The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index; none of the 23 sources its claims cite is checked for support yet.
Alzheimer's Disease Neuroimaging Initiative (ADNI)
Open accessAbstract
BACKGROUND: Neuroimaging measures and chemical biomarkers may be important indices of clinical progression in normal aging and mild cognitive impairment (MCI) and need to be evaluated longitudinally. OBJECTIVE: To characterize cross-sectionally and longitudinally clinical measures in normal controls, subjects with MCI, and subjects with mild Alzheimer disease (AD) to enable the assessment of the utility of neuroimaging and chemical biomarker measures. METHODS: A total of 819 subjects (229 cognitively normal, 398 with MCI, and 192 with AD) were enrolled at baseline and followed for 12 months using standard cognitive and functional measures typical of clinical trials. RESULTS: The subjects with MCI were more memory impaired than the cognitively normal subjects but not as impaired as the subjects with AD. Nonmemory cognitive measures were only minimally impaired in the subjects with MCI. The subjects with MCI progressed to dementia in 12 months at a rate of 16.5% per year. Approximately 50% of the subjects with MCI were on antidementia therapies. There was minimal movement on the Alzheimer's Disease Assessment Scale-Cognitive Subscale for the normal control subjects, slight movement for the subjects with MCI of 1.1, and a modest change for the subjects with AD of 4.3. Baseline CSF measures of Abeta-42 separated the 3 groups as expected and successfully predicted the 12-month change in cognitive measures. CONCLUSION: The Alzheimer's Disease Neuroimaging Initiative has successfully recruited cohorts of cognitively normal subjects, subjects with mild cognitive impairment (MCI), and subjects with Alzheimer disease with anticipated baseline characteristics. The 12-month progression rate of MCI was as predicted, and the CSF measures heralded progression of clinical measures over 12 months.
Overview
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Written from the paper’s full text.
TL;DR
This ADNI clinical characterization paper reports baseline and 12-month clinical, cognitive, and CSF biomarker data for 819 participants: 229 cognitively normal, 398 with MCI, and 192 with mild AD. It shows expected group differences at baseline, a 16.5% 12-month MCI-to-AD progression rate, and baseline CSF Aβ-42 predicting 12-month cognitive change in normal and MCI groups.
Key findings
- At baseline, ADAS-Cog total scores were 6.2 ± 2.9 in controls, 11.5 ± 4.4 in MCI, and 18.6 ± 6.3 in AD (p < 0.001); MCI subjects were most impaired on memory items and only mildly impaired in nonmemory domains.
- Over 12 months, 64 of 398 MCI subjects progressed to AD, a rate of 16.5% (95% CI 11.9%–20.5%); normal controls progressed to MCI at an estimated 1.4% (95% CI 0%–3.2%).
- ADAS-Cog 12-month change was −0.5 ± 3.0 in controls, 1.1 ± 4.4 in MCI, and 4.3 ± 6.6 in AD (p < 0.001).
- Baseline CSF Aβ-42 decreased across groups (controls 206 ± 5, MCI 164 ± 4, AD 143 ± 4 pg/mL; p < 0.001), and higher baseline Aβ-42 was associated with better ADAS-Cog performance and smaller 12-month change in normal subjects and MCI.
Methods
- Multicenter ADNI cohort of 819 subjects aged 55–90 enrolled at baseline and classified as cognitively normal, MCI, or mild AD using standard clinical, cognitive, and functional criteria; subjects were followed for 12 months with cognitive and functional measures, MRI/PET in subsets, and lumbar puncture in a minimum of 20% per site.
- Baseline group comparisons used nonparametric tests (Kruskal-Wallis) with Hochberg multiple comparisons and exact tests for proportions; 12-month change was summarized as conversion proportion, baseline-to-12-month mean or percent difference, and rate per year from mixed-effects regression models.
- Annual conversion rates were estimated using a hybrid interval-censored method; CSF Aβ-42 was related to diagnosis, ADAS-Cog performance, and 12-month ADAS-Cog change.
Limitations
- The paper states that a 12-month change period is not sufficient to draw conclusions about the likelihood of clinical change, and that the ADNI cohort likely does not represent “typical” community subjects because participants were more highly educated, mostly white, and had a high proportion of APOE4 carriers in the MCI and AD groups.
- The paper also states that the MCI criteria required a stringent degree of memory impairment, so these subjects were likely further along the clinical spectrum and more likely to progress than less-impaired MCI subjects.
- Evident from the text: CSF analyses were limited to subsets (baseline n = 114 controls, 199 MCI, 102 AD), and some subjects lacked 12-month follow-up diagnoses (17 controls and 41 MCI subjects), which limits interpretation of conversion and biomarker results.
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Claims the paper makes
53 Own result · 8 Sources not found · 8 Own interpretation · 5 No basis given · 3 Not checked yet · 3 Own method
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“successfully predicted the 12-month change in cognitive measures.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Abstract
plaincorrelationaln = 819- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“but they looked more like the AD group for change in digit span, category fluency, and trail making”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Concomitant medications.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Nonmemory cognitive measures were only minimally impaired in the subjects with MCI.”
- Also stated in: § RESULTS Baseline characteristics. · hedged
“The neuropsychological battery indicated that, in general, subjects with MCI were more impaired than controls on the memory items and were only mildly impaired in nonmemory domains (table 3).”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Abstract
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Also stated in: § RESULTS Baseline characteristics. · hedged
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The sub-jects with AD were impaired in virtually all of the cognitive areas tested.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > RESULTS Baseline characteristics.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These scores indicated, however, that the subjects with MCI gained most of their error points on memory items.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > RESULTS Baseline characteristics.
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Baseline CSF measures of A-42 separated the 3 groups as expected”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Abstract
plaincorrelationaln = 819- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The next greatest area of impairment for the subjects with MCI resulted from the executive function domain.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > RESULTS Baseline characteristics.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The subjects with MCI progressed to dementia in 12 months at a rate of 16.5% per year.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Abstract
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The subjects with MCI also remained relatively stable as a group, and the subjects with mild AD showed a decline on most of these measures.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > DISCUSSION
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The interaction of A-42 and time also had a significant effect on ADAS-Cog scores in subjects with MCI ( p ϭ 0.00), but not in normal subjects.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Concomitant medications.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- It may state two separate claims as one. 20
- Probably not a substantial claim. 14
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The CDR sum of the boxes essentially did not change in the normal control group (0.1) and increased by approximately 0.7 points in the subjects with MCI and 1.5 points in the subjects with AD ( p Ͻ 0.001).”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Concomitant medications.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The subjects with MCI were more memory impaired than the cognitively normal subjects but not as impaired as the subjects with AD.”
