The DOI matches its Crossref record; 49 of the 49 references it cites were found in a registry or an index.
The Natural History of Alzheimer's Disease: Description of Study Cohort and Accuracy of Diagnosis
Abstract
OBJECTIVE: We describe the sampling, initial evaluation, and final diagnostic classification of subjects enrolled in a natural history study of Alzheimer's disease (AD). DESIGN: Volunteer cohort study. SETTING: Multidisciplinary behavioral neurology research clinic. PATIENTS OR OTHER PARTICIPANTS: Three-hundred nineteen individuals were enrolled in the Alzheimer Research Program between March 1983 and March 1988. Of these, 204 were originally classified with AD, 102 were normal elderly control subjects, and 13 were considered special cases. MAIN OUTCOME MEASURES: Final consensus clinical diagnosis, final neuropathologic diagnosis, and death. RESULTS: Of the 204 patients enrolled in the study, re-review after as many as 5 years of follow-up resulted in a final clinical classification of 188 with probable AD. Seven patients were believed to have a significant vascular component to the dementia, three were found to have developed depression, and six were excluded on other clinical grounds. Neuropathologic examination of 50 brains indicated definite AD in 43. After removing these seven misdiagnosed patients, the final group of probable/definite AD totaled 181 individuals. Accuracy of the baseline clinical diagnosis relative to neuropathology was 86%, and when follow-up clinical data were considered, 91.4%. Detailed neuropsychological testing yielded high sensitivity (0.988) and specificity (0.983) to dementia. Analyses of survival time from study entry until death revealed that older patients were significantly more likely to die during follow-up, but neither sex, years of education, nor pattern of cognitive impairment were related to survival. CONCLUSIONS: These data provide the descriptive basis for future studies of this cohort. They indicate that longitudinal follow-up of demented cases increases accuracy of diagnosis, and that detailed cognitive testing aids in early classification.
Overview
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
Cites
Statistics
Verification
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.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
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
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
VALIDITY OF THE TRAIL MAKING TEST AS AN INDICATOR OF ORGANIC BRAIN DAMAGE
Reitan, R. M. (1958) · Perceptual and Motor Skills · doi:10.2466/pms.1958.8.3.271 · Cited by 8,167
Blessed, G., Tomlinson, B. E., & Roth, M. (1968) · British Journal of Psychiatry · doi:10.1192/bjp.114.512.797 · Cited by 4,117
The assessment of aphasia and related disorders
Goodglass, H., & Kaplan, E. (1972) · Medical Entomology and Zoology · Cited by 3,258
. Cerebral blood flow in dementia.
Hachinski, V. C., Iliff, L. D., Zilhka, E., Du Boulay, G. H., McAllister, V. L., Marshall, J., Russell, R. W. R., & Symon, L. (1975) · Archives of Neurology · doi:10.1001/archneur.1975.00490510088009 · Cited by 3,186
A Standardized Memory Scale for Clinical Use
Wechsler, D. (1945) · The Journal of Psychology · doi:10.1080/00223980.1945.9917223 · Cited by 2,949
BMPD statistical software manual
Dixon, W. (1988) · University of California Press eBooks · Cited by 2,536
Diagnosis of Alzheimer's Disease
