., & Mori, S. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.07.053 · Cited by 1,507 · see citing works
Induction of plasticity in the human motor cortex by paired associative stimulation
Stefan, K. (2000) · Brain · doi:10.1093/brain/123.3.572 · Cited by 1,484 · see citing works
Reduction of eddy‐current‐induced distortion in diffusion MRI using a twice‐refocused spin echo
Reese, T. G., Heid, O., Weisskoff, R. M., & Wedeen, V. J. (2002) · Magnetic Resonance in Medicine · doi:10.1002/mrm.10308 · Cited by 1,229 · see citing works
A review of group ICA for fMRI data and ICA for joint inference of imaging, genetic, and ERP data
Calhoun, V. D., Liu, J., & Adalı, T. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.10.057 · Cited by 1,148 · see citing works
Diffusion spectrum magnetic resonance imaging (DSI) tractography of crossing fibers
Wedeen, V. J., Wang, R., Schmahmann, J. D., Benner, T., Tseng, W. I., Dai, G., Pandya, D. Ν., Hagmann, P., D'Arceuil, H. E., & de Crespigny, A. J. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.03.036 · Cited by 941 · see citing works
About “axial” and “radial” diffusivities
Wheeler‐Kingshott, C. A. M., & Cercignani, M. (2009) · Magnetic Resonance in Medicine · doi:10.1002/mrm.21965 · Cited by 885 · see citing works
Douaud, G., Jbabdi, S., Behrens, T. E. J., Menke, R. A., Gass, A., Monsch, A. U., Rao, A., Whitcher, B., Kindlmann, G., Matthews, P. M., et al. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2010.12.008 · Cited by 514 · see citing works
Rose, S. (2000) · Journal of Neurology Neurosurgery & Psychiatry · doi:10.1136/jnnp.69.4.528 · Cited by 465 · see citing works
Bobinski, M., de Leon, M. J., Wegiel, J., DeSanti, S., Convit, A., Saint Louis, L. A., Rusinek, H., & Wisniewski✠, H. M. (1999) · Neuroscience · doi:10.1016/s0306-4522(99)00476-5 · Cited by 455 · see citing works
Focal Decline of Cortical Thickness in Alzheimer's Disease Identified by Computational Neuroanatomy
Lerch, J. P., Pruessner, J. C., Zijdenbos, A. P., Hampel, H. J., Teipel, S. J., & Evans, A. C. (2004) · Cerebral Cortex · doi:10.1093/cercor/bhh200 · Cited by 431 · see citing works
Unmixing fMRI with independent component analysis
Calhoun, V. D., & Adalı, T. (2006) · IEEE Engineering in Medicine and Biology Magazine · doi:10.1109/memb.2006.1607672 · Cited by 309 · see citing works
Stricker, N. H., Schweinsburg, B. C., Delano‐Wood, L., Wierenga, C. E., Bangen, K. J., Haaland, K. Y., Frank, L. R., Salmon, D. P., & Bondi, M. W. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.11.027 · Cited by 308 · see citing works
Biological parametric mapping: A statistical toolbox for multimodality brain image analysis
Casanova, R., Ryali, S., Baer, A., Laurienti, P. J., Burdette, J. H., Hayasaka, S., Flowers, L., Wood, F., & Maldjian, J. A. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2006.09.011 · Cited by 302 · see citing works
Orthogonal tensor invariants and the analysis of diffusion tensor magnetic resonance images
Ennis, D. B., & Kindlmann, G. (2006) · Magnetic Resonance in Medicine · doi:10.1002/mrm.20741 · Cited by 276 · see citing works
Motor cortex hyperexcitability to transcranial magnetic stimulation in Alzheimer's disease
Lazzaro, V. D. (2004) · Journal of Neurology Neurosurgery & Psychiatry · doi:10.1136/jnnp.2003.018127 · Cited by 274 · see citing works
Jing Sui (Beijing Normal University), my correct affiliation is beijing normal university, N. Q. U. O. S. A. T., Pearlson, G. D., Caprihan, A., Adalı, T., Kiehl, K. A., Liu, J., Yamamoto, J., & Calhoun, V. D. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.05.055 · Cited by 260 · see citing works
Mild cognitive impairment: pathology and mechanisms
Mufson, E. J., Binder, L. I., Counts, S., DeKosky, S. T., deToledo‐Morrell, L., Ginsberg, S. D., Ikonomović, M. D., Perez, S. E., & Scheff, S. W. (2011) · Acta Neuropathologica · doi:10.1007/s00401-011-0884-1 · Cited by 258 · see citing works
Liu, Y., Spulber, G., Lehtimäki, K. K., Könönen, M., Hallikainen, I., Gröhn, H., Kivipelto, M., Hallikainen, M., Vanninen, R., & Soininen, H. (2011) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2009.10.006 · Cited by 236 · see citing works
Christian Gaser, B., Volz, H., Kiebel, S. J., Riehemann, S., & Sauer, H. (1999) · NeuroImage · doi:10.1006/nimg.1999.0458 · Cited by 231 · see citing works
Regionally-specific diffusion tensor imaging in mild cognitive impairment and Alzheimer’s disease.
