Effectiveness of regional DTI measures in distinguishing Alzheimer’s disease, MCI, and normal aging

Nir, Talia M.; Jahanshad, Neda; Villalon-Reina, Julio E.; Toga, Arthur W.; Jack, Clifford R.; Weiner, Michael W.; Thompson, Paul M.

doi:10.1016/j.nicl.2013.07.006

Cited by 349Dementia and Cognitive Impairment ResearchAdvanced Neuroimaging Techniques and ApplicationsAlzheimer's disease research and treatments

Effectiveness of regional DTI measures in distinguishing Alzheimer’s disease, MCI, and normal aging

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Nir, Talia M., Jahanshad, Neda, Villalon-Reina, Julio E., Toga, Arthur W., Jack, Clifford R., Weiner, Michael W., Thompson, Paul M.·2013·NeuroImage: Clinical

DOI 10.1016/j.nicl.2013.07.006PMC PMC3792746

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Abstract

The Alzheimer's Disease Neuroimaging Initiative (ADNI) recently added diffusion tensor imaging (DTI), among several other new imaging modalities, in an effort to identify sensitive biomarkers of Alzheimer's disease (AD). While anatomical MRI is the main structural neuroimaging method used in most AD studies and clinical trials, DTI is sensitive to microscopic white matter (WM) changes not detectable with standard MRI, offering additional markers of neurodegeneration. Prior DTI studies of AD report lower fractional anisotropy (FA), and increased mean, axial, and radial diffusivity (MD, AxD, RD) throughout WM. Here we assessed which DTI measures may best identify differences among AD, mild cognitive impairment (MCI), and cognitively healthy elderly control (NC) groups, in region of interest (ROI) and voxel-based analyses of 155 ADNI participants (mean age: 73.5 ± 7.4; 90 M/65 F; 44 NC, 88 MCI, 23 AD). Both VBA and ROI analyses revealed widespread group differences in FA and all diffusivity measures. DTI maps were strongly correlated with widely-used clinical ratings (MMSE, CDR-sob, and ADAS-cog). When effect sizes were ranked, FA analyses were least sensitive for picking up group differences. Diffusivity measures could detect more subtle MCI differences, where FA could not. ROIs showing strongest group differentiation (lowest p-values) included tracts that pass through the temporal lobe, and posterior brain regions. The left hippocampal component of the cingulum showed consistently high effect sizes for distinguishing groups, across all diffusivity and anisotropy measures, and in correlations with cognitive scores.

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Among the three cognitive assessments, CDR-sob showed the strongest correlations with dMRI indices, in line with prior ADNI brain imaging studies.
“Among the three cognitive assessments, CDR-sob showed the strongest correlations with dMRI indices, in line with prior ADNI brain imaging studies (Hua et al., 2009; Nir et al., 2013).”
From Diffusion MRI Indices and Their Relation to Cognitive Impairment in Brain Aging: The Updated Multi-protocol Approach in ADNI3 (2019) · cites this paper as Nir et al., 2013
The Discussion reports that CDR-sob was the most widely correlated of the three assessments with diffusivity in the MCI group, in both voxel-based and ROI analyses. It does not establish that CDR-sob had the strongest correlations with dMRI indices across the full cohort or across all dMRI measures, as the unqualified claim implies. Page numbers are unavailable in our copy. (LLM, conf=0.86)
Experimental — accuracy not yet measured
In ADNI2, AxDDTI was the most sensitive to differences between CN and MCI diagnosis.
“Similarly, in ADNI2, AxDDTI was the most sensitive to differences between CN and MCI diagnosis (Nir et al., 2013).”
From Diffusion MRI Indices and Their Relation to Cognitive Impairment in Brain Aging: The Updated Multi-protocol Approach in ADNI3 (2019) · cites this paper as Nir et al., 2013
The Discussion (page unavailable) reports slightly larger effect sizes for axial diffusivity (AxD) when comparing controls with MCI in full-ROI analyses, supporting the sensitivity comparison. The claim omits that analysis-specific qualifier: the Results, under Voxel-based analyses (page unavailable), found no significant difference between controls and MCI as a whole for any DTI measure. (LLM, conf=0.83)
Experimental — accuracy not yet measured
As in prior analysis of ADNI2, FADTI was the least sensitive DTI measure.
“As in our prior analysis of ADNI2 (Nir et al., 2013), FADTI was the least sensitive DTI measure.”
From Diffusion MRI Indices and Their Relation to Cognitive Impairment in Brain Aging: The Updated Multi-protocol Approach in ADNI3 (2019) · cites this paper as Nir et al., 2013
dMRI has been used in numerous studies to understand the effects of AD on white matter microstructure and brain connectivity.
“dMRI has since been used in numerous studies to understand the effects of AD on white matter (WM) microstructure and brain connectivity (Daianu et al., 2013a,b; Nir et al., 2013; Prasad et al., 2013).”