Khachaturian, Z. S. (1985) · Archives of Neurology · doi:10.1001/archneur.1985.04060100083029 · Cited by 2,499
Le test de copie d'une figure complexe
Osterrieth, P. M. (1944) · Medical Entomology and Zoology · Cited by 2,336
Cited by
Sorted by citations
Items 101–200 of 576
Alzheimer’s Dementia Recognition From Spontaneous Speech Using Disfluency and Interactional Features
Nasreen, S., Rohanian, M., Hough, J., & Purver, M. (2021) · Frontiers in Computer Science · doi:10.3389/fcomp.2021.640669 · Cited by 54
A Method for Analysis of Patient Speech in Dialogue for Dementia Detection
Luz, S. F., de la Fuente, S., & Albert, P. (2018) · Edinburgh Research Explorer (University of Edinburgh) · doi:10.48550/arxiv.1811.09919 · Cited by 58
Personalized Adversarial Data Augmentation for Dysarthric and Elderly Speech Recognition
Jin, Z., Geng, M., Deng, J., Wang, T., Hu, S., Li, G., & Liu, X. (2023) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2023.3323888 · Cited by 52
Multiscale System for Alzheimer’s Dementia Recognition Through Spontaneous Speech
Edwards, E., Dognin, C., Bollepalli, B., & Singh, M. (2020) · Interspeech (USB) · doi:10.21437/interspeech.2020-2781 · Cited by 48
Executive functions and Alzheimer's disease: problems and prospects
Patterson, M. B., Mack, J. L., Geldmacher, D., & Whitehouse, P. J. (1996) · European Journal of Neurology · doi:10.1111/j.1468-1331.1996.tb00182.x · Cited by 44
Guo, Y., Li, C., Roan, C., Pakhomov, S., & Cohen, T. (2021) · Frontiers in Computer Science · doi:10.3389/fcomp.2021.642517 · Cited by 44
Automatic Detection of Alzheimer’s Disease Using Spontaneous Speech Only
Chen, J., Ye, J., Tang, F., & Zhou, J. (2021) · Interspeech (USB) · doi:10.21437/interspeech.2021-2002 · Cited by 44
Progress in Clinical Neurosciences: Cognitive Markers of Progression in Alzheimer's Disease
Behl, P., Stefurak, T. L., & Black, S. E. (2005) · Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques · doi:10.1017/s0317167100003917 · Cited by 43
Evaluating voice-assistant commands for dementia detection
Liang, X., Batsis, J. A., Zhu, Y., Driesse, T. M., Roth, R. M., Kotz, D., & MacWhinney, B. (2022) · Computer Speech & Language · doi:10.1016/j.csl.2021.101297 · Cited by 43
LMVD: A large-scale multimodal vlog dataset for depression detection in the wild
He, L., Chen, K., Zhao, J., Wang, Y., Pei, E., Chen, H., Jiang, J., Zhang, S., Zhang, J., Wang, Z., et al. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103632 · Cited by 48
A finer-grained linguistic profile of Alzheimer's disease and Mild Cognitive Impairment
Chapin, K., Clarke, N., Garrard, P., & Hinzen, W. (2022) · Journal of Neurolinguistics · doi:10.1016/j.jneuroling.2022.101069 · Cited by 51
Zolnoori, M., Zolnour, A., & Topaz, M. (2023) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2023.102624 · Cited by 43
Validation of Consensus Panel Diagnosis in Dementia
Gabel, M. J., Foster, N. L., Heidebrink, J. L., Higdon, R., Aizenstein, H. J., Arnold, S. E., Barbas, N. R., Boeve, B. F., Burke, J. R., Clark, C. M., et al. (2010) · Archives of Neurology · doi:10.1001/archneurol.2010.301 · Cited by 41
Kaya, H., Fedotov, D., Dresvyanskiy, D., Doyran, M., Mamontov, D., Markitantov, M., Salah, A. A. A., Kavcar, E., Karpov, A. A., & Salah, A. A. (2019) · doi:10.1145/3347320.3357691 · Cited by 41
What Drives Task Performance During Animal Fluency in People With Alzheimer’s Disease?