Mielke, M. M., Kozauer, N. A., Chan, K. C. G., George, M., Toroney, J., Zerrate, M., Bandeen-Roche, K., Wang, M.-C., vanZijl, P., & Pekar, J. J. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.01.054 · Cited by 225 · see citing works
A voxel-based morphometry study of temporal lobe gray matter reductions in Alzheimer’s disease
Busatto, G. F., Garrido, G. E. J., Almeida, O. P., de Castro, C. C., de Camargo, C. H. P., Cid, C. G., Buchpiguel, C. A., Furuie, S. S., & Bottino, C. M. D. C. (2002) · Neurobiology of Aging · doi:10.1016/s0197-4580(02)00084-2 · Cited by 217 · see citing works
Stahl, R., Dietrich, O., Teipel, S. J., Hampel, H. J., Reiser, M. F., & Schoenberg, S. O. (2007) · Radiology · doi:10.1148/radiol.2432051714 · Cited by 215 · see citing works
White matter microstructure underlying default mode network connectivity in the human brain
Teipel, S. J., Bokde, A. L. W., Meindl, T., Amaro, E., Soldner, J., Reiser, M. F., Herpertz, S. C., Möller, H., & Hampel, H. J. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.10.067 · Cited by 200 · see citing works
Teipel, S. J., Born, C., Ewers, M., Bokde, A. L. W., Reiser, M. F., Möller, H., & Hampel, H. J. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.07.008 · Cited by 200 · see citing works
Gray and white matter changes in Alzheimer's disease: a diffusion tensor imaging study
Rose, S. E., Janke PhD, A. L., & Chalk, J. B. (2008) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.21231 · Cited by 178 · see citing works
Multivariate network analysis of fiber tract integrity in Alzheimer’s disease
Teipel, S. J., Stahl, R., Dietrich, O., Schoenberg, S. O., Perneczky, R., Bokde, A. L. W., Reiser, M. F., Möller, H., & Hampel, H. J. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2006.07.047 · Cited by 169 · see citing works
Analysis of Multimodal Neuroimaging Data
Bießmann, F., Plis, S., Meinecke, F. C., Eichele, T., & Müller, K. (2011) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2011.2170675 · Cited by 156 · see citing works
Sui, J., He, H., Pearlson, G. D., Adali, T., Kiehl, K. A., Yu, Q., Clark, V. P., Castro, E., White, T., Mueller, B. A., et al. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2012.10.051 · Cited by 143 · see citing works
Diagnostic utility of hippocampal size and mean diffusivity in amnestic MCI.
Müller, M. J., Greverus, D., Weibrich, C., Dellani, P. R., Scheurich, A., Stoeter, P., & Fellgiebel, A. (2007) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2006.01.009 · Cited by 140 · see citing works
Dyrba, M., Ewers, M., Wegrzyn, M., Kilimann, I., Plant, C., Oswald, A., Meindl, T., Pievani, M., Bokde, A. L. W., Fellgiebel, A., et al. (2013) · PLoS ONE · doi:10.1371/journal.pone.0064925 · Cited by 121 · see citing works
Teipel, S. J., Reuter, S., Stieltjes, B., Acosta‐Cabronero, J., Ernemann, U., Fellgiebel, A., Filippi, M., Frisoni, G. B., Hentschel, F., Jessen, F., et al. (2011) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2011.05.012 · Cited by 107 · see citing works
Diffusion Tensor Imaging of the Hippocampus in MCI and Early Alzheimer's Disease
Fellgiebel, A., & Yakushev, I. Y. (2011) · Journal of Alzheimer s Disease · doi:10.3233/jad-2011-0001 · Cited by 104 · see citing works
Anatomical MRI and DTI in the Diagnosis of Alzheimer's Disease: A European Multicenter Study
Teipel, S. J., the EDSD study group, Wegrzyn, M., Meindl, T., Frisoni, G. B., Bokde, A. L. W., Fellgiebel, A., Filippi, M., Hampel, H. J., Klöppel, S., et al. (2012) · Journal of Alzheimer s Disease · doi:10.3233/jad-2012-112118 · Cited by 92 · see citing works
Pennisi, G., Ferri, R., Lanza, G., Cantone, M., Pennisi, M., Puglisi, V., Malaguarnera, G., & Bella, R. (2011) · Journal of Neural Transmission · doi:10.1007/s00702-010-0554-9 · Cited by 85 · see citing works