- Also stated in: § RESULTS Baseline characteristics. · hedged
“These scores indicated, however, that the subjects with MCI gained most of their error points on memory items.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Abstract
plainn = 819- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Also stated in: § RESULTS Baseline characteristics. · hedged
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In general, the normal group stayed nearly the same, and the AD group worsened significantly compared to the normal controls.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Concomitant medications.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“and more like the normal controls for the clock test and global CDR.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Concomitant medications.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Overall, subjects with MCI had a mean total Alzheimer's Disease Assessment Scale-Cognitive subscale (ADAS-Cog) score of 11.5, while the normal control subjects had a score of 6.2 and the subjects with mild AD had a score of 18.7 ( p Ͻ 0.001, table 3).”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > RESULTS Baseline characteristics.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“cholinesterase inhibitors-normal controls 0%, subjects with MCI 43.7%, subjects with AD 84.9%”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Concomitant medications.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“A total of 819 subjects were recruited and received a baseline evaluation as part of the ADNI study.”
- Also stated in: § RESULTS Baseline characteristics. · plain
“There were 229 normal control subjects, 398 subjects with MCI, and 192 subjects with mild AD enrolled.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > RESULTS Baseline characteristics.
plainn = 819- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Also stated in: § RESULTS Baseline characteristics. · plain
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The estimated premorbid verbal IQ of these subjects was quite high, at almost 120 for the normal control subjects, 116 for the subjects with MCI, and 114 for the subjects with AD.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > RESULTS Baseline characteristics.
hedgedn = 819- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In all cases except the Hachinski Scale and Geriatric Depression Scale, the mean scores for the 3 groups differed significantly at p Ͻ 0.001, with normal controls performing best, subjects with AD worst, and subjects with MCI in the middle.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > RESULTS Baseline characteristics.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The MCI group was more variable; in most cases they worsened more than the normal group but not as much as the AD group”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Concomitant medications.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Approximately 50% of the subjects with MCI were on antidementia therapies.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Abstract
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Most of the subjects in the study were white, and this was approximately equivalent across the groups.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > RESULTS Baseline characteristics.
plainn = 819- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“There was minimal movement on the Alzheimer's Disease Assessment Scale-Cognitive Subscale for the normal control subjects, slight movement for the subjects with MCI of 1.1, and a modest change for the subjects with AD of 4.3.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Abstract
plainn = 819- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As shown in table 2, the mean age of the 3 groups was equivalent at approximately 75 years.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > RESULTS Baseline characteristics.
plainn = 819- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“there were more men than women in the MCI group (64.6% men vs 35.4% women).”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > RESULTS Baseline characteristics.
plainn = 398- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“There was somewhat less variability on the global measures, while some of the individual neuropsychological tests such as the AVLT showed considerable variability among the subjects despite relatively modest mean group changes.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > DISCUSSION
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“CSF A-42 levels were assessed at baseline and were correlated with clinical diagnoses, performance on the ADAS-Cog, and changes in performance on the ADAS-Cog over 1 year (table 3).”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Concomitant medications.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Delayed recall improved by 5.3% in the normal control group and declined by 4.8% in the subjects with MCI and 7.2% in the subjects with AD.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Concomitant medications.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“As such, the baseline characteristics of the subjects indicated that the 3 groups were generally well educated, intelligent, and mostly white, and perform in a fashion similar to subjects recruited for typical clinical trials.[25][26][27][28]”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > DISCUSSION
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The CSF A-42 data correlated with the diagnostic groups in the expected fashion with levels being lower in the subjects with MCI than in the normal subjects.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > DISCUSSION
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: CSF Aβ-42 level is a valid biomarker of Alzheimer's disease pathology and progression.
None of the sources cited for this claim have been checked yet.
In the paper
“They tend to be more highly educated and the proportion of APOE4 carriers was quite high in the MCI and AD groups but is consistent with subjects recruited for clinical trials.25”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > DISCUSSION
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“On the memory measures such as the AVLT, there was virtually no change across 12 months in learning in the normal control group with a decreased learning of 1.3 items in the subjects with MCI and 3.7 fewer items learned in the AD group.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Concomitant medications.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“An additional 8 subjects with MCI reverted to a normal classification over that interval, and 41 did not have a follow-up diagnosis.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Concomitant medications.
plainn = 398- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“may allow detection of the neurodegenerative process at an earlier point in the spectrum”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Abstract
What the sources say
- Not matchedcites [6]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [7]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [8]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
hedgedconditional- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Not matched
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“All hypothesis tests were 2-sided at level 0.05, and models were validated both graphically and analytically.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Data analysis.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Most investigators believe that Alzheimer disease (AD) is a slowly evolving process that likely begins years to decades before the clinical symptoms are manifest.1”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Abstract
What the sources say
- Not matchedcites [1]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
- Not matched
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“A 12month change period is not a sufficient amount of time to draw conclusions regarding the likelihood of clinical change.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > DISCUSSION
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“has been a target for clinical trials.[2][3][4][5]”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Abstract
What the sources say
- Not matchedcites [2]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [3]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [4]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [5]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The diagnostic conversions over the 12-month period must be viewed with some caution.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > DISCUSSION
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This might be a useful technique to reduce the numbers of subjects used in clinical trials to detect a clinical effect of therapeutic interventions.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > DISCUSSION
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
- Rests on an assumption: CSF Aβ-42 level is a valid biomarker of Alzheimer's disease pathology and progression.
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“Mild cognitive impairment (MCI) represents an attempt to characterize subjects at an early clinical phase”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Abstract
What the sources say
- Not matchedcites [2]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [3]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [4]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [5]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
- Not matched
The paper relies on this without support.