Rofes, A., de Aguiar, V., Jonkers, R., Oh, S. J., DeDe, G., & Sung, J. E. (2020) · Frontiers in Psychology · doi:10.3389/fpsyg.2020.01485 · Cited by 44
Exploring Self-Supervised Pre-Trained ASR Models for Dysarthric and Elderly Speech Recognition
Hu, S., Xie, X., Jin, Z., Geng, M., Wang, Y., Cui, M., Deng, J., Liu, X., & Meng, H. M. L. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10097275 · Cited by 39
Noninvasive automatic detection of Alzheimer's disease from spontaneous speech: a review
Qi, X., Zhou, Q., Dong, J., & Bao, W. (2023) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2023.1224723 · Cited by 44
dos Santos, L., Júnior, E. A. C., Oliveira, O. N., Amancio, D. R., Mansur, L. L., & Aluísio, S. M. (2017) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p17-1118 · Cited by 49
Morgan, D., Funk, M., Crossley, M., Basran, J., Kirk, A., & Bello-Haas, V. P. M. D. (2007) · Canadian Journal on Aging / La Revue canadienne du vieillissement · doi:10.3138/1457-2411-v402-62l1 · Cited by 39
Data Representativeness in Accessibility Datasets: A Meta-Analysis
Kamikubo, R., Wang, L., Marte, C., Mahmood, A., & Kacorri, H. (2022) · International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS) · doi:10.1145/3517428.3544826 · Cited by 46
Self-Supervised ASR Models and Features for Dysarthric and Elderly Speech Recognition
Hu, S., Xie, X., Geng, M., Jin, Z., Deng, J., Li, G., Wang, Y., Cui, M., Wang, T., Meng, H. M. L., et al. (2024) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2024.3422839 · Cited by 40
Detecting Alzheimer’s Disease from Continuous Speech Using Language Models
Guo, Z., Ling, Z.-H., & Li, Y. (2019) · Journal of Alzheimer s Disease · doi:10.3233/jad-190452 · Cited by 37
Detecting Alzheimer’s Disease by Exploiting Linguistic Information from Nepali Transcript
Thapa, S., Adhikari, S., Naseem, U., Singh, P., Bharathy, G. K., & Prasad, M. (2020) · Communications in computer and information science · doi:10.1007/978-3-030-63820-7_20 · Cited by 38
Stark, B. C., & Fukuyama, J. (2020) · Language Cognition and Neuroscience · doi:10.1080/23273798.2020.1862258 · Cited by 42
Luz, S. F., Haider, F., Fromm, D., Lazarou, I., Kompatsiaris, I. Y., & MacWhinney, B. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10433923 · Cited by 42
Idea density for predicting Alzheimer's disease from transcribed speech
Sirts, K., Piguet, O., & Johnson, M. (2017) · Conference on Computational Natural Language Learning · doi:10.18653/v1/k17-1033 · Cited by 39
Automatic Hierarchical Attention Neural Network for Detecting AD
Pan, Y., Mirheidari, B., Reuber, M., Venneri, A., Blackburn, D. J., & Christensen, H. (2019) · Interspeech (USB) · doi:10.21437/interspeech.2019-1799 · Cited by 38
An Automated Approach to Examining Pausing in the Speech of People With Dementia
Sluis, R. A., Angus, D. J., Wiles, J., Back, A. D., Gibson, T., Liddle, J., Worthy, P., Copland, D. A., & Angwin, A. J. (2020) · American Journal of Alzheimer s Disease & Other Dementias® · doi:10.1177/1533317520939773 · Cited by 34
A Deep Learning-Based Multimodal Architecture to predict Signs of Dementia
Ortiz-Pérez, D., Ruiz-Ponce, P., Tomás, D., García‐Rodríguez, J., Vizcaya-Moreno, M. F., & Leo, M. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126413 · Cited by 41
Verma, M., Beaulieu‐Abdelahad, D., Ait‐Ghezala, G., Li, R., Crawford, F., MULLAN, M. J., & Paris, D. (2015) · PLoS ONE · doi:10.1371/journal.pone.0128224 · Cited by 36
Ye, Z., Hu, S., Li, J., Xie, X., Geng, M., Yu, J., Xu, J., Xue, B., Liu, S., Liu, X., et al. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9413634 · Cited by 36
Clinical outcomes of possible versus probable Alzheimer’s disease
