Cited by 134Advanced Neuroimaging Techniques and ApplicationsDementia and Cognitive Impairment ResearchFunctional Brain Connectivity Studies

The DOI matches its Crossref record; 103 of the 103 references it cites were found in a registry or an index.

DTI analyses and clinical applications in Alzheimer’s disease

Open access

Oishi, Kenichi, Mielke, Michelle M., Albert, Marilyn, Lyketsos, Constantine G., Mori, Susumu·2011·Journal of Alzheimer's Disease

DOI 10.3233/jad-2011-0007PMC PMC3294372

Open this paper in CoChat — free account

Abstract

DTI is one of the most effective MR tools for the investigation of the brain anatomy. In addition to the gray matter, histopathological studies indicate that white matter is also a good target for both the early diagnosis of AD and for monitoring disease progression, which motivates us to use DTI to study AD patients in vivo. There are already a large amount of studies reporting significant differences between AD patients and controls, as well as to predict progression of disease in symptomatic non-demented individuals. Application of these findings in clinical practice remains to be demonstrated.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.3233/jad-2011-0007

Cites

103 of 103 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

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?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Tract-based spatial statistics: Voxelwise analysis of multi-subject diffusion data
Smith, S. M., Jenkinson, M., Johansen‐Berg, H., Rueckert, D., Nichols, T. E., Mackay, C. E., Watkins, K. E., Ciccarelli, O., Cader, M. Z., Matthews, P. M., et al. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2006.02.024 · Cited by 6,598
Diffusion tensor MR imaging of the human brain
Pierpaoli, C., Jezzard, P., Basser, P. J., Barnett, A., & Di Chiro, G. (1996) · Radiology · doi:10.1148/radiology.201.3.8939209 · Cited by 2,664
Toward a quantitative assessment of diffusion anisotropy
Pierpaoli, C. M., & Basser, P. J. (1996) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1910360612 · Cited by 2,392
Alzheimer's disease: cell-specific pathology isolates the hippocampal formation.
Hyman, B. T., Van Hoesen, G. W., Damasio, A. R., & Barnes, C. L. (1984) · Science · doi:10.1126/science.6474172 · Cited by 2,268
Profound Loss of Layer II Entorhinal Cortex Neurons Occurs in Very Mild Alzheimer’s Disease
Gómez-Isla, T., Price, J. L., McKeel Jr., D. W., Morris, J. C., Growdon, J. H., & Hyman, B. T. (1996) · The Journal of Neuroscience · doi:10.1523/jneurosci.16-14-04491.1996 · Cited by 1,796 · cited 1×
“For instance, the loss of layer III and layer V large pyramidal neurons is seen in cortical association areas [22] and the loss of layer II pyramidal neurons is seen in the entorhinal cortex [23].”
Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution
Tournier, J.-D., Calamante, F., Gadian, D. G., & Connelly, A. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.07.037 · Cited by 1,635
High angular resolution diffusion imaging reveals intravoxel white matter fiber heterogeneity
Tuch, D. S., Reese, T. G., Wiegell, M. R., Makris, N., Belliveau, J. W., & Wedeen, V. J. (2002) · Magnetic Resonance in Medicine · doi:10.1002/mrm.10268 · Cited by 1,548 · cited 1×
“can also extract more parameters from each voxel( = increased intra-voxel information) by using sophisticated non-tensor diffusion analysis methods rather than using a simple tensor approach [14–21].”
Mapping complex tissue architecture with diffusion spectrum magnetic resonance imaging
Wedeen, V. J., Hagmann, P., Tseng, W. I., Reese, T. G., & Weisskoff, R. M. (2005) · Magnetic Resonance in Medicine · doi:10.1002/mrm.20642 · Cited by 1,339
Ways toward an early diagnosis in Alzheimer’s disease: the Alzheimer’s Disease Neuroimaging Initiative (ADNI)
Mueller, S. G., Weiner, M. W., Thal, L. J., Petersen, R. C., Jack, C. R., Jagust, W., Trojanowski, J. Q., Toga, A. W., & Beckett, L. (2005) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2005.06.003 · Cited by 1,233
Axonopathy and Transport Deficits Early in the Pathogenesis of Alzheimer’s Disease
Stokin, G. B., Lillo, C., Falzone, T. L., Brusch, R. G., Rockenstein, E., Mount, S. L., Raman, R., Davies, P., Masliah, E., Williams, D. S., et al. (2005) · Science · doi:10.1126/science.1105681 · Cited by 1,214

Flagged first, then by citations

Show all 103 references

Research works printed but not linked: 3

[6]Pajevic S Pierpaoli C Color schemes to represent the orientation of anisotropic tissues from diffusion tensor data: application to white matter fiber tract mapping in the human brain Magn Reson Med 1999 42 526 540 10467297
[55]Xue R van Zijl PC Crain BJ Solaiyappan M Mori S In vivo three-dimensional reconstruction of rat brain axonal projections by diffusion tensor imaging Magn Reson Med 1999 42 1123 1127 10571934
[65]Zhang H Yushkevich PA Alexander DC Gee JC Deformable registration of diffusion tensorMRimages with explicit orientation optimization Med Image Anal 2006 10 764 785 16899392