From Diffusion MRI Indices and Their Relation to Cognitive Impairment in Brain Aging: The Updated Multi-protocol Approach in ADNI3 (2019) · cites this paper as Nir et al., 2013
Lower FADTI and higher MDDTI are most frequently reported in studies of AD.
“Lower FADTI and higher MDDTI are most frequently reported in studies of AD (Kavcic et al., 2008; Clerx et al., 2012; Nir et al., 2013; Maggipinto et al., 2017; Mayo et al., 2017)”
From Diffusion MRI Indices and Their Relation to Cognitive Impairment in Brain Aging: The Updated Multi-protocol Approach in ADNI3 (2019) · cites this paper as Nir et al., 2013

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Common Alzheimer's Disease Risk Variant Within the CLU Gene Affects White Matter Microstructure in Young Adults
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3D characterization of brain atrophy in Alzheimer's disease and mild cognitive impairment using tensor-based morphometry
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Relating imaging indices of white matter integrity and volume in healthy older adults
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White matter hyperintensities are significantly associated with cortical atrophy in Alzheimer's disease
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New MRI markers for Alzheimer’s disease: a meta-analysis of diffusion tensor imaging and a comparison with medial temporal lobe measurements
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White matter lesions and hippocampal atrophy in Alzheimer’s disease
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White matter damage of patients with Alzheimer’s disease correlated with the decreased cognitive function
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Diffusion tensor in posterior cingulate gyrus: correlation with cognitive decline in Alzheimer??s disease
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Automated MRI measures predict progression to Alzheimer's disease
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A study of diffusion tensor imaging by tissue-specific, smoothing-compensated voxel-based analysis
Lee, J. E., Chung, M. K., Lazar, M., DuBray, M. B., Kim, J., Bigler, E. D., Lainhart, J. E., & Alexander, A. L. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.09.041 · Cited by 109 · see citing works
The tensor distribution function
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Decreasing myelin density reflected increasing white matter pathology in Alzheimer's disease—a neuropathological study
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White matter integrity linked to functional impairments in aging and early Alzheimer’s disease
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Alzheimer's disease disrupts rich club organization in brain connectivity networks
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Blood pressure and cerebral white matter share common genetic factors in Mexican Americans
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Tractography density and network measures in Alzheimer’s disease
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Incorrect ICBM-DTI-81 atlas orientation and white matter labels
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Visualization Tools for High Angular Resolution Diffusion Imaging
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Connectomics Sheds New Light on Alzheimer’s Disease
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Differential information content in staggered multiple shell hardi measured by the tensor distribution function
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How do spatial and angular resolution affect brain connectivity maps from diffusion MRI?
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Comparison of diffusion tensor image study in association fiber tracts among normal, amnestic mild cognitive impairment, and Alzheimer′s patients
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A Novel Measure of Fractional Anisotropy Based on the Tensor Distribution Function
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How Many Gradients are Sufficient in High-Angular Resolution Diffusion Imaging (HARDI)?
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Connectivity Network Breakdown Predicts Imminent Volumetric Atrophy in Early Mild Cognitive Impairment
Nir, T. M., Jahanshad, N., Toga, A. W., Jack, C. R., Weiner, M. W., & Thompson, P. M. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33530-3_4 · Cited by 6 · see citing works
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Daianu M. Jahanshad N. Nir T.M. Dennis E. Toga A.W. Jack C.R. Weiner M.W. Thompson P.M. ADNI Analyzing the structural k-core of brain connectivity networks in normal aging and Alzheimer's disease MICCAI NIBAD 2012 52 62
Gutman B. Svarer C. Leow A.D. Yanovsky I. Toga A.W. Thompson P.M. Creating unbiased minimal deformation templates for brain volume registration Proceedings of the Organization for Human Brain Mapping Annual Conference 2012

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Comparison of nine tractography algorithms for detecting abnormal structural brain networks in Alzheimer’s disease
Zhan, L., Zhou, J., Wang, Y., Jin, Y., Jahanshad, N., Prasad, G., Nir, T. M., Leonardo, C. D., Ye, J., Thompson, P. M., et al. (2015) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2015.00048 · Cited by 141 · see citing works
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DeepDTI: High-fidelity six-direction diffusion tensor imaging using deep learning
Tian, Q., Bilgiç, B., Fan, Q., Liao, C., Ngamsombat, C., Hu, Y., Witzel, T., Setsompop, K., Polimeni, J. R., & Huang, S. Y. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117017 · Cited by 137 · see citing works
Associations between white matter microstructure and amyloid burden in preclinical Alzheimer’s disease: a multimodal imaging investigation.