Joint source based morphometry identifies linked gray and white matter group differences
Xu, L., Pearlson, G. D., & Calhoun, V. D. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.09.051 · Cited by 72 · see citing works
Relationship between excitability, plasticity and thickness of the motor cortex in older adults
List, J., Kübke, J. C., Lindenberg, R., Külzow, N., Kerti, L., Witte, A. V., & Flöel, A. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.07.033 · Cited by 71 · see citing works
Preti, M. G., Baglio, F., Laganà, M. M., Griffanti, L., Nemni, R., Clerici, M., Bozzali, M., & Baselli, G. (2012) · PLoS ONE · doi:10.1371/journal.pone.0035856 · Cited by 56 · see citing works
Using joint ICA to link function and structure using MEG and DTI in schizophrenia
Stephen, J. M., Coffman, B. A., Jung, R. E., Bustillo, J., Aine, C. J., & Calhoun, V. D. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.06.038 · Cited by 54 · see citing works
Li, C., Wang, J., Gui, L., Zheng, J., Liu, C., & Du, H. (2011) · Journal of Alzheimer s Disease · doi:10.3233/jad-2011-110497 · Cited by 34 · see citing works
Automated tractography of the cingulate bundle in Alzheimer's disease: a multicenter DTI study
Fischer, F. U., Scheurich, A., Wegrzyn, M., Schermuly, I., Bokde, A. L. W., Klöppel, S., Pouwels, P. J. W., Teipel, S., Yakushev, I., & Fellgiebel, A. (2012) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.23621 · Cited by 34 · see citing works
Wegrzyn, M., Teipel, S. J., Oltmann, I., Bauer, A. J., Thome, J., Großmann, A., Hauenstein, K., & Höppner, J. (2012) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2012.04.008 · Cited by 31 · see citing works
Decline of fiber tract integrity over the adult age range: A diffusion spectrum imaging study
Teipel, S. J., Lerche, M., Kilimann, I., O’Brien, K., Grothe, M. J., Meyer, P., Li, X., Sänger, P., & Hauenstein, K. (2013) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.24420 · Cited by 15 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Aging of Cerebral White Matter
Liu, H., Yang, Y.-Y., Xia, Y., Zhu, W., Leak, R., Wei, Z., Wang, J.-Y., & Hu, X.-M. (2016) · Ageing Research Reviews · doi:10.1016/j.arr.2016.11.006 · Cited by 249 · see citing works
Szczepankiewicz, F., Lasič, S., van Westen, D., Sundgren, P. C., Englund, E. M., Westin, C., Ståhlberg, F., Lätt, J., Topgaard, D., & Nilsson, M. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.09.057 · Cited by 245 · see citing works
Common genetic variation influencing human white matter microstructure
Zhao, B., Li, T., Yang, Y., Wang, X., Luo, T., Shan, Y., Zhu, Z., Xiong, D., Hauberg, M. E., Bendl, J. M., et al. (2021) · Science · doi:10.1126/science.abf3736 · Cited by 217 · see citing works
White matter diffusion alterations precede symptom onset in autosomal dominant Alzheimer’s disease
Araque Caballero, M. Á., Suárez-Calvet, M., Duering, M., Franzmeier, N., Benzinger, T., Fagan, A. M., Bateman, R. J., Jack, C. R., Levin, J., Dichgans, M., et al. (2018) · Brain · doi:10.1093/brain/awy229 · Cited by 179 · see citing works
“DTI alterations such as those observed in the corticospinal tract in the current study should be interpreted with caution, as the current TBSS approach cannot be unambiguously attributed to that tract, but potentially are because of alterations in crossing fibres such as the superior longitudinal fasciculus or corpus…”
MethodCastellazzi, G., Cuzzoni, M. G., Cotta Ramusino, M., Martinelli, D., Denaro, F., Ricciardi, A., Vitali, P., Anzalone, N., Bernini, S., Palesi, F., et al. (2020) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2020.00025 · Cited by 135 · see citing works
Friedrich, P., Fraenz, C., Schlüter, C., Ocklenburg, S., Mädler, B., Güntürkün, O., & Genç, E. (2019) · Cerebral Cortex · doi:10.1093/cercor/bhz221 · Cited by 128 · see citing works