Sorted by citations

Degeneracy in model parameter estimation for multi‐compartmental diffusion in neuronal tissue
Jelescu, I. O., Veraart, J., Fieremans, E., & Novikov, D. S. (2016) · NMR in Biomedicine · doi:10.1002/nbm.3450 · Cited by 316
Application of neurite orientation dispersion and density imaging (NODDI) to a tau pathology model of Alzheimer's disease
Colgan, N., Siow, B., O'Callaghan, J. M., Harrison, I. F., Wells, J. A., Holmes, H. E., Ismail, O., Richardson, S., Alexander, D. C., Collins, E. C., et al. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2015.10.043 · Cited by 218
Multimodal analysis of functional and structural disconnection in A lzheimer's disease using multiple kernel SVM
Dyrba, M., Grothe, M. J., Kirste, T., & Teipel, S. J. (2015) · Human Brain Mapping · doi:10.1002/hbm.22759 · Cited by 188
Dissociating Normal Aging from Alzheimer’s Disease: A View from Cognitive Neuroscience
Toepper, M. (2017) · Journal of Alzheimer s Disease · doi:10.3233/jad-161099 · Cited by 191
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
“DTI is widely recognized as a useful tool for studying neurodegenerative disorders such as AD (Oishi et al., 2011; Müller and Kassubek, 2013; Abhinav et al., 2014; Acosta-Cabronero and Nestor, 2014; Maggipinto et al., 2017).”
White Matter Degeneration with Aging: Longitudinal Diffusion MR Imaging Analysis
de Groot, M., Cremers, L. G. M., Ikram, M. A., Albert Hofman, D., Krestin, G. P., van der Lugt, A., Niessen, W. J., & Vernooij, M. W. (2015) · Radiology · doi:10.1148/radiol.2015150103 · Cited by 97
Alzheimer’s disease diagnosis from diffusion tensor images using convolutional neural networks
Marzban, E. N., Eldeib, A. M., Yassine, I. A., Kadah, Y. M., & Initiative, A. D. N. (2020) · PLoS ONE · doi:10.1371/journal.pone.0230409 · Cited by 104
Predicting Prodromal Alzheimer's Disease in Subjects with Mild Cognitive Impairment Using Machine Learning Classification of Multimodal Multicenter Diffusion‐Tensor and Magnetic Resonance Imaging Data
Dyrba, M., Barkhof, F., Fellgiebel, A., Filippi, M., Hausner, L., Hauenstein, K., Kirste, T., Teipel, S. J., & the EDSD study group (2015) · Journal of Neuroimaging · doi:10.1111/jon.12214 · Cited by 99
Review on Alzheimer Disease Detection Methods: Automatic Pipelines and Machine Learning Techniques
Shukla, A., Tiwari, R., & Tiwari, S. (2023) · Sci · doi:10.3390/sci5010013 · Cited by 98
Quantitative evaluation of brain development using anatomical MRI and diffusion tensor imaging
Oishi, K., Faria, A. V., Yoshida, S., Chang, L., & Mori, S. (2013) · International Journal of Developmental Neuroscience · doi:10.1016/j.ijdevneu.2013.06.004 · Cited by 87
Self-citation
MITK Diffusion Imaging
Neher, P., Reicht, I., van Bruggen, T., Goch, C. J., Reisert, M., Nolden, M., Zelzer, S., Meinzer, H.-P., Stieltjes, B., & Fritzsche, K. H. (2012) · Methods of Information in Medicine · doi:10.3414/me11-02-0031 · Cited by 83
White matter abnormalities associated with Alzheimer’s disease and mild cognitive impairment: a critical review of MRI studies
Radanovic, M., Pereira, F., Stella, F., Aprahamian, I., Ferreira, L. K., Forlenza, O. V., & Busatto, G. F. (2013) · Expert Review of Neurotherapeutics · doi:10.1586/ern.13.45 · Cited by 85
Memory binding and white matter integrity in familial Alzheimer’s disease
Parra, M. A., Saarimäki, H., Bastin, M. E., Londoño, A. C., Pettit, L., Lopera, F., Della Sala, S., & Abrahams, S. (2015) · Brain · doi:10.1093/brain/awv048 · Cited by 79
Integrating Retrogenesis Theory to Alzheimer’s Disease Pathology: Insight from DTI-TBSS Investigation of the White Matter Microstructural Integrity
Alves, G. S., Knöchel, V. O., Knöchel, C., Carvalho, A. F., Pantel, J., Engelhardt, E., & Laks, J. (2015) · BioMed Research International · doi:10.1155/2015/291658 · Cited by 78
Alzheimer’s disease and the fornix
Oishi, K., & Lyketsos, C. G. (2014) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2014.00241 · Cited by 76
Self-citation
Dietary Polyphenols as Therapeutic Intervention for Alzheimer’s Disease: A Mechanistic Insight
Bukhari, S. N. A. (2022) · Antioxidants · doi:10.3390/antiox11030554 · Cited by 71
DTI based Alzheimer’s disease classification with rank modulated fusion of CNNs and random forest
De, A., & Chowdhury, A. S. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.114338 · Cited by 63
Early Shifts of Brain Metabolism by Caloric Restriction Preserve White Matter Integrity and Long-Term Memory in Aging Mice