The expected 12-month progression rate for MCI was approximately 16.5% per year.
“The 12-month progression rate of MCI was as predicted”
“The subjects with MCI progressed to dementia in 12 months at a rate of 16.5% per year.”
The paper relies on this without support.
The baseline characteristics observed in the recruited cohorts matched the characteristics anticipated for normal, MCI, and AD cohorts.
“The Alzheimer's DiseaseNeuroimaging Initiative has successfully recruited cohorts of cognitively normal subjects, subjects with mild cognitive impairment (MCI), and subjects with Alzheimer disease with anticipated baseline characteristics.”
The paper relies on this without support.
The MCI subjects have an underlying Alzheimer's disease process that drives clinical progression.
“As such, these subjects were likely further down the clinical spectrum with respect to the underlying AD process such that they would be more likely to progress than subjects with less impairment in the MCI spectrum.”
The paper relies on this without support.
CSF Aβ-42 level is a valid biomarker of Alzheimer's disease pathology and progression.
“The CSF A-42 data correlated with the diagnostic groups in the expected fashion with levels being lower in the subjects with MCI than in the normal subjects.”
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?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Another issue is that the vast majority of data from PET and CSF are from selected participants recruited through tertiary care dementia centers [176].”
From NIA‐AA Research Framework: Toward a biological definition of Alzheimer's disease (2018) · cites this paper as [176]
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated major: it would touch a result — a value a result rests on, reported and recomputed p-values on opposite sides of the significance level, two passages that conflict with no sign which is right, or a cited work retracted before this paper was published.
In the paper
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Concomitant medications.
“Among the 398 subjects with MCI at baseline, 64 progressed to AD at 12 months”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Concomitant medications.
“Table 5 Baseline measures on subjects according to progression status at 1 year Assortment variable Nonprogressors, mean ؎ SD (n ؍ 329) Progressors, mean ؎ SD (n ؍ 69)”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > RESULTS Baseline characteristics.
“the subjects with mild AD had a score of 18.7”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > RESULTS Baseline characteristics.
“ADAS-Cog total 6.2 Ϯ 2.9 11.5 Ϯ 4.4 18.1 18.6 Ϯ 6.3 14.0 Ͻ0.001 a, b, c”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Concomitant medications.
“subjects with MCI (r ϭ Ϫ0.22; 95% CI Ϫ0.035 to Ϫ0.08)”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Concomitant medications.
“95% CI Ϫ0.035 to Ϫ0.08”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > RESULTS Baseline characteristics.
“In all cases except the Hachinski Scale and Geriatric Depression Scale, the mean scores for the 3 groups differed significantly at p Ͻ 0.001”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > RESULTS Baseline characteristics.
“GDS Score 0.8 Ϯ 1.1 1.6 Ϯ 1.4 7.3 1.7 Ϯ 1.4 0.6 Ͻ0.001 a, b”
Citations
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VALIDITY OF THE TRAIL MAKING TEST AS AN INDICATOR OF ORGANIC BRAIN DAMAGE
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Self-citationThe Neuropsychiatric Inventory: Comprehensive assessment of psychopathology in dementia
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Cited by
Sorted by citations
Items 1,401–1,500 of 2,570
Ali, D. G., Abner, E. L., Bahrani, A. A., El Khouli, R., Gold, B. T., Jiang, Y., Wilcock, D. M., & Jicha, G. A. (2023) · Brain Sciences · doi:10.3390/brainsci13020218 · Cited by 6
“Details of ADNI inclusion criteria, clinical procedures and methodology are available elsewhere [28,29].”
MethodMRI-free processing of tau PET images for early detection
Carlson, M. L., Smith, V., Johns, E., Young, C. B., Vossler, H. A., Ward, T. J., Harrison, T. M., Tosun, D., Hohman, T. J., Landau, S. M., et al. (2024) · Imaging Neuroscience · doi:10.1162/imag_a_00369 · Cited by 6
Using space-filling curves and fractals to reveal spatial and temporal patterns in neuroimaging data
Grela, J., Drogosz, Z., Janarek, J., et al. (2025) · Journal of Neural Engineering · doi:10.1088/1741-2552/ada705 · Cited by 6
“To this end, we draw upon datasets from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) [42] and the Open Access Series of Imaging Studies (OASIS) [43].”
Sönmez, T. F., Harvey, D. J., Beckett, L. A., & Initiative, A. D. N. (2025) · Alzheimer s & Dementia · doi:10.1002/alz.14524 · Cited by 6
Self-citationPark, J., Lee, S., Huber, B. D., Devanand, D. P., Kim, H., & Goldberg, T. E. (2025) · Biological Psychiatry · doi:10.1016/j.biopsych.2025.01.026 · Cited by 6
Xu, X., Wang, J., Chen, T., Wang, S., Wang, F., He, J., Meng, X., & Shen, Y. (2025) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2025.1536142 · Cited by 7
Coomans, E. M., van Tol, B. G. J., Groot, C., Smith, R. A., Palmqvist, S., Stomrud, E., Pontecorvo, M. J., Shcherbinin, S., Kennedy, I. A., Kotari, V., et al. (2025) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-025-07384-y · Cited by 6
HoloDx: Knowledge- and Data-Driven Multimodal Diagnosis of Alzheimer’s Disease
Chen, Q., Wang, J., Wang, G., & Hong, Y. (2025) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2025.3594364 · Cited by 6
Jafrasteh, B., Adeli, E., Pohl, K. M., Kuceyeski, A. F., Sabuncu, M. R., & Zhao, Q. (2025) · Scientific Reports · doi:10.1038/s41598-025-12026-2 · Cited by 9
Han, J. W., Oh, D. J., Kim, T. H., Kwak, K. P., Kim, B., Kim, S. G., Kim, J. L., Moon, S. W., Park, J. H., Ryu, S., et al. (2025) · Journal of Korean Medical Science · doi:10.3346/jkms.2025.40.e326 · Cited by 6
Maccioni, L., Giacomel, A., Pinamonti, M., Francisci, M. D., Pup, F. D., Giubergia, A., Vallini, G., Visani, V., Barzon, L., Dipasquale, O., et al. (2025) · NeuroImage · doi:10.1016/j.neuroimage.2025.121637 · Cited by 9
Association of plasma p-tau and p-tau/Aβ ratio with Alzheimer's pathology
Chen, X., Jiang, M., Zhang, L., Zhu, J., Li, A., He, Z., Zhou, X., Zhu, Y., Zhang, C., Wang, C., et al. (2026) · The Journal of Prevention of Alzheimer s Disease · doi:10.1016/j.tjpad.2025.100472 · Cited by 6
“Participants were classified as cognitively unimpaired (CU), mild cognitive impairment (MCI), or dementia following the standard protocol of the Alzheimer ’ s Disease Neuroimaging Initiative (ADNI) [25].”