Racine, A. M., Adluru, N., Alexander, A. L., Christian, B. T., Okonkwo, O. C., Oh, J., Cleary, C. A., Birdsill, A., Hillmer, A. T., Murali, D., et al. (2014) · NeuroImage: Clinical · doi:10.1016/j.nicl.2014.02.001 · Cited by 136 · see citing works
Dementia risk and prevention by targeting modifiable vascular risk factors
Tariq, S., & Barber, P. A. (2017) · Journal of Neurochemistry · doi:10.1111/jnc.14132 · Cited by 135 · see citing works
Diffuse white matter tract abnormalities in clinically normal ageing retired athletes with a history of sports-related concussions
Tremblay, S., Henry, L. C., Bedetti, C., Larson-Dupuis, C., Gagnon, J., Evans, A. C., Théoret, H., Lassonde, M. C., & Beaumont, L. D. (2014) · Brain · doi:10.1093/brain/awu236 · Cited by 134 · see citing works
Connectome imaging for mapping human brain pathways
Shi, Y., & Toga, A. W. (2017) · Molecular Psychiatry · doi:10.1038/mp.2017.92 · Cited by 131 · see citing works
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Diffusion kurtosis imaging allows the early detection and longitudinal follow‐up of amyloid‐β‐induced pathology
Praet, J., Manyakov, N. V., Muchene, L., Mai, Z., Terzopoulos, V., de Backer, S., Torremans, A., Guns, P.-J., Van De Casteele, T., Bottelbergs, A., et al. (2018) · Alzheimer's Research & Therapy · doi:10.1186/s13195-017-0329-8 · Cited by 133 · see citing works
Cerebrospinal Fluid Biomarkers of Neurovascular Dysfunction in Mild Dementia and Alzheimer'S Disease
Sweeney, M. D., Sagare, A. P., & Zlokovic, B. V. (2015) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1038/jcbfm.2015.76 · Cited by 122 · see citing works
Effects of Arterial Stiffness on Brain Integrity in Young Adults From the Framingham Heart Study
Maillard, P., Mitchell, G. F., Himali, J. J., Beiser, A., Tsao, C. W., Pase, M. P., Satizábal, C. L., Vasan, R. S., Seshadri, S. S., & DeCarli, C. S. (2016) · Stroke · doi:10.1161/strokeaha.116.012949 · Cited by 122 · see citing works
Cerebral white matter free water
Maillard, P., Fletcher, E. M., Singh, B., Martinez, O., Johnson, D. K., Olichney, J., Farias, S. T., & DeCarli, C. S. (2019) · Neurology · doi:10.1212/wnl.0000000000007449 · Cited by 110 · see citing works
Microstructural disruption of the right inferior fronto‐occipital and inferior longitudinal fasciculus contributes to WMH‐related cognitive impairment
Chen, H., Huang, L., Li, H., Qian, Y., Yang, D., Zhao, Q., Luo, C., Li, M., Zhang, B., & Xu, Y. (2020) · CNS Neuroscience & Therapeutics · doi:10.1111/cns.13283 · Cited by 111 · see citing works
Diffusion tensor imaging in Alzheimer's disease: insights into the limbic-diencephalic network and methodological considerations
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Revolutionizing Alzheimer's disease and clinical trials through biomarkers
Mattsson, N., Carrillo, M. C., Dean, R. A., Devous, M. D., Nikolcheva, T., Pesini, P., Salter, H., Potter, W. Z., Sperling, R. A., Bateman, R. J., et al. (2015) · Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring · doi:10.1016/j.dadm.2015.09.001 · Cited by 102 · see citing works
Diffusion MRI Indices and Their Relation to Cognitive Impairment in Brain Aging: The Updated Multi-protocol Approach in ADNI3
Zavaliangos-Petropulu, A., Nir, T. M., Thomopoulos, S. I., Reid, R. I., Bernstein, M. A., Borowski, B., Jack Jr., C. R., Weiner, M. W., Jahanshad, N., & Thompson, P. M. (2019) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2019.00002 · Cited by 101 · see citing works
“We hypothesized that the diffusion indices from ADNI2 (Nir et al., 2013, 2017) would still be associated with clinical measures of disease burden in ADNI3—despite the variation in protocols.”