Guo, J., Bakshi, V., & Lin, A. (2015) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2015.00213 · Cited by 63
Serum neurofilament light chain levels are associated with white matter integrity in autosomal dominant Alzheimer's disease
Schultz, S. A., Strain, J. F., Adedokun, A., Wang, Q., Preische, O., Kühle, J., Flores, S. E., Keefe, S., Dincer, A., Ances, B. M., et al. (2020) · Neurobiology of Disease · doi:10.1016/j.nbd.2020.104960 · Cited by 58
Insights into human cerebral white matter maturation and degeneration across the adult lifespan
Kiely, M., Triebswetter, C., Cortina, L. E., Gong, Z., Alsameen, M. H., Spencer, R. G. S., & Bouhrara, M. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118727 · Cited by 54
The association between biomarkers in cerebrospinal fluid and structural changes in the brain in patients with Alzheimer's disease
Li, X., Li, T., Andreasen, N. C., Wiberg, M. K., Westman, E., & Wahlund, L. (2013) · Journal of Internal Medicine · doi:10.1111/joim.12164 · Cited by 58
Association of white matter microstructural integrity with cognition and dementia
Power, M. C., Su, D., Wu, A., Reid, R. I., Jack, C. R., Knopman, D. S., Coresh, J., Huang, J., Kantarci, K., Sharrett, A. R., et al. (2019) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2019.08.021 · Cited by 56
Deep learning with diffusion basis spectrum imaging for classification of multiple sclerosis lesions
Ye, Z., George, A., Wu, A. T., Niu, X., Lin, J. Y.-Y., Adusumilli, G., Naismith, R. T., Cross, A. H., Sun, P., & Song, S. (2020) · Annals of Clinical and Translational Neurology · doi:10.1002/acn3.51037 · Cited by 55
Predicting white matter integrity from multiple common genetic variants
Kohannim, O., Jahanshad, N., Braskie, M. N., Stein, J. L., Chiang, M.-C., Reese, A. H., Hibar, D. P., Toga, A. W., McMahon, K. L., de Zubicaray, G. I., et al. (2012) · Neuropsychopharmacology · doi:10.1038/npp.2012.49 · Cited by 50
Early stage alterations in white matter and decreased functional interhemispheric hippocampal connectivity in the 3xTg mouse model of Alzheimer’s disease.
Manno, F. A. M., Isla, A. G., Manno, S. H. C., Ahmed, I., Cheng, S. H., Barrios, F. A., & Lau, C. (2019) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2019.00039 · Cited by 49
Biomarkers of Alzheimer’s disease: Past, present and future clinical use
Georgakas, J. E., Howe, M. D., Thompson, L. I., Riera, N. M., & Riddle, M. C. (2023) · Biomarkers in Neuropsychiatry · doi:10.1016/j.bionps.2023.100063 · Cited by 52
In vivo quantitative whole‐brain diffusion tensor imaging analysis of APP/PS1 transgenic mice using voxel‐based and atlas‐based methods
Qin, Y.-Y., Li, M.-W., Zhang, S., Zhang, Y., Zhao, L.-Y., Lei, H., Oishi, K., & Zhu, W.-Z. (2013) · Neuroradiology · doi:10.1007/s00234-013-1195-0 · Cited by 42
Self-citation
High-Definition Transcranial Direct Current Stimulation Improves Delayed Memory in Alzheimer’s Disease Patients: A Pilot Study Using Computational Modeling to Optimize Electrode Position
Rasmussen, I. D., Boayue, N. M., Mittner, M., Bystad, M., Grønli, O. K., Vangberg, T. R., Csifcsák, G., & Aslaksen, P. M. (2021) · Journal of Alzheimer's Disease · doi:10.3233/jad-210378 · Cited by 42
Voxel-based diffusion tensor imaging of an APP/PS1 mouse model of Alzheimer’s disease.
Shu, X., Qin, Y.-Y., Zhang, S., Jiang, J.-J., Zhang, Y., Zhao, L.-Y., Shan, D., & Zhu, W.-Z. (2013) · Molecular Neurobiology · doi:10.1007/s12035-013-8418-6 · Cited by 41
The Influence of Genetic Factors and Cognitive Reserve on Structural and Functional Resting-State Brain Networks in Aging and Alzheimer’s Disease
Pietzuch, M., King, A. E., Ward, D. D., & Vickers, J. C. (2019) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2019.00030 · Cited by 45
Structural brain changes in patients with persistent headache after COVID-19 resolution
Planchuelo‐Gómez, Á., García‐Azorín, D., Peral, Á. L. G., Rodríguez, M., Aja‐Fernández, S., & de Luis-Garcı́a, R. (2022) · Journal of Neurology · doi:10.1007/s00415-022-11398-z · Cited by 40
Relationships Between Diffusion Tensor Imaging and Cerebrospinal Fluid Metrics in Early Stages of the Alzheimer’s Disease Continuum
Alm, K. H., & Bakker, A. (2019) · Journal of Alzheimer s Disease · doi:10.3233/jad-181210 · Cited by 38
Longitudinal evaluation of diffusion tensor imaging and cognition in systemic lupus erythematosus