Villareal, D. T., Grant, E., Miller, J. P., Storandt, M., McKeel, D. W., & Morris, J. C. (2003) · Neurology · doi:10.1212/wnl.61.5.661 · Cited by 35
Memory functions and rCBF 99mTc-HMPAO SPET: developing diagnostics in Alzheimer's disease
Elgh, E., Sundström, T., Näsman, B., Åhlström, K., & Nyberg, L. H. (2002) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-002-0829-y · Cited by 35
Liu, Z., Guo, Z., Ling, Z., & Li, Y. (2021) · ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp39728.2021.9413566 · Cited by 34
Liu, J., Fu, F., Li, L., Yu, J., Zhong, D., Zhu, S., Zhou, Y., Liu, B., & Li, J. (2023) · Brain Sciences · doi:10.3390/brainsci13030477 · Cited by 36
Absence of Amusia and Preserved Naming of Musical Instruments in an Aphasic Composer
Tzortzis, C., Goldblum, M., Dang, M., Forette, F., & Boller, F. (2000) · Cortex · doi:10.1016/s0010-9452(08)70526-4 · Cited by 34
Chen, J., Zhu, J., & Ye, J. (2019) · Interspeech (USB) · doi:10.21437/interspeech.2019-2872 · Cited by 33
Li, J., Yu, J., Ye, Z., Wong, K. H., Mak, M., Mak, B. K.-W., Liu, X., & Meng, H. M. L. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9414147 · Cited by 35
Jang, H., Soroski, T., Rizzo, M., Barral, O., Harisinghani, A., Newton‐Mason, S. M., Granby, S., Vasco, T. D. C., Lewis, C., Tutt, P., et al. (2021) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2021.716670 · Cited by 42
Rockwood, K. J., Howard, K. A., Thomas, V. S., Mallery, L. H., MacKnight, C., SANGALANG, V. E., & Darvesh, S. (1998) · International Psychogeriatrics · doi:10.1017/s1041610298005146 · Cited by 34
Detecting late-life depression in Alzheimer's disease through analysis of speech and language
Fraser, K., Rudzicz, F., & Hirst, G. (2016) · Workshop on Computational Linguistics and Clinical Psychology (CLPsych) · doi:10.18653/v1/w16-0301 · Cited by 34
Using narratives in differential diagnosis of neurodegenerative syndromes
Faroqi-Shah, Y., Treanor, A., Ratner, N. B., Ficek, B., Webster, K., & Tsapkini, K. (2020) · Journal of Communication Disorders · doi:10.1016/j.jcomdis.2020.105994 · Cited by 32
Independent effects of Alzheimer's disease on neuropsychological functioning.
Salthouse, T. A., & Becker, J. T. (1998) · Neuropsychology · doi:10.1037/0894-4105.12.2.242 · Cited by 32
Self-citationWang, N., Chen, M., & Subbalakshmi, K. P. (2020) · medRxiv · doi:10.1101/2020.06.24.20139592 · Cited by 35
Improving Alzheimer's Disease Detection for Speech Based on Feature Purification Network
Liu, N., Yuan, Z., & Tang, Q. (2022) · Frontiers in Public Health · doi:10.3389/fpubh.2021.835960 · Cited by 32
Enriching Neural Models with Targeted Features for Dementia Detection
Palo, F. D., & Parde, N. (2019) · Annual Meeting of the Association for Computational Linguistics: Student Research Workshop (ACL SRW) · doi:10.18653/v1/p19-2042 · Cited by 32
Language-related features for early detection of Alzheimer Disease
Ammar, R. B., & BenAyed, Y. (2020) · Procedia Computer Science · doi:10.1016/j.procs.2020.09.071 · Cited by 32
Identifying and Avoiding Confusion in Dialogue with People with Alzheimer's Disease
Chinaei, H., Currie, L. C., Danks, A., Lin, H., Mehta, T., & Rudzicz, F. (2017) · Computational Linguistics · doi:10.1162/coli_a_00290 · Cited by 30
Priyadarshinee, P., Clarke, C. J., Melechovský, J., Lin, C. M. Y., Balamurali, B. T., & Chen, J. (2023) · Applied Sciences · doi:10.3390/app13074244 · Cited by 33
Artificial Intelligence Techniques for the effective diagnosis of Alzheimer’s Disease: A Review
Shastry, K. A., & Sanjay, H. A. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-16928-z · Cited by 32
Applications of artificial intelligence in dementia research
Tsoi, K. K. F., Jia, P., Dowling, N. M., Titiner, J. R., Wagner, M., Capuano, A. W., & Donohue, M. (2022) · Cambridge Prisms Precision Medicine · doi:10.1017/pcm.2022.10 · Cited by 33