Bergamino, M., Walsh, R. R., & Stokes, A. M. (2021) · Scientific Reports · doi:10.1038/s41598-021-86505-7 · Cited by 124 · see citing works
Teipel, S. J., Grothe, M. J., Zhou, J. H., Sepulcre, J., Dyrba, M., Sorg, C., & Babiloni, C. (2016) · Journal of the International Neuropsychological Society · doi:10.1017/s1355617715000995 · Cited by 115 · see citing works
Chad, J. A., Pasternak, O., Salat, D. H., & Chen, J. J. (2018) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2018.07.018 · Cited by 110 · see citing works
Demyelination precedes axonal loss in the transneuronal spread of human neurodegenerative disease
You, Y., Joseph, C., Wang, C., Gupta, V., Liu, S., Yiannikas, C., Chua, B., Chitranshi, N., Shen, T., Dheer, Y., et al. (2018) · Brain · doi:10.1093/brain/awy338 · Cited by 112 · see citing works
Cremers, L. G. M., de Groot, M., Albert Hofman, D., Krestin, G. P., van der Lugt, A., Niessen, W. J., Vernooij, M. W., & Ikram, M. A. (2015) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2015.11.021 · Cited by 100 · see citing works
Yu, J., Lam, C. L. M., & Lee, T. M. C. (2017) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2017.10.026 · Cited by 81 · see citing works
Ruan, Q., D’Onofrio, G., Sancarlo, D., Bao, Z., Greco, A., & Yu, Z. (2016) · BMC Geriatrics · doi:10.1186/s12877-016-0281-7 · Cited by 79 · see citing works
Dance and music training have different effects on white matter diffusivity in sensorimotor pathways
Giacosa, C., Karpati, F. J., Foster, N. E. V., Penhune, V. B., & Hyde, K. L. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.04.048 · Cited by 81 · see citing works
Andersen, K. W., Lasič, S., Lundell, H., Nilsson, M., Topgaard, D., Sellebjerg, F. T., Szczepankiewicz, F., Siebner, H. R., Blinkenberg, M., & Dyrby, T. B. (2020) · Brain Communications · doi:10.1093/braincomms/fcaa077 · Cited by 77 · see citing works
Specific Patterns of White Matter Alterations Help Distinguishing Alzheimer's and Vascular Dementia
Palesi, F., Rinaldis, A. D., Vitali, P., Castellazzi, G., Casiraghi, L., Germani, G., Bernini, S., Anzalone, N., Ramusino, M. C., Denaro, F. M., et al. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00274 · Cited by 71 · see citing works
Applying Automated MR-Based Diagnostic Methods to the Memory Clinic: A Prospective Study
Klöppel, S., Peter, J., Ludl, A., Pilatus, A., Maier, S., Mader, I., Heimbach, B., Frings, L., Egger, K., Dukart, J., et al. (2015) · Journal of Alzheimer’s Disease · doi:10.3233/jad-150334 · Cited by 69 · see citing works
Advances in MRI Biomarkers for the Diagnosis of Alzheimer’s Disease
Kehoe, E. G., McNulty, J. P., Mullins, P. G., & Bokde, A. L. W. (2014) · Biomarkers in Medicine · doi:10.2217/bmm.14.42 · Cited by 65 · see citing works
Liu, Q., Zhu, Z., Teipel, S. J., Yang, J., Xing, Y., Tang, Y., & Jia, J. (2017) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2017.00047 · Cited by 64 · see citing works
Rash, B. G., Ramdas, K. N., Agafonova, N., Naioti, E., McClain‐Moss, L., Zainul, Z., Varnado, B., Peterson, K., Brown, M. T., Leal, T., et al. (2025) · Nature Medicine · doi:10.1038/s41591-025-03559-0 · Cited by 60 · see citing works
Evidence of progressive tissue loss in the core of chronic MS lesions: A longitudinal DTI study
Klistorner, A. I., Wang, C., Yiannikas, C., Parratt, J., Dwyer, M. G., Barton, J., Graham, S. L., You, Y., Liu, S., & Barnett, M. H. (2017) · NeuroImage Clinical · doi:10.1016/j.nicl.2017.12.010 · Cited by 57 · see citing works
Mendoza-Léon, R., Puentes, J., Uriza, L. F., & Hoyos, M. H. (2019) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2019.103527 · Cited by 58 · see citing works
Neuroanatomical correlates of verbal fluency in early Alzheimer’s disease and normal aging