Li, F., Bai, S., Liu, Y., Chen, Z., Zhao, S., Ding, Z., Xie, F., Xu, Y., Yue, L., Zhang, H., et al. (2026) · Cell Reports Medicine · doi:10.1016/j.xcrm.2026.102629 · Cited by 8
Kaczmarek, E., Szeto, J., Nichyporuk, B., & Arbel, T. (2025) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw69036.2025.00141 · Cited by 7
Macedo, A. C., Trudel, L., Therriault, J., Rahmouni, N., Aumont, É., Bezgin, G., Hosseini, S. A., Tissot, C., Chan, T., Servaes, S., et al. (2026) · Nature Aging · doi:10.1038/s43587-026-01143-w · Cited by 10
Nawaz, A., Rehman, A. U., Ali, T. M., Hayat, Z., Rahim, A., Zaman, U. K. U., & Ali, A. R. (2021) · Applied Artificial Intelligence · doi:10.1080/08839514.2021.1982185 · Cited by 12
“…(Rajpurkar et al. 2017) for pneumonia detection, Autism Brain Imaging Data Exchange (ABIDE) (Martino et al. 2014) for the detection of autism spectrum disorder (ASD), Alzheimer’s Disease Neuroimaging Initiative (ADNI) (Petersen et al. 2010) for the detection of Alzheimer disease and many more.”
BackgroundSleep-Disordered Breathing Risk with Comorbid Insomnia Is Associated with Mild Cognitive Impairment
Agudelo, C., Ramos, A. R., Sun, X., Kaur, S., Papa, D. D., Kather, J., Wallace, D. M., & (ADNI), O. B. O. T. A. D. N. I. (2022) · Applied Sciences · doi:10.3390/app12052414 · Cited by 6
Quek, Y., Bourgeat, P., Fung, Y. L., Vogrin, S. J., Collins, S. J., & Bowden, S. C. (2023) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2023.111707 · Cited by 6
“The ADNI diagnostic criteria are described in Petersen et al. (2010).”
Inverse Consistency by Construction for Multistep Deep Registration
Greer, H., Tian, L., Vialard, F., et al. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43999-5_65 · Cited by 6
Kim, R. T., Zhou, L., Li, Y., Krieger, A. C., Nordvig, A. S., Butler, T., de Leon, M. J., & Chiang, G. C.-Y. (2024) · Journal of the Neurological Sciences · doi:10.1016/j.jns.2024.122927 · Cited by 6
“…of ADNI subjects were white, highly educated, and healthier than typical OSA or sleep-impaired individuals in the community, since those with significant comorbid cardiovascular conditions were screened out, [74] so the slower rates of A β and tau in the NC group may reflect better access to care.”
Ex-Vivo Hippocampus Segmentation Using Diffusion-Weighted MRI
Tang, H., Dai, S., Zou, E. M., Liu, G., Ahearn, R., Krafty, R., Modo, M., & Zhan, L. (2024) · Mathematics · doi:10.3390/math12070940 · Cited by 7
“Diagnosis and Disease Monitoring: Accurate segmentation aids in diagnosing and monitoring neurodegenerative diseases like Alzheimer’s [10,11], epilepsy, and dementia.”
BackgroundEnhancing Continuous Domain Adaptation with Multi-path Transfer Curriculum
Liu, H., Wang, J., Zhang, X., et al. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-2253-2_23 · Cited by 6
Almatrafi, S., Abbas, Q., & Ibrahim, M. E. A. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-19104-z · Cited by 6
Reed, A. M., Duff, K., Dibble, L. E., Paul, S. S., Hooyman, A., & Schaefer, S. Y. (2024) · The Journal of Prevention of Alzheimer s Disease · doi:10.14283/jpad.2024.93 · Cited by 8
Clinical significance of plasma candidate biomarkers of Alzheimer’s Disease
Lewczuk, P., Łukaszewicz‐Zając, M., Kornhuber, J., & Mroczko, B. (2024) · Neurologia i Neurochirurgia Polska · doi:10.5603/pjnns.100675 · Cited by 7
Alzheimer's Disease Classification using Capsule Network
Pattanayak, B. K., Mansingh, P., Pati, B., Dash, B. B., Gourisaria, M. K., & Patra, S. S. (2024) · International Conference on Expert Clouds and Applications (ICOECA) · doi:10.1109/icoeca62351.2024.00117 · Cited by 6
Novel plasma protein biomarkers: A time-dependent predictive model for Alzheimer's disease
Zhuang, T., Yang, Y., Ren, H., Zhang, H., Gao, C. Q., Chen, S., Shen, J., Ji, M., & Cui, Y. (2024) · Archives of Gerontology and Geriatrics · doi:10.1016/j.archger.2024.105650 · Cited by 7
“ADNI aims to provide a systematic data platform for assessing and measuring the early transformation of AD through the integration of imaging, clinical neuropsychology, and fluid biomarkers (Petersen et al., 2010).”
Conversion-aware forecasting of Alzheimer’s disease via featurewise attention
Karasu, E., & Baytaş, İ. M. (2025) · Pattern Analysis and Applications · doi:10.1007/s10044-025-01447-4 · Cited by 6
Cumplido‐Mayoral, I., Sánchez‐Benavides, G., Vilor‐Tejedor, N., López‐Martos, D., Brugulat‐Serrat, A., Milà‐Alomà, M., Falcón, C., Cacciaglia, R., Minguillón, C., Fauria, K., et al. (2025) · Molecular Psychiatry · doi:10.1038/s41380-025-02961-x · Cited by 9
Prediction of Alzheimer’s Disease Based on Multi-Modal Domain Adaptation
Fu, B., Shen, C., Liao, S., Wu, F., & Liao, B. (2025) · Brain Sciences · doi:10.3390/brainsci15060618 · Cited by 8
“This study utilized a publicly available brain dataset, namely the Alzheimer’s Disease Neuroimaging Initiative (ADNI) [26] (https://ida.loni.usc.edu, accessed on 9 July 2024).”