Kozora, E., Filley, C. M., Erkan, D., Uluğ, A. M., Vo, A., Ramón, G., Burleson, A., Zimmerman, R. D., & Lockshin, M. D. (2018) · Lupus · doi:10.1177/0961203318793215 · Cited by 36
Feature selection improves the accuracy of classifying Alzheimer disease using diffusion tensor images
Demi̇rhan, A., Nir, T. M., Zavaliangos‐Petropulu, A., Jack, C. R., Weiner, M. W., Bernstein, M. A., Thompson, P. M., & Jahanshad, N. (2015) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2015.7163832 · Cited by 39
Simultaneous changes in gray matter volume and white matter fractional anisotropy in Alzheimer’s disease revealed by multimodal CCA and joint ICA
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
Fractional anisotropy derived from the diffusion tensor distribution function boosts power to detect Alzheimer’s disease deficits
Nir, T. M., Jahanshad, N., Villalon‐Reina, J. E., Isaev, D., Zavaliangos‐Petropulu, A., Zhan, L., Leow, A. D., Jack, C. R., Weiner, M. W., Thompson, P. M., et al. (2017) · Magnetic Resonance in Medicine · doi:10.1002/mrm.26623 · Cited by 34
Altered Microstructure Within Social-Cognitive Brain Networks During Childhood in Williams Syndrome
Haas, B. W., Barnea‐Goraly, N., Sheau, K. E., Yamagata, B., Ullas, S., & Reiss, A. L. (2013) · Cerebral Cortex · doi:10.1093/cercor/bht135 · Cited by 37
DTI Analysis of Presbycusis Using Voxel-Based Analysis
Ma, W., Li, M., Gao, F., Zhang, X., Shi, L., Yu, L., Zhao, B., Chen, W., Wang, G., & Wang, X. (2016) · American Journal of Neuroradiology · doi:10.3174/ajnr.a4870 · Cited by 33
In vivo parahippocampal white matter pathology as a biomarker of disease progression to Alzheimer's disease
Solodkin, A., Chen, E. E., Hoesen, G. W. V., Heimer, L., Shereen, A. D., Kruggel, F., & Mastrianni, J. A. (2013) · The Journal of Comparative Neurology · doi:10.1002/cne.23418 · Cited by 28
In vivo detection of gray matter neuropathology in the 3xTg mouse model of alzheimer’s disease with diffusion tensor imaging.
Snow, W. M., Dale, R., O’Brien-Moran, Z., Buist, R., Peirson, D., Martin, M., & Albensi, B. C. (2017) · Journal of Alzheimer s Disease · doi:10.3233/jad-170136 · Cited by 29
Plasma neurofilament light levels correlate with white matter damage prior to Alzheimer’s disease: results from ADNI
Nabizadeh, F., Balabandian, M., Rostami, M. R., Kankam, S. B., Ranjbaran, F., Pourhamzeh, M., & Initiative, A. D. N. (2022) · Aging Clinical and Experimental Research · doi:10.1007/s40520-022-02095-y · Cited by 30
Cerebrovascular Assessment for the Risk Prediction of Alzheimer's Disease
Silvestrini, M., Viticchi, G., Altamura, C., Luzzi, S., Balucani, C., & Vernieri, F. (2012) · Journal of Alzheimer s Disease · doi:10.3233/jad-2012-121240 · Cited by 29
In vivo diffusion tensor imaging of amyloid‐β‐induced white matter damage in mice
Sun, S.-W., Liang, H.-F., Mei, J., Xu, D., & Shi, W.-X. (2013) · Journal of Alzheimer s Disease · doi:10.3233/jad-130236 · Cited by 27
Atherosclerotic Risk Factors, Vascular Cognitive Impairment, and Alzheimer Disease
Kovacic, J. C., & Fuster, V. (2012) · Mount Sinai Journal of Medicine A Journal of Translational and Personalized Medicine · doi:10.1002/msj.21347 · Cited by 27
RNN-Based Alzheimer's Disease Prediction from Prodromal Stage using Diffusion Tensor Imaging
Velazquez, M., Anantharaman, R., Velazquez, S., & Lee, Y. (2019) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm47256.2019.8983391 · Cited by 27
Destruction of White Matter Integrity in Patients with Mild Cognitive Impairment and Alzheimer Disease
Sun, X., Salat, D. H., Upchurch, K., Deason, R. G., Kowall, N. W., & Budson, A. E. (2014) · Journal of Investigative Medicine · doi:10.1097/jim.0000000000000102 · Cited by 25
Analysis of longitudinal head impact exposure and white matter integrity in returning youth football players
Kelley, M. E., Urban, J. E., Jones, D. A., Davenport, E. M., Miller, L. E., Snively, B., Powers, A. K., Whitlow, C. T., Maldjian, J. A., & Stitzel, J. D. (2021) · Journal of Neurosurgery Pediatrics · doi:10.3171/2021.1.peds20586 · Cited by 25
Entrack: Probabilistic Spherical Regression with Entropy Regularization for Fiber Tractography
Wegmayr, V., & Buhmann, J. M. (2020) · International Journal of Computer Vision · doi:10.1007/s11263-020-01384-1 · Cited by 23
Microstructural Neurodegeneration of the Entorhinal-Hippocampus Pathway along the Alzheimer’s Disease Continuum
Uchida, Y., Onda, K., Hou, Z., Troncoso, J. C., Mori, S., & Oishi, K. (2023) · Journal of Alzheimer s Disease · doi:10.3233/jad-230452 · Cited by 23