Rodríguez‐Aranda, C., Waterloo, K. K., Johnsen, S. H., Eldevik, P., Sparr, S., Wikran, G., Herder, M., & Vangberg, T. R. (2016) · Brain and Language · doi:10.1016/j.bandl.2016.03.001 · Cited by 53 · see citing works
The DELCODE Study Group, Brueggen, K., Dyrba, M., Cardenas-Blanco, A., Schneider, A., Fliessbach, K., Buerger, K., Janowitz, D., Peters, O., Menne, F., et al. (2019) · Journal of Neurology · doi:10.1007/s00415-019-09429-3 · Cited by 51 · see citing works
Doan, N. T., Engvig, A., Persson, K., Alnæs, D., Kaufmann, T., Rokicki, J., Córdova‐Palomera, A., Moberget, T., Brækhus, A., Barca, M. L., et al. (2017) · Scientific Reports · doi:10.1038/srep45131 · Cited by 48 · see citing works
Alderson, T. H., Bokde, A. L. W., Kelso, J. A. S., Maguire, L., & Coyle, D. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.08.033 · Cited by 45 · see citing works
Brueggen, K., Grothe, M. J., Dyrba, M., Fellgiebel, A., Fischer, F., Filippi, M., Agosta, F., Nestor, P. J., Meisenzahl, E. M., Blautzik, J., et al. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.03.067 · Cited by 43 · see citing works
Colon-Perez, L. M., Ibanez, K. R., Suarez, M., Torroella, K., Acuna, K., Ofori, E., Levites, Y., Vaillancourt, D. E., Golde, T. E., Chakrabarty, P., et al. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116138 · Cited by 41 · see citing works
Cooccurrence of vascular risk factors and late-life white-matter integrity changes
Maillard, P., Carmichael, O. T., Reed, B., Mungas, D., & DeCarli, C. S. (2015) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2015.01.007 · Cited by 41 · see citing works
Cavedo, E., Lista, S., Rojkova, K., Chiesa, P. A., Houot, M., Brueggen, K., Blautzik, J., Bokde, A. L. W., Dubois, B., Barkhof, F., et al. (2017) · Neuroscience · doi:10.1016/j.neuroscience.2017.05.048 · Cited by 40 · see citing works
Self-citationMicrostructural Bias in the Assessment of Periventricular Flow as Revealed in Postmortem Brains
Li, S., Chen, R., Cao, Z., Zhu, Q., Ma, Y., Zhu, K., Lin, Z., Wu, D., & Initiative, A. D. N. (2025) · Radiology · doi:10.1148/radiol.250753 · Cited by 38 · see citing works
Hirsiger, S., Koppelmans, V., Mérillat, S., Erdin, C., Narkhede, A., Brickman, A. M., & Jäncke, L. (2017) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2017.00373 · Cited by 37 · see citing works
Distinct Types of White Matter Changes Are Observed after Anterior Temporal Lobectomy in Epilepsy
Pustina, D., Doucet, G. E., Evans, J. J., Sharan, A. D., Sperling, M. R., Skidmore, C. T., & Tracy, J. I. (2014) · PLoS ONE · doi:10.1371/journal.pone.0104211 · Cited by 37 · see citing works
Ouyang, X., Chen, K., Yao, L., Hu, B., Wu, X., Ye, Q., & Guo, X. (2015) · Neuroscience · doi:10.1016/j.neuroscience.2015.06.031 · Cited by 35 · see citing works
Chen, C., Shih, Y., Liou, H., Hsu, Y., Lin, F., & Tseng, W. I. (2019) · NeuroImage Clinical · doi:10.1016/j.nicl.2019.102033 · Cited by 32 · see citing works
Benjamini, D., Hutchinson, E. B., Komlosh, M. E., Comrie, C. J., Schwerin, S. C., Zhang, G., Pierpaoli, C. M., & Basser, P. J. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117195 · Cited by 30 · see citing works
White matter changes underlie hypertension-related cognitive decline in older adults
Li, Z., Wang, W., Sang, F., Zhang, Z., & Li, X. (2023) · NeuroImage Clinical · doi:10.1016/j.nicl.2023.103389 · Cited by 32 · see citing works
Chaim, T. M., Zhang, T., Zanetti, M. V., da Silva, M. A., Louzã, M. R., Doshi, J. J., Serpa, M. H., Duran, F., Caetano, S. C., Davatzikos, C. A., et al. (2014) · PLoS ONE · doi:10.1371/journal.pone.0110199 · Cited by 29 · see citing works
Lin, C., Frigerio, I., Bol, J. G. J. M., Bouwman, M. M. A., Wesseling, A. J., Dahl, M. J., Rozemüller, A. J. M., van der Werf, Y. D., Pouwels, P. J. W., van de Berg, W. D. J., et al. (2024) · Translational Neurodegeneration · doi:10.1186/s40035-024-00400-5 · Cited by 30 · see citing works