Yim, S., Park, S., Lim, K., Kang, H., Shin, D., Jo, H., Jang, H., Weiner, M. W., Zetterberg, H., Blennow, K., et al. (2025) · Neurology · doi:10.1212/wnl.0000000000213954 · Cited by 6
Self-citationCerebrovascular and microglial states are not altered by functional neuroinflammatory gene variant
Felsky, D., Jager, P. L. D., Schneider, J. A., Arfanakis, K., Fleischman, D. A., Arvanitakis, Z., Honer, W. G., Pouget, J. G., Mizrahi, R., Pollock, B. G., et al. (2016) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1177/0271678x15626719 · Cited by 6
Seo, E. H., Initiative, A. D. N., Kim, S. H., Park, S. H., Kang, S., & Choo, I. H. (2016) · Journal of Alzheimer s Disease · doi:10.3233/jad-160395 · Cited by 6
An Algorithm to Characterize a Dementia Population by Disease Subtype
Albrecht, J. S., Hanna, M., Randall, R. L., Kim, D., & Perfetto, E. M. (2019) · Alzheimer Disease & Associated Disorders · doi:10.1097/wad.0000000000000295 · Cited by 6
Diagnosis of Alzheimer’s Disease using Structural MRI and Convolution Neural Network
Bian, S. (2020) · E3S Web of Conferences · doi:10.1051/e3sconf/202018503037 · Cited by 6
Progression to dementia in patients with essential tremor
Louis, E. D., Radler, K. H., Huey, E. D., et al. (2020) · Parkinsonism & Related Disorders · doi:10.1016/j.parkreldis.2020.12.011 · Cited by 6
A Bayesian linear mixed model for prediction of complex traits
Hai, Y., & Wen, Y. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa1023 · Cited by 6
Sato, K., Mano, T., Ihara, R., Suzuki, K., Niimi, Y., Toda, T., Iwatsubo, T., & Iwata, A. (2021) · The Journal of Prevention of Alzheimer s Disease · doi:10.14283/jpad.2021.39 · Cited by 6
Deep Learning on SDF for Classifying Brain Biomarkers
Yang, Z., Wu, J., Thompson, P. M., et al. (2021) · Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society · doi:10.1109/embc46164.2021.9630850 · Cited by 6
Mansfield, C. A., Bullok, K. E., Fuhs, J. V., Tockhorn‐Heidenreich, A., Andrews, J. S., DiBenedetti, D. B., Matthews, B. R., Darling, J. C., Sutphin, J., & Hauber, B. (2022) · Patient · doi:10.1007/s40271-022-00576-w · Cited by 6
Shan, Q., Yu, X., & Tian, Y. (2022) · The Journals of Gerontology Series A · doi:10.1093/gerona/glac137 · Cited by 6
Ross, D. E., Seabaugh, J. D., Seabaugh, J. M., Alvarez, C., Ellis, L. P., Powell, C., Reese, C., Cooper, L., Shepherd, K. M., & Initiative, F. T. A. D. N. (2022) · Concussion · doi:10.2217/cnc-2022-0003 · Cited by 6
Suwalska, A., Siuda, J. S., Kocot, S., Żmuda, W., Rudzińska, M., & Polańska, J. (2023) · Signal Image and Video Processing · doi:10.1007/s11760-023-02643-7 · Cited by 6
Ge, C., Xu, J., Hu, J., Liu, P., Tang, R., Wang, J., & Ren, H. (2023) · International Conference on Cloud Computing and Big Data Analytics (ICCCBDA) · doi:10.1109/icccbda56900.2023.10154636 · Cited by 6
A linear primal–dual multi-instance SVM for big data classifications
Brand, L., Seo, H., Baker, L. Z., Ellefsen, C., Sargent, J., & Wang, H. (2023) · Knowledge and Information Systems · doi:10.1007/s10115-023-01961-z · Cited by 6
Developing Explainable Deep Model for Discovering Novel Control Mechanism of Neuro-Dynamics
Dan, T., Kim, M., Kim, W. H., & Guorong Wu, A. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3309821 · Cited by 6
“From the ADNI dataset [54], we selected 126 subjects who have diffusion-weighted imaging (DWI) scans and longitudinal A β and tau PET scans (consisting of 2-5 time points) to ensure the availability of tau, A β , and structural connectomes for each subject.”