Federated learning using game strategies: State-of-the-art and future trends
Gupta, R., & Gupta, J. (2023) · Computer Networks · doi:10.1016/j.comnet.2023.109650 · Cited by 30
Wang, C., Liu, S., Li, A., & Liu, J. (2023) · Journal of Medical Internet Research · doi:10.2196/51501 · Cited by 37
Barriers to Treatment: The Unique Challenges for Physicians Providing Dementia Care
Foster, N. L. (2001) · Journal of Geriatric Psychiatry and Neurology · doi:10.1177/089198870101400404 · Cited by 30
Wilkosz, P. A., Kodavali, C. V., Weamer, E. A., Miyahara, S., López, O. L., Nimgaonkar, V. L., DeKosky, S. T., & Sweet, R. A. (2007) · American Journal of Medical Genetics Part B Neuropsychiatric Genetics · doi:10.1002/ajmg.b.30549 · Cited by 30
desRosiers, G., Hodges, J. R., & Berríos, G. E. (1995) · Journal of the American Geriatrics Society · doi:10.1111/j.1532-5415.1995.tb07402.x · Cited by 30
Alkenani, A. H., Li, Y., Xu, Y., & Zhang, Q. (2021) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2021.103803 · Cited by 30
Wang, N., Cao, Y., Hao, S., Shao, Z., & Subbalakshmi, K. P. (2021) · Interspeech (USB) · doi:10.21437/interspeech.2021-2024 · Cited by 29
Advances in Multimodal Behavioral Analytics for Early Dementia Diagnosis: A Review
Guruge, C. J. P., Oviatt, S. L., Haghighi, P. D., & Pritchard, E. (2021) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3462244.3479933 · Cited by 31
Orimaye, S. O., Wong, S.-M. J., & Wong, C. P. (2019) · PLoS ONE · doi:10.1371/journal.pone.0214103 · Cited by 28
On the review of image and video-based depression detection using machine learning
Ashraf, A., Gunawan, T. S., Riza, B. S., Haryanto, E. V., & Janin, Z. (2020) · Indonesian Journal of Electrical Engineering and Computer Science · doi:10.11591/ijeecs.v19.i3.pp1677-1684 · Cited by 33
Hippocampal sclerosis, TDP‐43, and the duration of the symptoms of dementia of AD patients
López, O. L., Kofler, J., Chang, Y., Berman, S., Becker, J. T., Sweet, R. A., Nadkarni, N. K., Patira, R., Kamboh, M. I., Cohen, A. D., et al. (2020) · Annals of Clinical and Translational Neurology · doi:10.1002/acn3.51135 · Cited by 28
Self-citationDetecting Dementia through Retrospective Analysis of Routine Blog Posts by Bloggers with Dementia
Masrani, V., Murray, G., Field, T. S., & Carenini, G. (2017) · doi:10.18653/v1/w17-2329 · Cited by 28
Detecting Dementia from Face-Related Features with Automated Computational Methods
Zheng, C., Bouazizi, M., Ohtsuki, T. O., Kitazawa, M., Horigome, T., & Kishimoto, T. (2023) · Bioengineering · doi:10.3390/bioengineering10070862 · Cited by 29
Wang, H.-L., Tang, R., Ren, R., Dammer, E. B., Guo, Q., Peng, G., Cui, H., Zhang, Y.-M., Wang, J., Xie, X., et al. (2022) · BMC Medicine · doi:10.1186/s12916-022-02584-x · Cited by 28
Multimodal deep learning for dementia classification using text and audio
Lin, K., & Washington, P. Y. (2024) · Scientific Reports · doi:10.1038/s41598-024-64438-1 · Cited by 26
Foster, N. L., Wang, A. Y., Taşdizen, T., Fletcher, P. T., Hoffman, J. M., & Koeppe, R. A. (2007) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2007.10.004 · Cited by 28
Ilias, L., & Askounis, D. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110834 · Cited by 26
Dementing Disorders in the Elderly: Evolution of Disease Severity Over 7 Years
Agüero‐Torres, H., Qiu, C., Winblad, B. G., & Fratiglioni, L. (2002) · Alzheimer Disease & Associated Disorders · doi:10.1097/00002093-200210000-00003 · Cited by 25
An explainable machine learning model of cognitive decline derived from speech
Chandler, C., Diaz‐Asper, C., Turner, R. S., Reynolds, B., & Elvevåg, B. (2023) · Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring · doi:10.1002/dad2.12516 · Cited by 24
Luz, S. F., Haider, F., Fromm, D., Lazarou, I., Kompatsiaris, I. Y., & MacWhinney, B. (2024) · IEEE Open Journal of Signal Processing · doi:10.1109/ojsp.2024.3378595 · Cited by 26
Deep ensemble learning with transformer models for enhanced Alzheimer’s disease detection
Latif, S., Islam, N. U., Uddin, Z., Cheema, K. M., Ahmed, S. S., & Khan, M. F. (2025) · Scientific Reports · doi:10.1038/s41598-025-08362-y · Cited by 25
Natural Language Analysis to Detect Parkinson’s Disease
Pérez-Toro, P. A., Vásquez-Correa, J. C., Strauss, M., Orozco‐Arroyave, J. R., & Nöth, E. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-27947-9_7 · Cited by 24
A Summarization of the Visual Depression Databases for Depression Detection
Ashraf, A., Gunawan, T. S., Rahman, F. D. A., Kartiwi, M., Ismail, N., & Ulfiah (2020) · International Conference on Wireless and Telematics (ICWT) · doi:10.1109/icwt50448.2020.9243625 · Cited by 25
Soroski, T., Vasco, T. D. C., Newton‐Mason, S. M., Granby, S., Lewis, C., Harisinghani, A., Rizzo, M., Conati, C., Murray, G., Carenini, G., et al. (2022) · JMIR Aging · doi:10.2196/33460 · Cited by 25
Long-term cognitive treatment of Alzheimer's disease: A single case study
Fernández, A. L., Manoiloff, L. M. V., & Monti, A. A. (2006) · Neuropsychological Rehabilitation · doi:10.1080/09602010443000164 · Cited by 25
On the importance of normative data in speech-based assessmentPreprint
Noorian, Z., Pou-Prom, C., & Rudzicz, F. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1712.00069 · Cited by 24
Talk2Me: Automated linguistic data collection for personal assessment
Komeili, M., Pou-Prom, C., Liaqat, D., Fraser, K. C., Yancheva, M., & Rudzicz, F. (2019) · PLOS ONE · doi:10.1371/journal.pone.0212342 · Cited by 23
Language Sample Analysis With TalkBank: An Update and Review
MacWhinney, B., & Fromm, D. (2022) · Frontiers in Communication · doi:10.3389/fcomm.2022.865498 · Cited by 23
Runde, B. S., Alapati, A., & Bazan, N. G. (2024) · Brain Sciences · doi:10.3390/brainsci14030211 · Cited by 24
Psychiatric correlates of MR deep white matter lesions in probable Alzheimer's disease
López, O. L., Becker, J. T., Reynolds, C. F., Jungreis, C. A., Weinman, S., & DeKosky, S. T. (1997) · Journal of Neuropsychiatry · doi:10.1176/jnp.9.2.246 · Cited by 23
Self-citationRelationship between CSF neurotransmitter metabolites and aggressive behavior in Alzheimer's disease
López, O. L., Kaufer, D., Reiter, C. T., Carra, J. P., DeKosky, S. T., & Palmer, A. M. (1996) · European Journal of Neurology · doi:10.1111/j.1468-1331.1996.tb00209.x · Cited by 23
Multimodal Approaches for Alzheimer’s Detection Using Patients’ Speech and Transcript
Cai, H., Huang, X., Liu, Z., Liao, W., Dai, H., Wu, Z., Zhu, D., Ren, H., Li, Q., Liu, T., et al. (2023) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-43075-6_34 · Cited by 22
Pan, Y., Mirheidari, B., Reuber, M., Venneri, A., Blackburn, D. J., & Christensen, H. (2020) · Interspeech (USB) · doi:10.21437/interspeech.2020-2698 · Cited by 23
Guo, Z., Liu, Z., Ling, Z.-H., Wang, S., Jin, L., & Li, Y. (2020) · International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.542 · Cited by 20
Parsapoor, M., Alam, M. R., & Mihailidis, A. (2023) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-023-02122-6 · Cited by 26
Gait Disorder in a Cohort of Patients With Mild and Moderate Alzheimer’s Disease
Castrillo, A. V., Olmos, L. M. G., Rodríguez, F., & Duarte, J. (2015) · American Journal of Alzheimer s Disease & Other Dementias® · doi:10.1177/1533317515603113 · Cited by 22
Cohen, T., & Pakhomov, S. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.176 · Cited by 28
Understanding Impaired Analysis of Faces by Patients with Probable Alzheimer's Disease
Becker, J. T., López, O. L., & Boller, F. (1995) · Cortex · doi:10.1016/s0010-9452(13)80111-6 · Cited by 22
Self-citationDetecting cognitive impairments by agreeing on interpretations of linguistic features
Zhu, Z., Novikova, J., & Rudzicz, F. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1146 · Cited by 28
Dementia Detection by Analyzing Spontaneous Mandarin Speech
Liu, Z., Guo, Z., Ling, Z.-H., Wang, S., Jin, L., & Li, Y. (2019) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.1109/apsipaasc47483.2019.9023041 · Cited by 19
Autopsy as Gold Standard in FDG-PET Studies in Dementia
Durand-Martel, P., Tremblay, D., Brodeur, C., & Paquet, N. (2010) · Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques · doi:10.1017/s0317167100010222 · Cited by 21
Lee, B., Bang, J., Song, H. J., & Kang, B. (2025) · Scientific Reports · doi:10.1038/s41598-024-82597-z · Cited by 22
Learning multiview embeddings for assessing dementia
Pou-Prom, C., & Rudzicz, F. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1304 · Cited by 17
Adhikari, S., Thapa, S., Singh, P., Huo, H., Bharathy, G. K., & Prasad, M. (2021) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9534449 · Cited by 21
Speech Paralinguistic Approach for Detecting Dementia Using Gated Convolutional Neural Network
Mariana, R. M., Makiuchi, M. R., Tifani, W., Warnita, T., 中順, 井., Inoue, N., 浩一, 篠., Shinoda, K., Yoshimura, M., Kitazawa, M., et al. (2021) · Tokyo Tech Research Repository (Tokyo Institute of Technology) · Cited by 20
Pragmatic Aspects of Discourse Production for the Automatic Identification of Alzheimer's Disease
Pompili, A., Abad, A., de Matos, D. M., & Martins, I. P. (2020) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2020.2967879 · Cited by 24
Bayesian Learning for Deep Neural Network Adaptation
Xie, X., Liu, X., Lee, T., & Wang, L. (2021) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2021.3084072 · Cited by 20
Natural history of Alzheimer's disease: prognostic value of plateaux
Piccini, C., Bracco, L., Falcini, M., Pracucci, G., & Amaducci, L. A. (1995) · Journal of the Neurological Sciences · doi:10.1016/0022-510x(95)00107-d · Cited by 20
Sweet, R. A., Kamboh, M. I., Wisniewski, S. R., López, O. L., Klunk, W. E., Kaufer, D., & DeKosky, S. T. (2002) · Journal of Geriatric Psychiatry and Neurology · doi:10.1177/089198870201500106 · Cited by 20
Metadata
- Authors
- Becker, J. T.
- Affiliations
- University of Pittsburgh Medical Center (US)
- Corresponding author
- Becker, James T.
- ORCID
- Becker, James T.
- Venue
- Archives of Neurology
- Publisher
- American Medical Association (AMA)
- Published
- 1994-06-01
- Volume
- 51
- Issue
- 6
- Pages
- 585
- ISSN
- 0003-9942 (ISSN-L)
- OpenAlex
- W2074037951
- Cited by
- 576
- References
- 49
- Citations by year
- 2026: 53 · 2025: 67 · 2024: 63 · 2023: 59 · 2022: 53 · 2021: 61 · 2020: 62 · 2019: 26 · 2018: 15 · 2017: 14 · 2016: 7 · 2015: 7 · 2014: 1 · 2013: 1 · 2012: 2
- FWCI
- 5.79
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Archives of Neurology (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (5p01ag003705-04)
- National Institute on Aging (AG 05-133, P50AG005133, AG 03705)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Alzheimer's disease research and treatments
- Life Sciences › Neuroscience › Neurology › Neurological Disease Mechanisms and Treatments
- Keywords
- Alzheimer's disease, natural history (disease), diagnosis, neuropathologic diagnosis, diagnostic accuracy, dementia, neuropsychological assessment, survival, death, vascular dementia, depression, cohort studies
- MeSH terms
- Aged, Alzheimer Disease, Mental Disorders, Female, Humans, Male, Middle Aged, Nervous System, Nervous System Diseases, Neuropsychological Tests, Cohort Studies
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