Disruption of thalamic connectivity in Alzheimer’s disease: a diffusion tensor imaging study
Zhu, Q., Bi, S., Yao, X.-T., Ni, Z., Li, Y., Chen, B., Fan, G., & Shang, X. (2015) · Metabolic Brain Disease · doi:10.1007/s11011-015-9708-7 · Cited by 28 · see citing works
Crespi, C., Galandra, C., Manera, M. R., Basso, G., Poggi, P., & Canessa, N. (2019) · Frontiers in Psychology · doi:10.3389/fpsyg.2019.02479 · Cited by 27 · see citing works
Kehoe, E. G., Farrell, D., Metzler‐Baddeley, C., LAWLOR, B. A., Kenny, R. A., Lyons, D., McNulty, J. P., Mullins, P. G., Coyle, D., & Bokde, A. L. W. (2015) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2015.00010 · Cited by 23 · see citing works
Connectome-derived diffusion characteristics of the fornix in Alzheimer's disease
Perea, R. D., Rabin, J. S., Fujiyoshi, M. G., Neal, T. E., Smith, E. E., Dijk, K. R. A. V., & Hedden, T. (2018) · NeuroImage Clinical · doi:10.1016/j.nicl.2018.04.029 · Cited by 20 · see citing works
Lin, C., Knoop, L., Frigerio, I., Bol, J. G. J. M., Rozemüller, A. J. M., Berendse, H. W., Pouwels, P. J. W., van de Berg, W. D. J., & Jonkman, L. E. (2023) · Movement Disorders · doi:10.1002/mds.29510 · Cited by 20 · see citing works
Teipel, S. J., the DELCODE study group, Kuper-Smith, J. O., Bartels, C., Brosseron, F., Buchmann, M., Büerger, K., Catak, C., Janowitz, D., Dechent, P., et al. (2019) · Journal of Alzheimer s Disease · doi:10.3233/jad-190446 · Cited by 18 · see citing works
Structural disconnectivity and the risk of dementia in the general population
Cremers, L. G. M., Wolters, F. J., de Groot, M., Ikram, M. A., van der Lugt, A., Niessen, W. J., Vernooij, M. W., & Ikram, M. A. (2020) · Neurology · doi:10.1212/wnl.0000000000010231 · Cited by 18 · see citing works
Analysis of longitudinal diffusion-weighted images in healthy and pathological aging: An ADNI study
Kruggel, F., Masaki, F., & Solodkin, A. (2017) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2016.12.020 · Cited by 15 · see citing works
Cahn, A. J., Little, G. E., Beaulieu, C., & Tétreault, P. (2021) · Brain Structure and Function · doi:10.1007/s00429-020-02181-9 · Cited by 15 · see citing works
Chad, J. A., Pasternak, O., & Chen, J. J. (2021) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2020.12.020 · Cited by 13 · see citing works
Ouyang, X., Chen, K., Yao, L., Wu, X., Zhang, J., Li, K., Jin, Z., Guo, X., & Initiative, A. D. N. (2015) · PLoS ONE · doi:10.1371/journal.pone.0119714 · Cited by 15 · see citing works
Teipel, S. J., Ehlers, I., Erbe, A., Holzmann, C., Lau, E., Hauenstein, K., & Berger, C. (2014) · Journal of Neuroimaging · doi:10.1111/jon.12178 · Cited by 14 · see citing works
Roldán-Valadéz, E., Flores-Álvarez, E., Durand-Muñoz, C., Cortes-Hernandez, F., Muñoz‐Hernández, O., & Moreno‐Jiménez, S. (2019) · Neurology India · doi:10.4103/0028-3886.266284 · Cited by 13 · see citing works
Zhang, Y., & Zhan, F. (2023) · Journal of Integrative Neuroscience · doi:10.31083/j.jin2204101 · Cited by 12 · see citing works
Uddin, M. N., Singh, M. V., Faiyaz, A., Szczepankiewicz, F., Nilsson, M., Boodoo, Z. D., Sutton, K. R., Tivarus, M. E., Zhong, J., Wang, L., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-80372-8 · Cited by 13 · see citing works
Klistorner, S., Barnett, M. H., Wasserthal, J., Yiannikas, C., Barton, J., Parratt, J., You, Y., Graham, S. L., & Klistorner, A. (2021) · PLOS ONE · doi:10.1371/journal.pone.0244766 · Cited by 10 · see citing works
“Therefore, apart from the limited area of white matter occupied by coherent fiber tracts, voxel-based analysis utilising DTI models does not adequately estimate orientationally-sensitive diffusivity measures, such as axial and radial diffusivity (AD and RD) [12, 13].”