Self-citation
NODDI in gray matter is a sensitive marker of aging and early AD changes
Yu, X., Przybelski, S. A., Reid, R. I., Lesnick, T. G., Raghavan, S., Graff‐Radford, J., Lowe, V. J., Kantarci, K., Knopman, D. S., Petersen, R. C., et al. (2024) · Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring · doi:10.1002/dad2.12627 · Cited by 23
An Efficient Approach for Differentiating Alzheimer's Disease from Normal Elderly Based on Multicenter MRI Using Gray-Level Invariant Features
Li, M., Oishi, K., He, X., Qin, Y., Gao, F., Mori, S., & Initiative, A. D. N. (2014) · PLoS ONE · doi:10.1371/journal.pone.0105563 · Cited by 23
Self-citation
Evoked and induced EEG oscillations to visual targets reveal a differential pattern of change along the spectrum of cognitive decline in Alzheimer's Disease
Tülay, E. E., Güntekin, B., Yener, G., Bayram, A., Başar‐Eroğlu, C., & Demıralp, T. (2020) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2020.06.001 · Cited by 22
Central Nervous System Involvement in Primary Sjögren’s Syndrome: Narrative Review of MRI Findings
Módis, L., Aradi, Z., Horváth, I. F., Bencze, J., Papp, T., Emri, M., Berényi, E. L., Bugán, A., & Szántó, A. (2022) · Diagnostics · doi:10.3390/diagnostics13010014 · Cited by 21
High-throughput neuro-imaging informatics
Miller, M. I., Faria, A. V., Oishi, K., & Mori, S. (2013) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2013.00031 · Cited by 21
Self-citation
Discriminating subcortical ischemic vascular disease and Alzheimer's disease by diffusion kurtosis imaging in segregated thalamic regions
Tu, M., Huang, S., Hsu, Y., Yang, J., Lin, C.-Y. E., & Kuo, L. (2021) · Human Brain Mapping · doi:10.1002/hbm.25342 · Cited by 18
Diffusion Tensor Imaging in the Study of Aging and Age-Associated Neural Disease
Salat, D. H. (2014) · Elsevier eBooks · doi:10.1016/b978-0-12-396460-1.00012-3 · Cited by 21
Hippocampal correlates of episodic memory in Parkinson’s disease: A systematic review of magnetic resonance imaging studies
Pourzinal, D., Yang, J., Bakker, A., McMahon, K. L., Byrne, G. J., Pontone, G. M., Mari, Z. K., & Dissanayaka, N. N. (2021) · Journal of Neuroscience Research · doi:10.1002/jnr.24863 · Cited by 19
Imaging the Alzheimer Brain
Ashford, J. W., Salehi, A., Furst, A. J., Bayley, P. J., Frisoni, G. B., Jack, C. R., Sabri, O., Adamson, M. M., Coburn, K. L., Olichney, J., et al. (2011) · Journal of Alzheimer s Disease · doi:10.3233/jad-2011-0073 · Cited by 20
Atlas-Based Diffusion Tensor Imaging Correlates of Executive Function
Nowrangi, M. A., Okonkwo, O., Lyketsos, C., Oishi, K., Mori, S., Albert, M., & Mielke, M. M. (2015) · Journal of Alzheimer's Disease · doi:10.3233/jad-141937 · Cited by 19
Self-citation
The trouble with free-water elimination using single-shell diffusion MRI data: A case study in ageing
Correia, M., Henriques, R. N., Golub, M., Winzeck, S., & Nunes, R. G. (2024) · Imaging Neuroscience · doi:10.1162/imag_a_00252 · Cited by 19
FLAIR MRI biomarkers of the normal appearing brain matter are related to cognition
Bahsoun, M.-A., Khan, M. U., Mitha, S., Ghazvanchahi, A., Khosravani, H., Maralani, P. J., Tardif, J., Moody, A. R., Tyrrell, P. N., & Khademi, A. (2022) · NeuroImage Clinical · doi:10.1016/j.nicl.2022.102955 · Cited by 18
Neurite orientation dispersion and density imaging in a rodent model of acute mild traumatic brain injury
McCunn, P., Xu, X., Moszczynski, A. J., Li, A., Brown, A. M., & Bartha, R. (2021) · Journal of Neuroimaging · doi:10.1111/jon.12917 · Cited by 18
Multicenter Tract-Based Analysis of Microstructural Lesions within the Alzheimer’s Disease Spectrum: Association with Amyloid Pathology and Diagnostic Usefulness
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
Longitudinal Changes in Brain Diffusion Characteristics Associated With Cognition and Vascular Risk Factors
Uchida, Y., Onda, K., Nishimaki, K., Kucharska‐Newton, A., Windham, B. G., Wasserman, B. A., & Oishi, K. (2025) · Neurology · doi:10.1212/wnl.0000000000213867 · Cited by 17
Self-citation
Cloud-Based Brain Magnetic Resonance Image Segmentation and Parcellation System for Individualized Prediction of Cognitive Worsening
Sakamoto, R., Marano, C. M., Miller, M. I., Lyketsos, C. G., Li, Y., Mori, S., Oishi, K., & Initiative, A. D. N. (2019) · Journal of Healthcare Engineering · doi:10.1155/2019/9507193 · Cited by 16
Self-citation
Independent Component Analysis-Based Identification of Covariance Patterns of Microstructural White Matter Damage in Alzheimer’s Disease