MethodAssessment for Alzheimer’s Disease Advancement Using Classification Models with Rules
Thabtah, F., & Peebles, D. (2023) · Applied Sciences · doi:10.3390/app132212152 · Cited by 6
Understanding ethnic diversity in open dementia neuroimaging data sets
Heng, N. Y. W., & Rittman, T. (2023) · Brain Communications · doi:10.1093/braincomms/fcad308 · Cited by 6
Affection of Motor Network Regions by Tau Pathology Across the Alzheimer's Disease Spectrum
Bischof, G. N., Jaeger, E., Giehl, K., Hoenig, M. C., Initiative, A. D. N., Weiss, P. H., & Drzezga, A. E. (2023) · eNeuro · doi:10.1523/eneuro.0242-23.2023 · Cited by 5
Degeneration Directory: a multi-omics web resource for degenerative diseases
Yan, H., Lu, C., Lan, C.-Y., Wang, S., Zhang, W.-Q., He, Z., Hu, J.-H., Ai, J., Liu, G.-H., Ma, S., et al. (2023) · Protein & Cell · doi:10.1093/procel/pwad066 · Cited by 4
Study protocol of the Hungarian Longitudinal Study of Healthy Brain Aging (HuBA)
Bankó, É. M., Weiss, B., Hevesi, I., Manga, A., Vakli, P., Havadi-Nagy, M., Kelemen, R., Somogyi, E., Homolya, I., Bihari, A., et al. (2024) · Ideggyógyászati Szemle · doi:10.18071/isz.77.0051 · Cited by 5
Solving the Pervasive Problem of Protocol Non-Compliance in MRI using an Open-Source tool mrQA
Sinha, H., & Raamana, P. R. (2024) · Neuroinformatics · doi:10.1007/s12021-024-09668-4 · Cited by 9
Seibyl, J. P., Zubal, I. G., Jennings, D. L., MAREK, K. L., & Doraiswamy, P. M. (2011) · Expert Review of Neurotherapeutics · doi:10.1586/ern.11.168 · Cited by 6
The Role of Neuropsychology in Prodromal Dementia
Benke, T. (2011) · Neurodegenerative Disease Management · doi:10.2217/nmt.11.34 · Cited by 6
Postmortem imaging and neuropathologic correlations
Augustinack, J. C., & van der Kouwe, A. J. W. (2016) · Handbook of clinical neurology · doi:10.1016/b978-0-444-53486-6.00069-7 · Cited by 7
Harper, A. M., Clayson, L., Wardlaw, J. M., Hernández, M. V., & Initiative, T. A. D. N. (2018) · Journal of International Medical Research · doi:10.1177/0300060518755623 · Cited by 6
Cadena-Hernandez, A. G., Trejo-Castro, A. I., Celaya-Padilla, J. M., Tamez‐Peña, J. G., & Martínez‐Torteya, A. (2018) · IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI ...) · doi:10.1109/bhi.2018.8333404 · Cited by 4
Regional Amyloid Accumulation and White Matter Integrity in Cognitively Normal Individuals
Wang, Y., Hu, H., Yang, Y., Zuo, C., Tan, L., Yu, J., & Initiative, A. D. N. (2020) · Journal of Alzheimer s Disease · doi:10.3233/jad-191350 · Cited by 6
Fowler, M. E., Initiative, A. D. N., Triebel, K. L., Cutter, G., Schneider, L. S. S., & Kennedy, R. (2020) · Journal of Alzheimer s Disease · doi:10.3233/jad-200108 · Cited by 6
“…are ages 55 through 90 years old at various stages of cognitive functioning (normal, MCI, and dementia due to AD), good overall health with no diseases preventing enrollment, and stable doses of permitted medications (including memory-related therapies) for four weeks prior to enrollment [6, 7].”
Bonete-López, B., Oltra‐Cucarella, J., Marín, M., Antón, C., Balao, N., López, E., & Maciá, E. S. (2020) · Archives of Clinical Neuropsychology · doi:10.1093/arclin/acaa117 · Cited by 7
Brief Survey on Machine Learning in Epistasis
Chicco, D., & Faultless, T. (2021) · Methods in molecular biology · doi:10.1007/978-1-0716-0947-7_11 · Cited by 6
Beyond Simple Images: Human Knowledge-Guided GANs for Clinical Data Generation
Dhami, D. S., Das, M., & Natarajan, S. (2021) · Principles of Knowledge Representation and Reasoning · doi:10.24963/kr.2021/24 · Cited by 6
Multivariate patterns of brain-behavior associations across the adult lifespan
Doucet, G. E., Hamlin, N., West, A., Kruse, J. A., Moser, D. A., & Wilson, T. W. (2022) · Aging · doi:10.18632/aging.203815 · Cited by 7
Priorities for research consortia on Alzheimer's disease
Gauthier, S. G., Garcia, A., Sano, M., et al. (2010) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2010.05.2017 · Cited by 6
Constrained Generative Adversarial Network Ensembles for Sharable Synthetic Data GenerationPreprint
Dikici, E., Prevedello, L. M. S., Bigelow, M. T., White, R., & Erdal, B. S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.00086 · Cited by 6
Inflammation in Alzheimer’s disease: do sex and APOE matter?Preprint
Duarte‐Guterman, P., Albert, A., Inkster, A. M., Barha, C. K., Galea, L. A. M., & Initiative, O. B. O. T. A. D. N. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/741777 · Cited by 4
Multi-organ imaging-derived polygenic indexes for brain and body healthPreprint
Yang, X., Sullivan, P. F., Li, B., Fan, Z., Ding, D., Shu, J., Guo, Y., Paschou, P., Bao, J., Shen, L., et al. (2023) · medRxiv · doi:10.1101/2023.04.18.23288769 · Cited by 9
Leonardsen, E. H., Persson, K., Grødem, E., Dinsdale, N. K., Schellhorn, T., Roe, J. M., Dídac Vidal-piñeiro, A., Sørensen, Ø., Kaufmann, T., Westman, E., et al. (2023) · medRxiv · doi:10.1101/2023.06.22.23291592 · Cited by 6
Hsieh, P., Huang, T., Chiou, J., et al. (2024) · Epidemiology and Health · doi:10.4178/epih.e2024057 · Cited by 5
Kim, K. Y., Kim, E., Lee, J., Initiative, A. D. N., Weiner, M. W., Aisen, P., Petersen, R. C., Jack, C. R., Jagust, W., Trojanowki, J. Q., et al. (2024) · Alzheimer s Research & Therapy · doi:10.1186/s13195-024-01564-y · Cited by 5
“Detailed inclusion and exclusion criteria for the study participants have been outlined elsewhere (https:// adni. loni. usc. edu/ metho ds/ docum ents/) [21].”