Perus, L., Busto, G. U., Mangin, J., Bars, E. L., & Gabelle, A. (2022) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2022.1014559 · Cited by 9 · see citing works
Investigating Tissue-Specific Abnormalities in Alzheimer’s Disease with Multi-Shell Diffusion MRI
Giraldo, D. L., Smith, R., Struyfs, H., Niemantsverdriet, E., Roeck, E. D. D., Bjerke, M., Engelborghs, S., Romero, E., Sijbers, J., & Jeurissen, B. (2022) · Journal of Alzheimer's Disease · doi:10.3233/jad-220551 · Cited by 8 · see citing works
Initiative, A. D. N., Yu, J., & Lee, T. M. C. (2019) · Brain Imaging and Behavior · doi:10.1007/s11682-019-00183-8 · Cited by 8 · see citing works
White Matter Network Alterations in Alzheimer’s Disease Patients
Lama, R. K., & Lee, S.-W. (2020) · Applied Sciences · doi:10.3390/app10030919 · Cited by 8 · see citing works
Karan, P., Reymbaut, A., Gilbert, G., & Descoteaux, M. (2021) · Medical Image Analysis · doi:10.1016/j.media.2022.102476 · Cited by 7 · see citing works
Chen, Y., Chang, C., Liang, Y., Tseng, H.-Y., Li, S., Chang, C., Wu, Y.-T., Shao, H.-H., Chen, P., Lai, M.-L., et al. (2024) · Journal of Neural Engineering · doi:10.1088/1741-2552/ad7322 · Cited by 7 · see citing works
Correction for Eddy Current-Induced Echo-Shifting Effect in Partial-Fourier Diffusion Tensor Imaging
Truong, T., Song, A. W., & Chen, N. (2015) · BioMed Research International · doi:10.1155/2015/185026 · Cited by 5 · see citing works
Neurobiological Mechanisms of Cognitive Decline Correlated with Brain Aging
Zhang, X., An, H., Chen, Y., & Shu, N. (2023) · Advances in experimental medicine and biology · doi:10.1007/978-981-99-1627-6_10 · Cited by 4 · see citing works
Sacchi, L., Farruggio, G., Bocca, G., Carandini, T., Riz, M. A. D., Ghezzi, L., Pintus, M., Pietroboni, A. M., Boffa, G., Scola, E., et al. (2026) · Human Brain Mapping · doi:10.1002/hbm.70603 · Cited by 3 · see citing works
Fractals in the Neurosciences: A Translational Geographical Approach
Andronache, I., Peptenatu, D., Ahammer, H., Radulović, M., Djuričić, G., Jelinek, H. F., Russo, C., & Ieva, A. D. (2024) · Advances in neurobiology · doi:10.1007/978-3-031-47606-8_47 · Cited by 2 · see citing works
Visual modulation of vestibular‐evoked balance response disturbed by posterior cortical atrophy
Ocal, D., Day, B. L., Peters, A., Bancroft, M. J., Cash, D. M., Kaski, D. N., Ryan, N. S., Crutch, S. J., & Yong, K. X. X. (2025) · The Journal of Physiology · doi:10.1113/jp288693 · Cited by 1 · see citing works
Diffusion Alterations at the Gray Matter/White Matter Boundary in Traumatic Encephalopathy Syndrome
Wiegand, T. L. T., Pankatz, L., Arciniega, H., Jung, L., Tuz‐Zahra, F., Sylvain Bouix, A., Lubeck, H., Rojczyk, P., Schuhmacher, L. S., Buring, J. E., et al. (2025) · Journal of Neurotrauma · doi:10.1177/08977151251393966 · Cited by 1 · see citing works
Clinical correlates of perfusion and diffusion MRI metrics in cognitively unimpaired older adults
Shakibajahromi, B., Dolui, S., Brown, C., Taghvaei, M., Sadaghiani, S., Khandelwal, P., Das, S. R., Yushkevich, P. A., Nasrallah, I. M., Xie, S. X., et al. (2025) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1177/0271678x251389043 · Cited by 1 · see citing works
Functional and Structural MRI in Alzheimer’s Disease: A Multimodal Approach
Grothe, M. J., Bokde, A. L. W., & Teipel, S. J. (2014) · Springer eBooks · doi:10.1007/978-3-642-54542-9_20 · Cited by 1 · see citing works
Chu, W.-L., Huang, M., Jian, B., & Cheng, K. (2017) · Current Medical Imaging Formerly Current Medical Imaging Reviews · doi:10.2174/1573405613666171122163759 · Cited by 1 · see citing works