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Hydration water mobility is enhanced around tau amyloid fibers
Fichou, Y., Schirò, G., Gallat, F., Laguri, C., Moulin, M., Combet, J., Zamponi, M., Haertlein, M., Picart, C., Mossou, E., et al. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1422824112 · Cited by 99 · see citing works
White matter alterations in early‐stage Alzheimer's disease: A tract‐specific study
Wen, Q., Mustafi, S. M., Li, J., Risacher, S. L., Tallman, E., Brown, S. A., West, J. D., Harezlak, J., Farlow, M. R., Unverzagt, F. W., et al. (2019) · Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring · doi:10.1016/j.dadm.2019.06.003 · Cited by 96 · see citing works
Alzheimer’s disease diagnosis framework from incomplete multimodal data using convolutional neural networks
Abdelaziz, M., Wang, T., & Elazab, A. (2021) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2021.103863 · Cited by 93 · see citing works
Perivascular space fluid contributes to diffusion tensor imaging changes in white matter
Sepehrband, F., Cabeen, R. P., Choupan, J., Barisano, G., Law, M., Toga, A. W., & Initiative, T. A. D. N. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2019.04.070 · Cited by 88 · see citing works
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White matter disruption at the prodromal stage of Alzheimer's disease: Relationships with hippocampal atrophy and episodic memory performance
Rémy, F., Vayssière, N., Saint‐Aubert, L., Barbeau, E. J., & Pariente, J. (2015) · NeuroImage Clinical · doi:10.1016/j.nicl.2015.01.014 · Cited by 87 · see citing works
Tract-based analysis of white matter degeneration in Alzheimer’s disease
Lee, S., Coutu, J., Wilkens, P. J., Yendiki, A., Rosas, H. D., & Salat, D. H. (2015) · Neuroscience · doi:10.1016/j.neuroscience.2015.05.049 · Cited by 88 · see citing works
Default Mode Network Functional Connectivity in Early and Late Mild Cognitive Impairment
Lee, E., Yoo, K., Lee, Y., Chung, J., Lim, J., Yoon, B., & Jeong, Y. (2016) · Alzheimer Disease & Associated Disorders · doi:10.1097/wad.0000000000000143 · Cited by 86 · see citing works
Dissecting the Fornix in Basic Memory Processes and Neuropsychiatric Disease: A Review
Benear, S. L., Ngo, C. T., & Olson, I. R. (2020) · Brain Connectivity · doi:10.1089/brain.2020.0749 · Cited by 84 · see citing works
Overview of ADNI MRI
Jack, C. R., Arani, A., Borowski, B. J., Cash, D. M., Crawford, K., Das, S. R., DeCarli, C., Fletcher, E., Fox, N. C., Gunter, J. L., et al. (2024) · Alzheimer's & Dementia · doi:10.1002/alz.14166 · Cited by 82 · see citing works
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SDnDTI: Self-supervised deep learning-based denoising for diffusion tensor MRI
Tian, Q., Li, Z., Fan, Q., Polimeni, J. R., Bilgiç, B., Salat, D. H., & Huang, S. Y. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119033 · Cited by 81 · see citing works
Hippocampal volume and cingulum bundle fractional anisotropy are independently associated with verbal memory in older adults
Ezzati, A., Katz, M. J., Lipton, M. L., Zimmerman, M. E., & Lipton, R. B. (2016) · Brain Imaging and Behavior · doi:10.1007/s11682-015-9452-y · Cited by 79 · see citing works
Diffusion weighted imaging-based maximum density path analysis and classification of Alzheimer's disease
Nir, T. M., Villalón‐Reina, J. E., Prasad, G., Jahanshad, N., Joshi, S. H., Toga, A. W., Bernstein, M. A., Jack, C. R., Weiner, M. W., & Thompson, P. M. (2014) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2014.05.037 · Cited by 75 · see citing works
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Neural correlates of the impulse dyscontrol domain of mild behavioral impairment
Gill, S., Wang, M., Mouchès, P., Rajashekar, D., Sajobi, T. T., MacMaster, F. P., Smith, E. E., Forkert, N. D., Ismail, Z., & Initiative, A. D. N. (2021) · International Journal of Geriatric Psychiatry · doi:10.1002/gps.5540 · Cited by 73 · see citing works
CNN and swin-transformer based efficient model for Alzheimer’s disease diagnosis with sMRI
Xin, J., Wang, A., Guo, R. J., Liu, W., & Tang, X. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105189 · Cited by 68 · see citing works
White matter degeneration in subjective cognitive decline: a diffusion tensor imaging study
Li, X.-Y., Tang, Z.-C., Sun, Y., Tian, J., Liu, Z.-Y., & Han, Y. (2016) · Oncotarget · doi:10.18632/oncotarget.10091 · Cited by 65 · see citing works