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
Short‐term white matter alterations in Alzheimer's disease characterized by diffusion tensor imaging
Genc, S., Steward, C. E., Malpas, C. B., Velakoulis, D., O'Brien, T. J., & Desmond, P. M. (2015) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.25017 · Cited by 14
Rapid microscopic fractional anisotropy imaging via an optimized linear regression formulation
Arezza, N. J. J., Tse, D. H. Y., & Baron, C. A. (2021) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2021.04.015 · Cited by 14
Toward diffusion tensor imaging as a biomarker in neurodegenerative diseases: technical considerations to optimize recordings and data processing
Müller, H., & Kassubek, J. (2024) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2024.1378896 · Cited by 13
Multiparametric MRI for the improved diagnostic accuracy of Alzheimer’s disease and mild cognitive impairment: Research protocol of a case-control study design
Piersson, A. D., Ibrahim, B., Suppiah, S., Mohamad, M., Hassan, H. A., Omar, N. F., Ibrahim, M. I., Yusoff, A. N., Ibrahim, N., Saripan, M. I., et al. (2021) · PLoS ONE · doi:10.1371/journal.pone.0252883 · Cited by 14
Medial temporal lobe white matter pathway variability is associated with individual differences in episodic memory in cognitively normal older adults
Alm, K. H., Faria, A. V., Moghekar, A., Pettigrew, C. A., Soldan, A., Mori, S., Albert, M., & Bakker, A. (2019) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2019.11.011 · Cited by 13
Self-citation
Neuroimaging Biomarkers in Alzheimer’s Disease
Khan, T. K. (2016) · Elsevier eBooks · doi:10.1016/b978-0-12-804832-0.00003-1 · Cited by 11
Decreased white matter integrity before the onset of delusions in patients with Alzheimer's disease: diffusion tensor imaging
Nakaaki, S., Sato, Torii, Oka, Negi, Nakamae, T., Narumoto, J., Miyata, Furukawa, T. A., & Mimura (2012) · Neuropsychiatric Disease and Treatment · doi:10.2147/ndt.s38942 · Cited by 12
Molecular and Diffusion Tensor Imaging Biomarkers of Traumatic Brain Injury: Principles for Investigation and Integration
Turner, S. B., Lazarus, R. C., Marion, D. W., & Main, K. (2021) · Journal of Neurotrauma · doi:10.1089/neu.2020.7259 · Cited by 11
Baroreflex functionality in the eye of diffusion tensor imaging
Tsai, C., Poon, Y., Chan, J. Y. H., & Chan, S. H. H. (2018) · The Journal of Physiology · doi:10.1113/jp277008 · Cited by 11
Disrupted white matter integrity is associated with cognitive deficits in patients with amnestic mild cognitive impairment: An atlas-based study
Liu, D., Wang, Z., Shu, H., & Zhang, Z. (2016) · SAGE Open Medicine · doi:10.1177/2050312116648812 · Cited by 10
A dedicated eight‐channel receive RF coil array for monkey brain MRI at 9.4 T
Li, M., Li, Y., Jin, J., Yang, Z., Zhang, B., Liu, Y., Song, M., Freakly, C., Ewald, W., Liu, F., et al. (2020) · NMR in Biomedicine · doi:10.1002/nbm.4369 · Cited by 8
Association of Olfaction and Microstructural Integrity of Brain Tissue in Community-Dwelling Adults
Shrestha, S., Zhu, X., Sullivan, K., Blackshear, C. T., Deal, J. A., Sharrett, A. R., Kamath, V. J., Schneider, A. L. C., Jack, C. R., Huang, J., et al. (2023) · Neurology · doi:10.1212/wnl.0000000000207636 · Cited by 8
Early Prediction and Analysis of DTI and MRI-Based Alzheimer’s Disease Through Machine Learning Techniques
Mahjabeen, A., Mia, M. R., Islam, S., Faruqui, N., & Mahmud, I. (2023) · Lecture notes in networks and systems · doi:10.1007/978-981-19-9483-8_1 · Cited by 7
Cortical Diffusivity, a Biomarker for Early Neuronal Damage, Is Associated with Amyloid-β Deposition: A Pilot Study
Debatisse, J., Leng, F., Ashraf, A., & Edison, P. (2025) · Cells · doi:10.3390/cells14030155 · Cited by 6
Tensor Completion for Alzheimer's Disease Prediction From Diffusion Tensor Imaging
Gou, Y., Liu, Y., He, F., Hunyadi, B., & Zhu, C. (2024) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2024.3365131 · Cited by 8
SVM-Based Classification of Diffusion Tensor Imaging Data for Diagnosing Alzheimer’s Disease and Mild Cognitive Impairment
Lee, W., Park, B., & Han, K. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-22186-1_49 · Cited by 8
Risk Assessment of Alzheimer’s Disease using the Information Diffusion Model from Structural Magnetic Resonance Imaging
Beheshti, I., Olya, H. G. T., Demirel, H., & Initiative, A. D. N. (2016) · Journal of Alzheimer s Disease · doi:10.3233/jad-151176 · Cited by 6