Karan, P., Reymbaut, A., Gilbert, G., & Descoteaux, M. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.04.07.438845 · Cited by 1 · see citing works
Fractional Anisotropy Brain Changes in Spasticity after Peripheral Injections of Botulin
Angel, B. M., Sakel, M., & Hojjatoleslami, A. (2018) · International Journal of Physiatry · doi:10.23937/2572-4215.1510015
汪, 克. (2018) · Advances in Clinical Medicine · doi:10.12677/acm.2018.810153
Colón-Pérez, L. M., Ibanez, K. R., Suarez, M., Torroella, K., Acuna, K., Ofori, E., Levites, Y., Vaillancourt, D. E., Golde, T. E., Chakrabarty, P., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/562348
Diffusion Tensor Imaging and Cognition of Transient Ischemic Attack Patients and Healthy Controls
Tariq, S. (2018) · Libraries and Cultural Resources (University of Calgary) · doi:10.11575/prism/31920
Zdanovskis, N., Kaļva, K., Platkājis, A., Kostiks, A., Šneidere, K., Karelis, G., & Stepens, A. (2025) · Neurology International · doi:10.3390/neurolint17100154
Riquelme-Contreras, P., Mery, I., Aguilera, S., Henriquez, F. A., Medel, V., Ferreira, P. C. L., Bellaver, B., Karikari, T. K., Pascoal, T. A., Thumala, D., et al. (2026) · Research Square · doi:10.21203/rs.3.rs-8621977/v1
Abburi, V. T., Singhal, A., Shigwan, S. J., & Kumar, N. (2026) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi61048.2026.11515798
Sacchi, L., Veronesi, V., Bocca, G., Schmid, L., D’Agata, F., Campisi, C., Örzsik, B., Arcaro, M., Fenoglio, C., Carandini, T., et al. (2026) · Journal of Alzheimer s Disease · doi:10.1177/13872877261467934
Li, R.-H., Li, J., Li, C.-Y., Wang, X.-G., Zhou, W.-Y., Zou, Y.-C., Zhen, Z., Xiao, L., Liang, N., Zou, Z.-X., et al. (2026) · Visual Information Expert Workshop · doi:10.1002/viw2.70210
- Authors
- Teipel, S. J., the EDSD study group, Grothe, M. J., Filippi, M., Fellgiebel, A., Dyrba, M., Frisoni, G. B., Meindl, T., Bokde, A. L. W., Hampel, H., Klöppel, S., & Hauenstein, K.
- Affiliations
- German Center for Neurodegenerative Diseases (DE)
- University of Rostock (DE)
- Vita-Salute San Raffaele University (IT)
- University of Geneva (CH)
- Trinity College Dublin (IE)
- Goethe University Frankfurt (DE)
- University Medical Center Freiburg (DE)
- ORCID
- Venue
- Journal of Alzheimer s Disease
- Publisher
- SAGE Publications
- Published
- 2014-06-04
- Volume
- 41
- Issue
- 1
- Pages
- 69-83
- ISSN
- 1387-2877 (ISSN-L)
- 1875-8908
- OpenAlex
- W1527867630
- Cited by
- 82
- References
- 50
- Citations by year
- 2026: 5 · 2025: 6 · 2024: 4 · 2023: 5 · 2022: 3 · 2021: 6 · 2020: 5 · 2019: 10 · 2018: 10 · 2017: 8 · 2016: 6 · 2015: 8 · 2014: 6
- FWCI
- 7.29
- Type
- journal article
- Language
- en
- Open access
- Licence
- cc-by
- Locations
- Journal of Alzheimer s Disease (publishedVersion)
- PubMed (publishedVersion)
- Archive ouverte UNIGE (University of Geneva) (submittedVersion, cc-by)
- Archivio Istituzionale della Ricerca - Università Vita-Salute San Raffaele (submittedVersion)
- DZNE Pub (submittedVersion)
- Funders
- Japan International Cooperation Agency
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Keywords
- Alzheimer's disease, fractional anisotropy
- MeSH terms
- Aged, Alzheimer Disease, Axons, Brain, Europe, Female, Humans, Image Processing, Computer-Assisted, Male, Nerve Degeneration, Nerve Fibers, Myelinated, Pyramidal Tracts, Anisotropy, Disease Progression, Diffusion Magnetic Resonance Imaging, Early Diagnosis, Diffusion Tensor Imaging, Cognitive Dysfunction, White Matter
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review