Neurofilament relates to white matter microstructure in older adults
Moore, E. E., Hohman, T. J., Badami, F. S., Pechman, K. R., Osborn, K. E., Acosta, L. M. Y., Bell, S. P., Babicz, M. A., Gifford, K. A., Anderson, A. W., et al. (2018) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2018.06.023 · Cited by 65 · see citing works
The Worldwide Alzheimer's Disease Neuroimaging Initiative: An update
Hendrix, J. A., Finger, B., Weiner, M. W., Frisoni, G. B., Iwatsubo, T., Rowe, C. C., Kim, S. Y., Guinjoan, S. M., Sevlever, G. E., & Carrillo, M. C. (2015) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2015.05.008 · Cited by 65 · see citing works
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An advanced white matter tract analysis in frontotemporal dementia and early-onset Alzheimer’s disease
Daianu, M., Mendez, M. F., Baboyan, V. G., Jin, Y., Melrose, R. J., Jimenez, E. E., & Thompson, P. M. (2016) · Brain Imaging and Behavior · doi:10.1007/s11682-015-9458-5 · Cited by 64 · see citing works
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Shape and diffusion tensor imaging based integrative analysis of the hippocampus and the amygdala in Alzheimer's disease
Tang, X., Qin, Y., Wu, J., Zhang, M., Zhu, W., & Miller, M. I. (2016) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2016.05.001 · Cited by 64 · see citing works
Transcriptome-wide association analysis of brain structures yields insights into pleiotropy with complex neuropsychiatric traits
Zhao, B., Shan, Y., Yang, Y., Yu, Z., Li, T., Wang, X., Luo, T., Zhu, Z., Sullivan, P. F., Zhao, H., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-23130-y · Cited by 66 · see citing works
Prediction of Incipient Alzheimer’s Disease Dementia in Patients with Mild Cognitive Impairment
Ardekani, B. A., Initiative, A. D. N., Bermudez, E., Mubeen, A. M., & Bachman, A. H. (2016) · Journal of Alzheimer s Disease · doi:10.3233/jad-160594 · Cited by 63 · see citing works
Altered white matter microstructure is associated with social cognition and psychotic symptoms in 22q11.2 microdeletion syndrome
Jalbrzikowski, M., Villalón‐Reina, J. E., Karlsgodt, K. H., Şentürk, D., Chow, C. C., Thompson, P. M., & Bearden, C. E. (2014) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2014.00393 · Cited by 63 · see citing works
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Vascular burden and APOE ε4 are associated with white matter microstructural decline in cognitively normal older adults
Williams, O. A., An, Y., Beason‐Held, L. L., Huo, Y., Ferrucci, L., Landman, B. A., & Resnick, S. M. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.12.009 · Cited by 64 · see citing works
Quantification of white matter cellularity and damage in preclinical and early symptomatic Alzheimer's disease
Wang, Q., Wang, Y., Liu, J., Sutphen, C. L., Cruchaga, C., Blazey, T., Gordon, B. A., Su, Y., Chen, C., Shimony, J. S., et al. (2019) · NeuroImage: Clinical · doi:10.1016/j.nicl.2019.101767 · Cited by 63 · see citing works
White matter in the older brain is more plastic than in the younger brain
Yotsumoto, Y., Chang, L.-H., Ni, R., Pierce, R. S., Andersen, G. J., Watanabe, T., & Sasaki, Y. (2014) · Nature Communications · doi:10.1038/ncomms6504 · Cited by 62 · see citing works
Early functional connectivity deficits and progressive microstructural alterations in the TgF344-AD rat model of Alzheimer’s Disease: A longitudinal MRI study
Anckaerts, C., Blockx, I., Summer, P., Michael, J., Hamaide, J., Kreutzer, C., Boutin, H., Couillard‐Després, S., Verhoye, M., & der Linden, A. V. (2018) · Neurobiology of Disease · doi:10.1016/j.nbd.2018.11.010 · Cited by 62 · see citing works
Diffusion Kurtosis Imaging: A Possible MRI Biomarker for AD Diagnosis?
Struyfs, H., Hecke, W. V., Veraart, J., Sijbers, J., Slaets, S., Belder, M. D., Wuyts, L. L., Peters, B., Sleegers, K., Robberecht, C., et al. (2015) · Journal of Alzheimer s Disease · doi:10.3233/jad-150253 · Cited by 61 · see citing works
Fractional Anisotropy changes in Parahippocampal Cingulum due to Alzheimer’s Disease
da Rocha, J. L. D., Bramati, I. E., Coutinho, G., Tovar-Moll, F. F., & Sitaram, R. (2020) · Scientific Reports · doi:10.1038/s41598-020-59327-2 · Cited by 61 · see citing works
Diffusion MRI and its Role in Neuropsychology
Mueller, B. A., Lim, K. O., Hemmy, L. S., & Camchong, J. (2015) · Neuropsychology Review · doi:10.1007/s11065-015-9291-z · Cited by 60 · see citing works
White matter abnormalities and cognition in aging and Alzheimer disease
Peter, C., Sathe, A., Shashikumar, N., Pechman, K. R., Workmeister, A. W., Jackson, T. B., Huo, Y., Mukherjee, S., Mez, J., Dumitrescu, L. C., et al. (2025) · JAMA Neurology · doi:10.1001/jamaneurol.2025.1601 · Cited by 59 · see citing works
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Characterization of white matter changes along fibers by automated fiber quantification in the early stages of Alzheimer's disease
Zhang, X., Sun, Y., Li, W., Liu, B., Wu, W., Zhao, H., Liu, R., Zhang, Y., Yin, Z., Yu, T., et al. (2019) · NeuroImage Clinical · doi:10.1016/j.nicl.2019.101723 · Cited by 58 · see citing works
White Matter Integrity Is Associated With Intraindividual Variability in Neuropsychological Test Performance in Healthy Older Adults
Halliday, D., Gawryluk, J. R., García-Barrera, M. A., & MacDonald, S. W. S. (2019) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2019.00352 · Cited by 57 · see citing works
White matter microstructure in chronic moderate‐to‐severe traumatic brain injury: Impact of acute‐phase injury‐related variables and associations with outcome measures
Haberg, A. K., Olsen, A., Moen, K. G., Schirmer-Mikalsen, K., Visser, E., Finnanger, T. G., Evensen, K. A. I., Skandsen, T., Vik, A., & Eikenes, L. (2014) · Journal of Neuroscience Research · doi:10.1002/jnr.23534 · Cited by 56 · see citing works
Voxel-wise comparisons of cellular microstructure and diffusion-MRI in mouse hippocampus using 3D Bridging of Optically-clear histology with Neuroimaging Data (3D-BOND)
Stolp, H. B., Ball, G., So, P., Tournier, J., Jones, M., Thornton, C., & Edwards, A. D. (2018) · Scientific Reports · doi:10.1038/s41598-018-22295-9 · Cited by 57 · see citing works
Functional connectivity of white matter as a biomarker of cognitive decline in Alzheimer’s disease
Gao, Y., Sengupta, A., Li, M., Zu, Z., Rogers, B. P., Anderson, A. W., Ding, Z., Gore, J. C., & Initiative, A. D. N. (2020) · PLoS ONE · doi:10.1371/journal.pone.0240513 · Cited by 57 · see citing works
Differential White Matter Regional Alterations in Motor Subtypes of Early Drug-Naive Parkinson’s Disease Patients
Wen, M., Heng, H. S. E., Lu, Z., Xu, Z., Chan, L. L., Tan, E. K., & Tan, L. C. S. (2018) · Neurorehabilitation and neural repair · doi:10.1177/1545968317753075 · Cited by 53 · see citing works
Gray matter T1‐w/T2‐w ratios are higher in Alzheimer's disease
Pelkmans, W., Dicks, E., Barkhof, F., Vrenken, H., Scheltens, P., van der Flier, W. M., & Tijms, B. M. (2019) · Human Brain Mapping · doi:10.1002/hbm.24638 · Cited by 53 · see citing works
Selective Persistence of Sensorimotor Mismatch Signals in Visual Cortex of Behaving Alzheimer’s Disease Mice
Liebscher, S., Keller, G. B., Goltstein, P. M., Bonhoeffer, T., & Hübener, M. (2016) · Current Biology · doi:10.1016/j.cub.2016.01.070 · Cited by 52 · see citing works
Morphological and Microstructural Changes of the Hippocampus in Early MCI: A Study Utilizing the Alzheimer's Disease Neuroimaging Initiative Database
Lee, P., Ryoo, H., Park, J., Jeong, Y., & Initiative, A. D. N. (2017) · Journal of Clinical Neurology · doi:10.3988/jcn.2017.13.2.144 · Cited by 52 · see citing works
Growing out of attention deficit hyperactivity disorder: Insights from the ‘remitted’ brain
Sudre, G. P., Mangalmurti, A., & Shaw, P. (2018) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2018.08.010 · Cited by 51 · see citing works
Multi-modal multiple kernel learning for accurate identification of Tourette syndrome children
Wen, H., Liu, Y., Rekik, I., Wang, S., Chen, Z., Zhang, J., Zhang, Y., Peng, Y., & He, H. (2016) · Pattern Recognition · doi:10.1016/j.patcog.2016.09.039 · Cited by 50 · see citing works
3 D tract‐specific local and global analysis of white matter integrity in A lzheimer's disease
Jin, Y., Huang, C., Daianu, M., Zhan, L., Dennis, E. L., Reid, R. I., Jack, C. R., Hongtu Zhu, B., Thompson, P. M., & Initiative, A. D. N. (2016) · Human Brain Mapping · doi:10.1002/hbm.23448 · Cited by 50 · see citing works
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Multimodal Hippocampal Subfield Grading For Alzheimer’s Disease Classification
Hett, K., Ta, V.-T., Catheline, G., Tourdias, T., Manjón, J. V., Coupé, P., Initiative, A. D. N., Weiner, M. W., Aisen, P., Petersen, R., et al. (2019) · Scientific Reports · doi:10.1038/s41598-019-49970-9 · Cited by 49 · see citing works
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Prognostic classification of mild cognitive impairment and Alzheimer׳s disease: MRI independent component analysis
Willette, A. A., Calhoun, V. D., Egan, J. M., & Kapogiannis, D. (2014) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2014.08.005 · Cited by 49 · see citing works
Myelin content changes in probable Alzheimer's disease and mild cognitive impairment: Associations with age and severity of neuropsychiatric impairment
Kavroulakis, E., Simos, P. G., Kalaitzakis, G., Maris, T. G., Karageorgou, D., Zaganas, I. V., Panagiotakis, S., Basta, M., Vgontzas, A. N., & Papadaki, E. Z. (2017) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.25849 · Cited by 49 · see citing works
A Clinical Trial of Transcranial Electromagnetic Treatment in Alzheimer’s Disease: Cognitive Enhancement and Associated Changes in Cerebrospinal Fluid, Blood, and Brain Imaging
Arendash, G. W., Cao, C., Abulaban, H., Baranowski, R., Wisniewski, G., Becerra, L. R., Andel, R., Lin, X., Zhang, X., Wittwer, D., et al. (2019) · Journal of Alzheimer s Disease · doi:10.3233/jad-190367 · Cited by 49 · see citing works
Characterizing White Matter Tract Degeneration in Syndromic Variants of Alzheimer’s Disease: A Diffusion Tensor Imaging Study
Madhavan, A. A., Schwarz, C. G., Duffy, J. R., Strand, E. A., Machulda, M. M., Drubach, D. A., Kantarci, K., Przybelski, S. A., Reid, R. I., Senjem, M. L., et al. (2015) · Journal of Alzheimer s Disease · doi:10.3233/jad-150502 · Cited by 48 · see citing works
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Artificial Intelligence-Based Methodologies for Early Diagnostic Precision and Personalized Therapeutic Strategies in Neuro-Ophthalmic and Neurodegenerative Pathologies
Kumar, R., Waisberg, E., Ong, J., Paladugu, P., Amiri, D., Saintyl, J., Yelamanchi, J., Nahouraii, R., Jagadeesan, R., & Tavakkoli, A. (2024) · Brain Sciences · doi:10.3390/brainsci14121266 · Cited by 51 · see citing works
Discriminative analysis of multivariate features from structural MRI and diffusion tensor images
Li, M., Qin, Y., Gao, F., Zhu, W., & He, X. (2014) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2014.05.008 · Cited by 44 · see citing works
Going round in circles—The Papez circuit in Alzheimer's disease
Forno, G., Lladó, A., & Hornberger, M. (2021) · European Journal of Neuroscience · doi:10.1111/ejn.15494 · Cited by 43 · see citing works
Quantitative T2 mapping of white matter: applications for ageing and cognitive decline
Knight, M. J., McCann, B., Tsivos, D., Dillon, S., Coulthard, E. J., & Kauppinen, R. A. (2016) · Physics in Medicine and Biology · doi:10.1088/0031-9155/61/15/5587 · Cited by 43 · see citing works
State-dependent microstructural white matter changes in drug-naïve patients with first-episode psychosis
Serpa, M. H., Doshi, J. J., Erus, G., Chaim-Avancini, T. M., Cavallet, M., van de Bilt, M. T., Sallet, P. C., Gattaz, W. F., Davatzikos, C. A., Busatto, G. F., et al. (2017) · Psychological Medicine · doi:10.1017/s0033291717001015 · Cited by 42 · see citing works
Central white matter degeneration in bulbar- and limb-onset amyotrophic lateral sclerosis
Cárdenas‐Blanco, A., Machts, J., Acosta‐Cabronero, J., Kaufmann, J., Abdulla, S., Kollewe, K., Petri, S., Heinze, H., Dengler, R., Vielhaber, S., et al. (2014) · Journal of Neurology · doi:10.1007/s00415-014-7434-4 · Cited by 42 · see citing works
White matter integrity and key structures affected in Alzheimer's disease characterized by diffusion tensor imaging
Xiao, D., Wang, K., Theriault, L., Charbel, E., & Initiative, A. D. N. (2022) · European Journal of Neuroscience · doi:10.1111/ejn.15815 · Cited by 40 · see citing works
Cerebellar White Matter Disruption in Alzheimer’s Disease Patients: A Diffusion Tensor Imaging Study
Toniolo, S., Serra, L., Olivito, G., Caltagirone, C., Mercuri, N. B., Marra, C., Cercignani, M., & Bozzali, M. (2020) · Journal of Alzheimer s Disease · doi:10.3233/jad-191125 · Cited by 40 · 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
Differential and subtype-specific neuroimaging abnormalities in amnestic and nonamnestic mild cognitive impairment: A systematic review and meta-analysis
Yeung, M. K.-C., Chau, A. K.-Y., Chiu, J. Y.-C., Shek, J. T.-L., Leung, J. P.-Y., & Wong, T. C.-H. (2022) · Ageing Research Reviews · doi:10.1016/j.arr.2022.101675 · Cited by 39 · see citing works
Effects of low-level sarin and cyclosarin exposure on white matter integrity in Gulf War Veterans
Chao, L. L., Zhang, Y., & Buckley, S. T. (2015) · NeuroToxicology · doi:10.1016/j.neuro.2015.04.005 · Cited by 40 · see citing works
Protein-based amide proton transfer-weighted MR imaging of amnestic mild cognitive impairment
Zhang, Z., Zhang, W., Yao, J., Chen, X., Gao, F., Jiang, S., Chen, W., Zhou, J., & Wang, G. (2019) · NeuroImage Clinical · doi:10.1016/j.nicl.2019.102153 · Cited by 38 · see citing works
Early Segmental White Matter Fascicle Microstructural Damage Predicts the Corresponding Cognitive Domain Impairment in Cerebral Small Vessel Disease Patients by Automated Fiber Quantification
Huang, L., Chen, X., Sun, W., Chen, H., Ye, Q., Yang, D., Li, M., Luo, C., Ma, J., Shao, P., et al. (2021) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2020.598242 · Cited by 40 · see citing works
Obesity gene NEGR1 associated with white matter integrity in healthy young adults
Dennis, E. L., Jahanshad, N., Braskie, M. N., Warstadt, N. M., Hibar, D. P., Omid Kohannim, A., Nir, T. M., McMahon, K. L., de Zubicaray, G. I., Montgomery, G. W., et al. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.07.041 · Cited by 39 · see citing works
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MAGP2 controls Notch via interactions with RGD binding integrins: Identification of a novel ECM–integrin–Notch signaling axis
DeFord, P., Brown, K., Richards, R. L., King, A., Newburn, K., Westover, K., & Albig, A. R. (2016) · Experimental Cell Research · doi:10.1016/j.yexcr.2016.01.011 · Cited by 39 · see citing works
A Multimodal Structural and Functional Neuroimaging Study of Amnestic Mild Cognitive Impairment
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Magnetic Resonance Imaging to Detect Early Molecular and Cellular Changes in Alzheimer's Disease
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Authors: Nir, T. M., Jahanshad, N., Villalon-Reina, J. E., Toga, A. W., Jack, C. R., Weiner, M. W., & Thompson, P. M.
Affiliations: University of California, Los Angeles (US)
Mayo Clinic (US)
University of California, San Francisco (US)
Corresponding author: Thompson, Paul M.
ORCID: Nir, Talia M.
Jahanshad, Neda
Toga, Arthur W.
Jack, Clifford R.
Weiner, Michael W.
Thompson, Paul M.
Venue: NeuroImage: Clinical
Publisher: Elsevier BV
Published: 2013
Volume: 3
Pages: 180-195
ISSN: 2213-1582 (ISSN-L)
DOI: 10.1016/j.nicl.2013.07.006
PMC: PMC3792746
OpenAlex: W2028748733
Cited by: 348
References: 131
Citations by year: 2026: 14 · 2025: 37 · 2024: 28 · 2023: 33 · 2022: 29 · 2021: 31 · 2020: 23 · 2019: 32 · 2018: 22 · 2017: 21 · 2016: 27 · 2015: 30 · 2014: 21
FWCI: 18.49
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by-nc-nd
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
creativecommons.org/licenses/by-nc-nd/3.0/ (vor, 205 d)
Locations: NeuroImage Clinical (publishedVersion, cc-by-nc-nd)
Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
PubMed Central (publishedVersion, cc-by-nc-nd)
PubMed (publishedVersion)
eScholarship (California Digital Library) (submittedVersion, cc-by-nc-nd)
Funders: ADNI (U01 AG024904)
National Institute on Aging
National Institute of Biomedical Imaging and Bioengineering
Abbott
Alzheimer's Association
Alzheimer's Drug Discovery Foundation
Amorfix Life Sciences Ltd.
AstraZeneca
Bayer HealthCare
BioClinica, Inc.
Biogen Idec Inc.
Bristol-Myers Squibb Company
Eisai Inc.
Elan Pharmaceuticals Inc.
Eli Lilly and Company
F. Hoffmann-La Roche Ltd.
Genentech, Inc.
GE Healthcare
Innogenetics, N.V.
IXICO Ltd.
Janssen Alzheimer Immunotherapy Research & Development, LLC.
Johnson & Johnson Pharmaceutical Research & Development LLC.
Medpace, Inc.
Merck & Co., Inc.
Meso Scale Diagnostics, LLC.
Novartis Pharmaceuticals Corporation
Pfizer Inc.
Servier
Synarc Inc.
Takeda Pharmaceutical Company
Canadian Institutes of Health Research
NIH (P30 AG010129, K01 AG030514)
NIA
NIBIB
National Library of Medicine
National Center for Research Resources (AG016570, EB01651, LM05639, RR019771)
Topics: Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
Keywords: Alzheimer's disease, mild cognitive impairment, normal aging, diffusion tensor imaging, fractional anisotropy, white matter diffusivity, white matter microstructure, temporal lobe, neurodegeneration biomarkers, cognitive impairment detection, voxel-based analysis, region of interest analysis

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