Positron Emission Tomography in Alzheimer Disease
Schmidt, M. E., Matthews, D. C., Andrews, R. D., & Mosconi, L. (2012) · Elsevier eBooks · doi:10.1016/b978-0-12-386945-6.00005-6 · Cited by 7
Assessment of complementary white matter microstructural changes and grey matter atrophy in a preclinical model of Alzheimer’s disease
Bergamino, M., Nelson, M. R., Numani, A., Scarpelli, M., Healey, D., Fuentes, A., Turner, G., & Stokes, A. M. (2023) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2023.03.024 · Cited by 4
Clinical Applications of PET/MRI in Brain Imaging
Fraioli, F., & Almansory, K. O. (2019) · Springer eBooks · doi:10.1007/978-3-030-01523-7_13 · Cited by 4
s2MRI-ADNet: an interpretable deep learning framework integrating Euclidean-graph representations of Alzheimer’s disease solely from structural MRI
Song, Z., Li, H., Zhang, Y., Zhu, C., Jiang, M., Song, L., Wang, Y., Ouyang, M., Hu, F., & Zheng, Q. (2024) · Magnetic Resonance Materials in Physics Biology and Medicine · doi:10.1007/s10334-024-01178-3 · Cited by 6
Learning semi-supervised enrichment of longitudinal imaging-genetic data for improved prediction of cognitive decline
Seo, H., Brand, L., Wang, H., & Initiative, A. D. N. (2024) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-024-02455-w · Cited by 3
DTI versus NODDI White Matter Microstructural Biomarkers of Alzheimer’s Disease
Liou, K., Thomopoulos, S. I., Yoo, H., Shuai, Y., Chehrzadeh, S., Arani, A., Borowski, B., Reid, R. I., Jack, C. R., Weiner, M. W., et al. (2025) · International Symposium on Medical Information Processing and Analysis (SIPAIM) · doi:10.1109/sipaim67325.2025.11283294 · Cited by 4
Independent component analysis of DTI data reveals white matter covariances in Alzheimer's disease
Ouyang, X., Sun, X., Guo, T., Sun, Q., Chen, K., Li, Y., Wu, X., & Guo, X. (2014) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2042237 · Cited by 3
Time-Optimized High-Resolution Readout-Segmented Diffusion Tensor Imaging
Reishofer, G., Koschutnig, K., Langkammer, C., Porter, D. A., Jehna, M., Enzinger, C., Keeling, S. L., & Ebner, F. (2013) · PLoS ONE · doi:10.1371/journal.pone.0074156 · Cited by 3
Associations between diffusion tensor imaging patterns and cerebrospinal fluid markers in mild cognitive impairment
Niknam, N., Khaefi, S., Heidarpour, H., Sadeghi, M., Jafari, N. A., Mohammadi, S., Ahmadi, Z. S., Ahangar‐Sirous, R., Mayeli, M., & Seyedmirzaei, H. (2025) · Journal of Clinical Neuroscience · doi:10.1016/j.jocn.2025.111141 · Cited by 3
Towards Visual Mega-Analysis of Voxel-based Measurement in Brain Cohorts
Zhang, G., Kochunov, P., Hong, E. L., Carr, H. A., & Chen, J. (2016) · Eurographics · doi:10.2312/eurovisshort.20161161 · Cited by 3
A multi-view DTI feature fusion framework for enhanced diagnosis of Alzheimer’s disease
Qiao, J., Zhou, G., Wu, S., Shang, H., Yuan, Q., & Sun, J. (2026) · Neuroscience · doi:10.1016/j.neuroscience.2026.01.024 · Cited by 2
Role of Positron Emission Tomography/Computed Tomography in Dementia
Hinds, S. R., Stocker, D. J., & BRADLEY, Y. C. (2013) · Radiologic Clinics of North America · doi:10.1016/j.rcl.2013.06.002 · Cited by 2
Influence of age and sex on presymptomatic phases of neurodegenerative diseases: Focus on multiple sclerosis and Alzheimer's disease
Zeydan, B., & Kantarci, K. (2025) · Revue Neurologique · doi:10.1016/j.neurol.2025.06.015 · Cited by 2
Diffusion tensor imaging analysis of aged dogs with and without canine cognitive dysfunction
Henry, J., Ukai, M., Antonakakis, M., Zhou, T., Moreno, J. A., Burzynska, A. Z., & McGrath, S. (2025) · BMC Veterinary Research · doi:10.1186/s12917-025-04973-9 · Cited by 2
Standardization of MRI and Amyloid Imaging
Grothe, M. J., Kurth, J., Hampel, H. J., Krause, B. J., & Teipel, S. J. (2013) · Elsevier eBooks · doi:10.1016/b978-0-12-411464-7.00009-2 · Cited by 2
Different macaque brain network remodeling after spinal cord injury and NT3 treatment
Feng, T., Zhao, C. H., Rao, J., Guo, X.-J., Bao, S.-S., He, L., Zhao, W., Liu, Z., Yang, Z., & Li, X. (2023) · iScience · doi:10.1016/j.isci.2023.106784 · Cited by 2
Associations between APOE‐TOMM40 ‘523 haplotypes and limbic system white matter microstructure
Mooney, K. E., Archer, D. B., Sathe, A., Hohman, T. J., Kadiri, O., Lamar, M., Arfanakis, K., Yu, L., Barnes, L. L., & Deters, K. (2025) · Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring · doi:10.1002/dad2.70099 · Cited by 1