BackgroundMethodInfluentialSelf-citationDiscovery of plasma biomarkers related to blood-brain barrier dysregulation in Alzheimer’s disease
Dan, Y. R., & Chiam, K. (2024) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2024.1463001 · Cited by 5
Domain Aware Multi-task Pretraining of 3D Swin Transformer for T1-Weighted Brain MRI
Kim, J., Kim, M., & Park, H. (2024) · Lecture notes in computer science · doi:10.1007/978-981-96-0901-7_8 · Cited by 6
Amyloid-β and tau deposition in traumatic brain injury: a study of Vietnam War veterans
de Bruin, H., Groot, C., Kamps, S., Vijverberg, E. G. B., Steward, A., Dehsarvi, A., Pijnenburg, Y. A. L., Ossenkoppele, R., & Franzmeier, N. (2024) · Brain Communications · doi:10.1093/braincomms/fcaf009 · Cited by 5
Uncovering atrophy progression pattern and mechanisms in individuals at risk of Alzheimer's disease
Tremblay, C., Rahayel, S., Pastor‐Bernier, A., St‐Onge, F., Vo, A., Rheault, F., Daneault, V., Morys, F., Rajah, N., Villeneuve, S., et al. (2025) · Brain Communications · doi:10.1093/braincomms/fcaf099 · Cited by 8
Self-citationLao, H., Zhang, X., & Tang, Y. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.127607 · Cited by 5
Evaluation of amyloid PET positivity using machine learning on 18F-FDG PET images
Yamada, T., Kimura, Y., Watanabe, S., et al. (2025) · Japanese Journal of Radiology · doi:10.1007/s11604-025-01789-3 · Cited by 5
“However, such validation is feasible using open datasets like Alzheimer's Disease Neuroimaging Initiative (ADNI) [16].”
Wen, Z., & Biros, G. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103757 · Cited by 5
Lozupone, G., Bria, A., Fontanella, F., Meijer, F. J. A., Stefano, C. D., & Huisman, H. J. (2026) · Medical Image Analysis · doi:10.1016/j.media.2026.103932 · Cited by 8
Condado, J. G., Recuero, I. V., Elorriaga, I. T., Birkenbihl, C., Carrigan, M., Diez, I. P., Buckley, R. F., Erramuzpe, A., Cortés, J. M., & Initiative, T. A. D. N. (2025) · NeuroImage · doi:10.1016/j.neuroimage.2025.121548 · Cited by 7
White Matter Hyperintensities Moderate the Association Between Diabetes and Non-AD Brain Atrophy
Cui, J., Liu, Z., Tan, W. Y., Chong, J. R., On, S. H., Chong, E. J. Y., Venketasubramanian, N., Lai, M. K. P., Chen, C., Hilal, S., et al. (2025) · Diabetes Care · doi:10.2337/dc25-1162 · Cited by 5
Nasiri, H., Khosravi, F., Saberian, P., Kavian, M., Mozafar, M., Torabi, P., Malekzadeh, T., Hadavi, S. M., Rezaee, A., Ghorbanalinejad, M., et al. (2025) · Scientific Reports · doi:10.1038/s41598-025-25694-x · Cited by 6
Vahid, A., Zamani, J., & Hosseini, S. M. H. (2025) · npj Dementia · doi:10.1038/s44400-025-00041-z · Cited by 5
“In this study, we utilized longitudinal A β PET imaging data from the Alzheimer ’ s Disease Neuroimaging Initiative (ADNI) 22,23 to (a) investigate the probability of conversion from A β -negative to A β -positive status, and (b) to identify regional A β loads that are most important in predicting…”
Systematic review and meta-analysis of AI-driven MRI motion artifact detection and correction
Safari, M., Eidex, Z., Qiu, R. L. J., Goette, M., Wang, T., & Yang, X. (2025) · Physica Medica · doi:10.1016/j.ejmp.2025.105704 · Cited by 8
A Novel Multistage Attention‐Enhanced Mixture of Experts Model for Alzheimer's Disease Diagnosis
Abbas, M. J., Khan, M. A., Dillshad, V., Alzahrani, A. I., Alalwan, N., Alamer, A. A., Nam, Y., & Hussain, A. (2026) · Expert Systems · doi:10.1111/exsy.70223 · Cited by 8
Multiplexed magnetic resonance imaging
Li, Y., Guo, R., Zhao, Y., Jin, W., Ke, Z., Chen, L., Chen, X. J., Tang, W. T., Li, Y., & Liang, Z. (2026) · Nature · doi:10.1038/s41586-026-10475-x · Cited by 5
Perron, J., & Ko, J. H. (2022) · Applied Sciences · doi:10.3390/app122211463 · Cited by 5
Sun, L., Zhang, J., Li, W., Sheng, J., Xiao, S., & Initiative, A. D. N. (2023) · BMC Medicine · doi:10.1186/s12916-023-02910-x · Cited by 5
Lespinasse, J., Dufouil, C., & Proust‐Lima, C. (2023) · BMC Medical Research Methodology · doi:10.1186/s12874-023-02009-0 · Cited by 5
Zhou, J., Wang, Z., Sun, Y., Fu, Y., Li, D., Tan, L., & Initiative, A. D. N. (2023) · Journal of Alzheimer s Disease · doi:10.3233/jad-230513 · Cited by 5
“…capacities for neu-ropsychologic assessment, central nervous system infection, recent head trauma, active substance abuse, medical contraindication for magnetic resonance imaging (MRI), currently being enrolled in other studies, and poor general health with diseases pre-cluding enrollment [21].”
Oldham, C. M., Kukla, B., Walsh, P., & Lee, H. B. (2024) · Journal of Geriatric Psychiatry and Neurology · doi:10.1177/08919887241246226 · Cited by 5
Olsson, H., Millward, J. M., Starke, L., Gladytz, T., Klein, T., Fehr, J., Lai, W. H., Lippert, C., Niendorf, T., & Waiczies, S. (2024) · PLoS ONE · doi:10.1371/journal.pone.0301132 · Cited by 9
“The modified motion simulation described here has been applied to data from the ADNI (Alzheimer’s Disease Neuroimaging Initiative, [34]) to mitigate biases in the distribution of motion artifacts between healthy controls, subjects with mild cognitive impairment, and AD patients.”
Ali, E. H., Sadek, S., Nashef, G. Z. E., et al. (2024) · Algorithms · doi:10.3390/a17050207 · Cited by 5
Classifying Alzheimer’s Disease Neuropathology Using Clinical and MRI Measurements
Zhuang, X., Cordes, D., Bender, A. R., Nandy, R. R., Oh, E. C. T., Kinney, J. W., Caldwell, J., Cummings, J. L., & Miller, J. B. (2024) · Journal of Alzheimer s Disease · doi:10.3233/jad-231321 · Cited by 7
“Moreover,MRIisnon-invasive, generallywell-tolerated,replicable,andwidely accessibleinmanyclinicalsettingsandlarge-scale legacydatabases[14,15].”
TE-SSL: Time and Event-Aware Self Supervised Learning for Alzheimer’s Disease Progression Analysis
Thrasher, J., Devkota, A., Tafti, A. P., et al. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72390-2_31 · Cited by 5
SMART: Self-Weighted Multimodal Fusion for Diagnostics of Neurodegenerative Disorders
Chen, Q., & Hong, Y. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3681140 · Cited by 6
Improved Alzheimer’s Disease Detection with Dynamic Attention Guided Multi-modal Fusion
Basu, A., Raha, S., Gupta, A., & Das, S. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-78195-7_29 · Cited by 5
Fah, H., Bohn, L., Greiner, R., & Dixon, R. A. (2025) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2025.1542514 · Cited by 7
Addressing overfitting bias due to sample overlap in polygenic risk scoring
Jeong, S., Shivakumar, M., Jung, S., Won, H., Nho, K. T., Huang, H., Davatzikos, C. A., Saykin, A. J., Thompson, P. M., Shen, L., et al. (2025) · Alzheimer s & Dementia · doi:10.1002/alz.70109 · Cited by 7
Villalpando, J. M., Leclerc, B., Le, M. T., Hudon, C., Bolduc, A., & Kergoat, M. (2025) · Neuropsychology Review · doi:10.1007/s11065-025-09663-9 · Cited by 6
Adverse childhood experiences influence markers of neurodegeneration risk in older adults
Rose, D., Pfund, G. N., Lee, J. K., Liu, A., Benitez, A. M., Fani, N., Walker, K. A., Mielke, M. M., & Bateman, J. R. (2025) · Alzheimer s & Dementia · doi:10.1002/alz.70523 · Cited by 5
Metadata
- Authors
- Petersen, R. C., Aisen, P. S., Beckett, L. A., Donohue, M. C., Gamst, A. C., Harvey, D. J., Jack, C. R., Jagust, W. J., Shaw, L. M., Toga, A. W., Trojanowski, J. Q., & Weiner, M. W.
- Affiliations
- Mayo Clinic (US)
- ORCID
- Venue
- Neurology
- Published
- 2010-01-19
- Volume
- 74
- Issue
- 3
- Pages
- 201-209
- ISSN
- 0028-3878 (ISSN-L)
- 1526-632X
- PMC
- PMC2809036
- OpenAlex
- W2122328291
- Cited by
- 2,572
- References
- 27
- Citations by year
- 2026: 312 · 2025: 391 · 2024: 327 · 2023: 254 · 2022: 233 · 2021: 209 · 2020: 182 · 2019: 121 · 2018: 85 · 2017: 67 · 2016: 71 · 2015: 66 · 2014: 45 · 2013: 63 · 2012: 59
- FWCI
- 26.56
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Neurology (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (submittedVersion)
- PubMed Central (submittedVersion)
- Funders
- Alzheimer's Disease Neuroimaging Initiative (AG024904)
- National Institutes of Health (5r01ag027984-03, 5r01ag030048-05, n01mh22005-16-0-1, 5r01dk075128-05, 5u01ag006786-38, 5r01mh083552-05, 5r01ca104573-05, 5p30ag010124-30, 5p01ag009215-20, 5r01ag011378-25, 5p41rr023953-04, 5u01ag024904-10, 5u01ag029213-05, 5r01ag016381-08, 5r01eb006266-04, 5r01ag010897-27, AG024904, 5p01ag019724-23, 5p30ca093373-23, 5p50ag016574-20)
- National Institute on Aging (AG027984, U01-AG-024904, R01AG11378, P30AG010129, U19 AG010483, U01‐AG10483, R01-AG030048, P50‐AG16574, P01 & AG019724, U01AG06786, P50-AG23501, RC1AG035427, AG 010483, R01AG031224, AG031224, P01 AG 17586-10, 1 U01 AG024904, AG 024904, UO1AG029213-01, P30AG036468, 2 P50 AG016570, 1 U01 AG 024904-05, P30 AG 10124-18, R01 AG031252, P01AG017586, U01AG010483, 1 U01 AG24904, 1P01 AG-19724-07, R01AG027859, P50AG023501, AG027859, P01 AG012435, AG-11378, 2P50AG005133, RC2AG036535, P01-AG19724, P01 AG 09215-20, R01-AG 10897, 2P30AG10129, R01 AG029672, R01 AG16381)
- National Heart, Lung, and Blood Institute (HL-095089, R01HL095089)
- National Institute of Mental Health (U01MH083506, MH 22005, MH062512, MH083506, R01MH079752, R01MH069433, MH083552, MH079752, R01 MH069259, 5 R01 MH071940, MH069433, R01MH072641, P30MH062512)
- National Institute on Drug Abuse (1 R01 DA017830)
- National Cancer Institute (2 P30 CA93373, CA104573)
- National Institute of Diabetes and Digestive and Kidney Diseases (DK075128)
- National Institute of Neurological Disorders and Stroke (1 R01 NS050792, RL1NS062412, P50NS053488, 1 R01 NS049194, RC2NS069368, R01NS031966)
- National Institute of Biomedical Imaging and Bioengineering (1 U54 EB05149, 1 R01 EB 006266-01, U54EB005149, P01EB001955, 5R01EB002138-07)
- U.S. National Library of Medicine (2 R01LM005639)
- National Center for Research Resources (5 P41 RR013642, P41 RR-023953, U24RR021760, U54RR021813, R24 RR021382, R24 RR021992, KL2RR024144)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01HD050735, 1R01 HD053893-01)
- 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
- Health Sciences › Medicine › Oncology › Cancer-related cognitive impairment studies
- Keywords
- Alzheimer's disease, mild cognitive impairment, neuroimaging biomarkers, cerebrospinal fluid biomarkers, amyloid-beta 42, longitudinal cognitive change, normal aging, memory impairment, ADAS-Cog
- MeSH terms
- Aged, Aged, 80 and over, Alzheimer Disease, Cross-Sectional Studies, Diagnostic Imaging, Female, Follow-Up Studies, Humans, Longitudinal Studies, Male, Middle Aged, Biomarkers
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.
A language model drew the claims from the paper on 2026-10-18, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-18 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
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 ›
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