Using the Alzheimer's Disease Neuroimaging Initiative to improve early detection, diagnosis, and treatment of Alzheimer's disease

Veitch, Dallas P.; Weiner, Michael W.; Aisen, Paul S.; Beckett, Laurel A.; DeCarli, Charles; Green, Robert C.; Harvey, Danielle; Jack, Clifford R.; Landau, Susan; Morris, John C.; Okonkwo, Ozioma C.; Perrin, Richard J.; Petersen, Ronald C.; Rivera‐Mindt, Monica; Saykin, Andrew J.; Shaw, Leslie M.; Toga, Arthur W.; Tosun, Duygu; Trojanowski, John Q.; Initiative, Alzheimer’s Disease Neuroimaging

doi:10.1002/alz.12422

Cited by 77 publications

(91 citation statements)

References 214 publications

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“…The Alzheimer's Disease Neuroimaging Initiative (ADNI) is a large, multicenter, longitudinal neuroimaging study, launched in 2003. 21 , 28 To date, ADNI have recruited over 3000 adults (ages 55 to 90), and our study included cognitively normal older individuals (CN) and patients with mild cognitive impairment (MCI). Participants provided detailed medical history and physical examination and offered biological samples, such as a cerebrospinal fluid (CSF) sample.…”

Section: Methodsmentioning

confidence: 99%

Hearing impairment is associated with cognitive decline, brain atrophy and tau pathology

et al. 2022

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Section: Methodsmentioning

confidence: 99%

Hearing impairment is associated with cognitive decline, brain atrophy and tau pathology

et al. 2022

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“…The relatively low accuracy in this study may be due to the small size of the J-ADNI dataset compared to the much larger ADNI dataset. However, although the ADNI is a very useful public database that includes the data of about 1,700 subjects and has been used as a dataset in more than 3,500 publications since 2004, about 80% of the participants were Whites whereas only 2.7% of them were Asians ( 36 ). Therefore, to achieve partial generalizability of our findings for the Asian subjects, we chose the J-ADNI dataset, even though it is much smaller than the ADNI dataset.…”

Section: Discussionmentioning

confidence: 99%

Predicting progression to dementia with “comprehensive visual rating scale” and machine learning algorithms

Park

Jang

Joo³

et al. 2022

Front. Neurol.

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Background and ObjectiveIdentifying biomarkers for predicting progression to dementia in patients with mild cognitive impairment (MCI) is crucial. To this end, the comprehensive visual rating scale (CVRS), which is based on magnetic resonance imaging (MRI), was developed for the assessment of structural changes in the brains of patients with MCI. This study aimed to investigate the use of the CVRS score for predicting dementia in patients with MCI over a 2-year follow-up period using various machine learning (ML) algorithms.MethodsWe included 197 patients with MCI who were followed up more than once. The data used for this study were obtained from the Japanese-Alzheimer's Disease Neuroimaging Initiative study. We assessed all the patients using their CVRS scores, cortical thickness data, and clinical data to determine their progression to dementia during a follow-up period of over 2 years. ML algorithms, such as logistic regression, random forest (RF), XGBoost, and LightGBM, were applied to the combination of the dataset. Further, feature importance that contributed to the progression from MCI to dementia was analyzed to confirm the risk predictors among the various variables evaluated.ResultsOf the 197 patients, 108 (54.8%) showed progression from MCI to dementia. Tree-based classifiers, such as XGBoost, LightGBM, and RF, achieved relatively high performance. In addition, the prediction models showed better performance when clinical data and CVRS score (accuracy 0.701–0.711) were used than when clinical data and cortical thickness (accuracy 0.650–0.685) were used. The features related to CVRS helped predict progression to dementia using the tree-based models compared to logistic regression.ConclusionsTree-based ML algorithms can predict progression from MCI to dementia using baseline CVRS scores combined with clinical data.

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“…Nowadays, the diagnosis of AD is based on clinical, cognitive, and functional criteria using brief cognitive tests (e.g., Mini-Mental Status Examination (MMSE) and clinical dementia rating (CDR)), neuroimaging techniques (e.g., brain scanning by computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET)), and biomarker analysis in cerebrospinal fluid (CSF) [ 10 ]. Likewise, the National Institute of Aging and the Alzheimer’s Association established complete clinical and cognitive guidelines for the diagnosis of mild cognitive impairment (MCI) or dementia associated with AD, allowing for the classification of individuals with probable AD dementia, possible AD dementia, and probable or possible AD dementia [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

Alzheimer’s Disease Seen through the Eye: Ocular Alterations and Neurodegeneration

Romaus-Sanjurjo

Regueiro

López-López

et al. 2022

IJMS

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Alzheimer’s Disease (AD) is one of the main neurodegenerative diseases worldwide. Unfortunately, AD shares many similarities with other dementias at early stages, which impedes an accurate premortem diagnosis. Therefore, it is urgent to find biomarkers to allow for early diagnosis of the disease. There is increasing scientific evidence highlighting the similarities between the eye and other structures of the CNS, suggesting that knowledge acquired in eye research could be useful for research and diagnosis of AD. For example, the retina and optic nerve are considered part of the central nervous system, and their damage can result in retrograde and anterograde axon degeneration, as well as abnormal protein aggregation. In the anterior eye segment, the aqueous humor and tear film may be comparable to the cerebrospinal fluid. Both fluids are enriched with molecules that can be potential neurodegenerative biomarkers. Indeed, the pathophysiology of AD, characterized by cerebral deposits of amyloid-beta (Aβ) and tau protein, is also present in the eyes of AD patients, besides numerous structural and functional changes observed in the structure of the eyes. Therefore, all this evidence suggests that ocular changes have the potential to be used as either predictive values for AD assessment or as diagnostic tools.

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Using the Alzheimer's Disease Neuroimaging Initiative to improve early detection, diagnosis, and treatment of Alzheimer's disease

Cited by 77 publications

References 214 publications

Hearing impairment is associated with cognitive decline, brain atrophy and tau pathology

Hearing impairment is associated with cognitive decline, brain atrophy and tau pathology

Predicting progression to dementia with “comprehensive visual rating scale” and machine learning algorithms

Alzheimer’s Disease Seen through the Eye: Ocular Alterations and Neurodegeneration

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