Authors: Oishi, K., Mielke, M. M., Albert, M., Lyketsos, C. G., & Mori, S.
Affiliations: Johns Hopkins University (US)
Kennedy Krieger Institute (US)
Corresponding author: Oishi, Kenichi
ORCID: Oishi, Kenichi
Mielke, Michelle M.
Albert, Marilyn
Lyketsos, Constantine G.
Mori, Susumu
Venue: Journal of Alzheimer's Disease
Publisher: SAGE Publications
Published: 2011-10-04
Volume: 26
Issue: s3
Pages: 287-296
ISSN: 1387-2877 (ISSN-L)
1875-8908
DOI: 10.3233/jad-2011-0007
PMC: PMC3294372
OpenAlex: W1556823089
Cited by: 134
References: 103
Citations by year: 2026: 14 · 2025: 8 · 2024: 12 · 2023: 9 · 2022: 8 · 2021: 10 · 2020: 9 · 2019: 11 · 2018: 3 · 2017: 3 · 2016: 5 · 2015: 14 · 2014: 6 · 2013: 14 · 2012: 7
FWCI: 6.86
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Locations: Journal of Alzheimer s Disease (publishedVersion)
PubMed (publishedVersion)
Europe PMC (PubMed Central) (acceptedVersion)
PubMed Central (acceptedVersion)
Funders: Johns Hopkins University
National Institutes of Health
National Institute on Aging (R01AG020012, R21AG033774, R01-AG20012, P50AG005146)
National Institute of Biomedical Imaging and Bioengineering (P01EB00195)
National Center for Research Resources (P41RR015241, U24RR021382)
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
Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
Keywords: diffusion tensor imaging, Alzheimer's disease
MeSH terms: Aged, Alzheimer Disease, Brain, Brain Mapping, Cognition Disorders, Female, Humans, Male, Neuropsychological Tests, Psychiatric Status Rating Scales, Diffusion Magnetic Resonance Imaging

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

DTI analyses and clinical applications in Alzheimer’s disease

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks