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
AI snapshot
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.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Verification
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.
- Based: passes — The paper gives a reason why it holds.
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.
- 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.
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.
- 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.
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
Cites
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
Development and validation of a geriatric depression screening scale: a preliminary report
Yesavage, J. A., Brink, T. L., Rose, T. L., Lum, O., Huang, V., Adey, M., & Leirer, V. O. (1982) · Journal of Psychiatric Research · doi:10.1016/0022-3956(82)90033-4 · Cited by 15,025
Mild Cognitive Impairment: Clinical Characterization and Outcome
Petersen, R. C., Smith, G. E., Waring, S. C., Ivnik, R. J., Tangalos, E. G., & Kokmen, E. (1999) · Archives of Neurology · doi:10.1001/archneur.56.3.303 · Cited by 8,980
Self-citationRandom-Effects Models for Longitudinal Data
Laird, N. M., & Ware, J. H. (1982) · Biometrics · doi:10.2307/2529876 · Cited by 8,923
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
Mild cognitive impairment as a diagnostic entity
Petersen, R. C. (2004) · Journal of Internal Medicine · doi:10.1111/j.1365-2796.2004.01388.x · Cited by 7,864
Self-citationThe Neuropsychiatric Inventory: Comprehensive assessment of psychopathology in dementia
Cummings, J. L., Mega, M., Gray, K., Rosenberg-Thompson, S., Carusi, D. A., & Gornbein, J. (1994) · Neurology · doi:10.1212/wnl.44.12.2308 · Cited by 7,568
A new rating scale for Alzheimer's disease
(1984) · American Journal of Psychiatry · doi:10.1176/ajp.141.11.1356 · Cited by 4,175
Gauthier, S. G., Reisberg, ., Zaudig, M., Petersen, R. C., Ritchie, K. A., Broich, K., Belleville, S., Brodaty, H., Bennett, D. A., Chertkow, H. M., et al. (2006) · The Lancet · doi:10.1016/s0140-6736(06)68542-5 · Cited by 4,094
Self-citationL’examen Clinique en Psychologie
Rubé, P. (1959) · American Journal of Psychotherapy · doi:10.1176/appi.psychotherapy.1959.13.4.989 · Cited by 3,299
Cited by
Sorted by citations
Items 101–200 of 2,570
Zhang, Y., Wang, S., Xia, K., Jiang, Y., & Qian, P. (2020) · Information Fusion · doi:10.1016/j.inffus.2020.09.002 · Cited by 184
Modeling Task fMRI Data Via Deep Convolutional Autoencoder
Huang, H., Hu, X., Zhao, Y., Makkie, M., Dong, Q., Zhao, S., Guo, L., & Liu, T. (2017) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2715285 · Cited by 187
Longitudinal change in CSF Tau and Aβ biomarkers for up to 48 months in ADNI
Toledo, J. B., Xie, S. X., Trojanowski, J. Q., & Shaw, L. M. (2013) · Acta Neuropathologica · doi:10.1007/s00401-013-1151-4 · Cited by 180
Self-citationDouaud, G., Menke, R. A. L., Gass, A., Monsch, A. U., Rao, A., Whitcher, B., Zamboni, G., Matthews, P. M., Sollberger, M., & Smith, S. (2013) · The Journal of Neuroscience · doi:10.1523/jneurosci.4437-12.2013 · Cited by 180
“Best prediction accuracy of 91% is obtained by combining MRI and CSF measures following the observations made by Jack and others (Vemuri et al., 2009, Jack et al., 2010, Petersen et al., 2010).”
MethodDeep Learning and Neurology: A Systematic Review
Valliani, A. A.-A., Ranti, D. L., & Oermann, E. K. (2019) · Neurology and Therapy · doi:10.1007/s40120-019-00153-8 · Cited by 184
“A variety of publicly available databases, such as the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and Brain Tumor Segmentation Benchmark (BraTS), have become available to spur advancements in neuroimaging analysis [32, 33].”
BackgroundCOINS: An Innovative Informatics and Neuroimaging Tool Suite Built for Large Heterogeneous Datasets
Scott, A., Courtney, W., Wood, D., de la Garza, R., Lane, S., King, M. D., Wang, R., Roberts, J., Turner, J. A., & Calhoun, V. D. (2011) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2011.00033 · Cited by 178
Factors affecting Aβ plasma levels and their utility as biomarkers in ADNI
Toledo, J. B., Vanderstichele, H., Figurski, M., Aisen, P. S. S., Petersen, R. C., Weiner, M. W., Jack, C. R., Jagust, W., DeCarli, C. S., Toga, A. W., et al. (2011) · Acta Neuropathologica · doi:10.1007/s00401-011-0861-8 · Cited by 182
Self-citationDong, A., Toledo, J. B., Honnorat, N., Doshi, J. J., Varol, E., Sotiras, A., Wolk, D. A., Trojanowski, J. Q., Davatzikos, C. A., & Initiative, A. D. N. (2016) · Brain · doi:10.1093/brain/aww319 · Cited by 183
Self-citationA second X chromosome contributes to resilience in a mouse model of Alzheimer’s disease
Davis, E. J., Broestl, L., Abdulai-Saiku, S., Worden, K., Bonham, L. W., Miñones-Moyano, E., Moreno, A. J., Wang, D., Chang, K., Williams, G., et al. (2020) · Science Translational Medicine · doi:10.1126/scitranslmed.aaz5677 · Cited by 176
Morris, J. C. (2012) · Archives of Neurology · doi:10.1001/archneurol.2011.3152 · Cited by 180
Report of the task force on designing clinical trials in early (predementia) AD
Aisen, P. S. S., Andrieu, S., Sampaio, C., Carrillo, M. C., Khachaturian, Z. S., Dubois, B., Feldman, H., Petersen, R. C., Siemers, E., Doody, R. S., et al. (2010) · Neurology · doi:10.1212/wnl.0b013e318207b1b9 · Cited by 173
Self-citationMild Cognitive Impairment in Geriatrics
Tangalos, E. G., & Petersen, R. C. (2018) · Clinics in Geriatric Medicine · doi:10.1016/j.cger.2018.06.005 · Cited by 182
Self-citationChintakindi, B. M., Hashmi, M. F., Bokde, N. D., & Geem, Z. W. (2020) · Applied Sciences · doi:10.3390/app10093280 · Cited by 188
“ILD [188] and LIDC-IDRI [189] datasets for the lung; ADNI [190], BRATS [191], and OASIS-3 [175,192] datasets for the brain; DDSM [193], MIAS [194], CAMELYON 17 [195], and INbreast [196] datasets for the breast; DRIVE [197], STARE [198], and MESSIDOR-2 [199] datasets for the eye.”
BackgroundSamper‐Gonzàlez, J., Burgos, N., Bottani, S., Fontanella, S., Lu, P., Marcoux, A., Routier, A., Guillon, J., Bacci, M., Wen, J., et al. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.08.042 · Cited by 188
Bae, J. B., Lee, S., Jung, W., Park, S., Kim, W., Oh, H., Han, J. W., Kim, G. E., Kim, J. S., Kim, J. H., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-79243-9 · Cited by 171
Sørensen, L., Igel, C., Pai, A., Balas, I., Anker, C., Lillholm, M., & Nielsen, M. (2017) · NeuroImage: Clinical · doi:10.1016/j.nicl.2016.11.025 · Cited by 179
Clinical and multimodal biomarker correlates of ADNI neuropathological findings
Toledo, J. B., Cairns, N. J., Da, X., Chen, K., Carter, D. R., Fleisher, A., Householder, E., Ayutyanont, N., Roontiva, A., Bauer, R. J., III, et al. (2013) · Acta Neuropathologica Communications · doi:10.1186/2051-5960-1-65 · Cited by 171
Self-citationObstructive Sleep Apnea and Longitudinal Alzheimer's Disease Biomarker Changes
Bubu, O. M., Pirraglia, E., Andrade, A. G., Sharma, R. A., Giménez, S., Umasabor‐Bubu, O. Q., Hogan, M. M., Shim, A., Mukhtar, F., Sharma, N. K., et al. (2018) · SLEEP · doi:10.1093/sleep/zsz048 · Cited by 176
“ADNI criteria for subject classification are described elsewhere [12].”
Mohammed, B., Senan, E. M., Rassem, T. H., Makbol, N. M., Alanazi, A., Al-Mekhlafi, Z., Almurayziq, T. S., & Ghaleb, F. A. (2021) · Electronics · doi:10.3390/electronics10222860 · Cited by 150
Clinical Research on Alzheimer’s Disease: Progress and Perspectives
Sun, B., Li, W., Zhu, C., Jin, W., Zeng, F., Liu, Y.-H., Bu, X., Zhu, J., Yao, X., & Wang, Y. (2018) · Neuroscience Bulletin · doi:10.1007/s12264-018-0249-z · Cited by 171
Type 2 diabetes mellitus, brain atrophy, and cognitive decline
Moran, C., Beare, R. J., Wang, W., Callisaya, M. L., Srikanth, V. K., Initiative, A. D. N., Initiative, A. D. N., Weiner, M. W., Aisen, P., Weiner, M. W., et al. (2019) · Neurology · doi:10.1212/wnl.0000000000006955 · Cited by 165
Self-citationSundermann, E. E., Initiative, A. D. N., Biegon, A. R., Rubin, L. H., Lipton, R. B., Landau, S. M., & Maki, P. M. (2017) · Journal of Alzheimer s Disease · doi:10.3233/jad-160716 · Cited by 163
Basal forebrain volume reliably predicts the cortical spread of Alzheimer’s degeneration
Fernández‐Cabello, S., Kronbichler, M., Dijk, K. R. A. V., Goodman, J. A., Spreng, R. N., Schmitz, T. W., & Initiative, O. B. O. T. A. D. N. (2020) · Brain · doi:10.1093/brain/awaa012 · Cited by 159
Vascular burden and Alzheimer disease pathologic progression
Lo, R. Y., Jagust, W. J., Weiner, M., Aisen, P., Weiner, M., Aisen, P., Petersen, R., Jack, C. R., Jagust, W., Trojanowki, J. Q., et al. (2012) · Neurology · doi:10.1212/wnl.0b013e31826c1b9d · Cited by 156
Self-citationFDG-PET as an independent biomarker for Alzheimer’s biological diagnosis: A longitudinal study
Initiative, O. B. O. A. D. N., Ou, Y.-N., Xu, W., Li, J.-Q., Guo, Y., Cui, M., Chen, K.-L., Huang, Y.-Y., Dong, Q., Tan, L., et al. (2019) · Alzheimer's Research & Therapy · doi:10.1186/s13195-019-0512-1 · Cited by 155
(2011) · Routledge eBooks · doi:10.4324/9780203152836-7 · Cited by 154
Does mild cognitive impairment always lead to dementia? A review
Pandya, S. Y., Clem, M. A., Silva, L. M., & Woon, F. L. M. (2016) · Journal of the Neurological Sciences · doi:10.1016/j.jns.2016.07.055 · Cited by 160
Atrophy subtypes in prodromal Alzheimer’s disease are associated with cognitive decline
ten Kate, M., Dicks, E., Visser, P. J., van der Flier, W. M., Teunissen, C. E., Barkhof, F., Scheltens, P., Tijms, B. M., & Initiative, A. D. N. (2018) · Brain · doi:10.1093/brain/awy264 · Cited by 150
Effect of data leakage in brain MRI classification using 2D convolutional neural networks
Yagis, E., Atnafu, S. W., García Seco de Herrera, A., Marzi, C., Scheda, R., Giannelli, M., Tessa, C., Citi, L., & Diciotti, S. (2021) · Scientific Reports · doi:10.1038/s41598-021-01681-w · Cited by 161
“For AD classification, accuracy on the test set, using subject-level CV, was below 71% for large datasets (OASIS-200 and ADNI), whereas they were below 59% for smaller datasets (OASIS-34).”
MethodInfluentialDiagnosing Mild Cognitive Impairment (MCI) in clinical trials: a systematic review
Stephan, B. C. M., Minett, T., Pagett, E., Siervo, M., Brayne, C., & McKeith, I. G. (2013) · BMJ Open · doi:10.1136/bmjopen-2012-001909 · Cited by 154
Besser, L. M., Gill, D. P., Monsell, S. E., Brenowitz, W. D., Meranus, D. H., Kukull, W. A., & Gustafson, D. (2013) · Alzheimer Disease & Associated Disorders · doi:10.1097/wad.0000000000000005 · Cited by 150
Caroli, A., & Frisoni, G. B. (2010) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2010.04.024 · Cited by 150
Interpretable machine learning for dementia: a systematic review
Martin, S. A., Townend, F. J., Barkhof, F., & Cole, J. H. (2023) · Alzheimer's & Dementia · doi:10.1002/alz.12948 · Cited by 149
de la Torre, J. (2018) · Journal of Alzheimer s Disease · doi:10.3233/jad-180004 · Cited by 161
Concordant peripheral lipidome signatures in two large clinical studies of Alzheimer’s disease
Huynh, K., Lim, W. L. F., Giles, C., Jayawardana, K. S., Salim, A., Mellett, N. A., Smith, A. A. T., Olshansky, G., Drew, B. G., Chatterjee, P., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-19473-7 · Cited by 154
The role of apolipoprotein E (APOE) genotype in early mild cognitive impairment (E-MCI)
Risacher, S. L. (2013) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2013.00011 · Cited by 151
Sugden, K., Caspi, A., Elliott, M. L., Bourassa, K. J., Chamarti, K., Corcoran, D. L., Hariri, A. R., Houts, R. M., Kothari, M., Kritchevsky, S., et al. (2022) · Neurology · doi:10.1212/wnl.0000000000200898 · Cited by 156
The role of explainable AI in the context of the AI Act
Panigutti, C., Hamon, R., Hupont, I., Llorca, D. F., Yela, D. F., Junklewitz, H., Scalzo, S., Mazzini, G., Sánchez, I., Garrido, J. S., et al. (2023) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3593013.3594069 · Cited by 201
Brix, M. K., Westman, E., Simmons, A., Ringstad, G. A., Eide, P. K., Wagner-Larsen, K. S., Page, C. M., Vitelli, V., & Beyer, M. K. (2017) · European Journal of Radiology · doi:10.1016/j.ejrad.2017.07.013 · Cited by 149
Iwatsubo, T., Iwata, A., Suzuki, K., Ihara, R., Arai, H., Ishii, K., Senda, M., Ito, K., Ikeuchi, T., Kuwano, R., et al. (2018) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2018.03.009 · Cited by 142
Self-citationTowards generalist foundation model for radiology by leveraging web-scale 2D&3D medical data
Wu, C., Zhang, X., Zhang, Y., Hui, H., Wang, Y., & Xie, W. (2025) · Nature Communications · doi:10.1038/s41467-025-62385-7 · Cited by 429
Finances in the Older Patient With Cognitive Impairment
Widera, E. (2011) · JAMA · doi:10.1001/jama.2011.164 · Cited by 146
Ewers, M., Biechele, G., Suárez‐Calvet, M., Sacher, C., Blume, T., Morenas‐Rodriguez, E., Deming, Y., Piccio, L., Cruchaga, C., Kleinberger, G., et al. (2020) · EMBO Molecular Medicine · doi:10.15252/emmm.202012308 · Cited by 133
“…normal (CN, MMSE > 24, CDR = 0, non-depressed), mild cognitively impaired (MCI; MMSE > 24, CDR = 0.5, objective memory-loss on the education adjusted Wechsler Memory Scale II, preserved activities of daily living) or AD dementia following standard diagnostic criteria (Petersen et al, 2010).”
InfluentialSelf-citationObjective subtle cognitive difficulties predict future amyloid accumulation and neurodegeneration
Thomas, K. R., Bangen, K. J., Weigand, A. J., Edmonds, E. C., Wong, C. G., Cooper, S., Delano‐Wood, L., Bondi, M. W., & Initiative, A. D. N. (2019) · Neurology · doi:10.1212/wnl.0000000000008838 · Cited by 144
“Participants Specific enrollment criteria for ADNI have been described previously in detail elsewhere.(19) Briefly, participants from ADNI were between 55 and 90 years old, had at least 6 years of education or work history equivalent, had a Geriatric Depression Scale score <6, had a Hachinski Ischemia Scale score <5,…”
MethodSegregation of functional networks is associated with cognitive resilience in Alzheimer’s disease
Ewers, M., Luan, Y., Frontzkowski, L. K., Neitzel, J., Rubinski, A., Dichgans, M., Hassenstab, J. J., Gordon, B. A., Chhatwal, J. P., Levin, J., et al. (2021) · Brain · doi:10.1093/brain/awab112 · Cited by 146
Ghazi, M. M., Nielsen, M., Pai, A., Cardoso, M. J., Marc Modat, A., Sebastien Ourselin, A., & Sørensen, L. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.01.004 · Cited by 141
“…values due to, for instance, dropped out patients, unsuccessful measurements, or different assessment patterns used for different subject groups – as seen in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) ( Petersen et al., 2010 ), standard RNNs inclunding LSTMs cannot be directly applied.”
Early versus late MCI: Improved MCI staging using a neuropsychological approach
Edmonds, E. C., McDonald, C. R., Marshall, A. J., Thomas, K. R., Eppig, J. S., Weigand, A. J., Delano‐Wood, L., Galasko, D. R., Salmon, D. P., Bondi, M. W., et al. (2019) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2018.12.009 · Cited by 143
“CN participants in ADNI-2 were classified via the following criteria [1]: (1) no memory complaints, beyond what would be expected for age; (2) normal memory function documented by scoring above education-adjusted cutoffs on delayed free recall of Story A from WMS-R Logical Memory II (a score of 9 for 16 or more years…”
InfluentialFrizzell, T. O., Glashutter, M., Liu, C. C., Zeng, A., Pan, D., Hajra, S. G., D’Arcy, R. C. N., & Song, X. (2022) · Ageing Research Reviews · doi:10.1016/j.arr.2022.101614 · Cited by 146
Alzheimer's Disease Neuroimaging Initiative 2 Clinical Core: Progress and plans
Aisen, P. S. S., Petersen, R. C., Donohue, M., Weiner, M. W., & Initiative, A. D. N. (2015) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2015.05.005 · Cited by 139
Self-citationThe accuracy and robustness of plasma biomarker models for amyloid PET positivity
Benedet, A. L., Brum, W. S., Hansson, O., Initiative, A. D. N., Karikari, T. K., Zimmer, E. R., Zetterberg, H., Blennow, K., & Ashton, N. J. (2022) · Alzheimer s Research & Therapy · doi:10.1186/s13195-021-00942-0 · Cited by 147
3D PIB and CSF biomarker associations with hippocampal atrophy in ADNI subjects
Apostolova, L. G., Hwang, K., Andrawis, J. P., Green, A. E., Babakchanian, S., Morra, J. H., Cummings, J. L., Toga, A. W., Trojanowski, J. Q., Shaw, L. M. J., et al. (2010) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2010.05.003 · Cited by 144
Self-citationDementia risk and prevention by targeting modifiable vascular risk factors
Tariq, S., & Barber, P. A. (2017) · Journal of Neurochemistry · doi:10.1111/jnc.14132 · Cited by 140
“Assessing the association of P-Tau/ Ab1-42 ratio to rates of cerebral atrophy is expected to provide an indication of potential contribution of AD to loss of brain volume in the preclinical dementia (Hansson et al. 2006; Fagan et al. 2007; Jack et al. 2008; Petersen et al. 2010).”
BackgroundCedarbaum, J. M., Jaros, M. J., Hernandez, C., Coley, N., Andrieu, S., Grundman, M., Vellas, B. J., & Initiative, A. D. N. (2012) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2011.11.002 · Cited by 143
Deep Learning-Based Diagnosis of Alzheimer’s Disease
Saleem, T. J., Zahra, S. R., Wu, F., Alwakeel, A., Alwakeel, M. M., Jeribi, F., & Hijji, M. (2022) · Journal of Personalized Medicine · doi:10.3390/jpm12050815 · Cited by 117
Transfer learning in magnetic resonance brain imaging: a systematic review
Valverde, J. M., Imani, V., Abdollahzadeh, A., De Feo, R., Prakash, M., Ciszek, R., & Tohka, J. (2021) · Journal of Imaging · doi:10.3390/jimaging7040066 · Cited by 137
Nation, D. A., Edmonds, E. C., Bangen, K. J., Delano‐Wood, L., Scanlon, B. K., Han, S. D., Edland, S. D., Salmon, D. P., Galasko, D. R., & Bondi, M. W. (2015) · JAMA Neurology · doi:10.1001/jamaneurol.2014.4477 · Cited by 133
Toledo, J. B., Toledo, E., Weiner, M. W., Jack, C. R., Jagust, W., Lee, V. M. ‐., Shaw, L. M. J., & Trojanowski, J. Q. (2012) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2011.08.008 · Cited by 130
Self-citationBarone, E., Di Domenico, F., Mancuso, C., & Butterfield, D. A. (2014) · Neurobiology of Disease · doi:10.1016/j.nbd.2013.09.018 · Cited by 127
Rueda, A. D., Lau, K. M., Saito, N. H., Harvey, D. J., Risacher, S. L., Aisen, P. S. S., Petersen, R. C., Saykin, A. J., Farias, S. T., & Initiative, A. D. N. (2014) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2014.09.002 · Cited by 138
Self-citationAmyloid negativity in patients with clinically diagnosed Alzheimer disease and MCI
Landau, S. M., Horng, A., Fero, A., Jagust, W. J., Initiative, A. D. N., Initiative, A. D. N., Weiner, M. W., Aisen, P., Weiner, M. W., Aisen, P., et al. (2016) · Neurology · doi:10.1212/wnl.0000000000002576 · Cited by 128
Self-citationMultimodal Federated Learning: A Survey
Che, L., Wang, J., Zhou, Y., & Ma, F. (2023) · Sensors · doi:10.3390/s23156986 · Cited by 143
Integrating multi-omics data with EHR for precision medicine using advanced artificial intelligence
Tong, L., Shi, W., Isgut, M., Zhong, Y., Lais, P., Gloster, L., Sun, J., Swain, A., Giuste, F., & Wang, M. D. (2024) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2023.3324264 · Cited by 130
Accurate brain‐age models for routine clinical MRI examinations
Wood, D. A., Kafiabadi, S., Busaidi, A. A., Guilhem, E., Montvila, A., Lynch, J., Townend, M., Agarwal, S., Mazumder, A., Barker, G. J., et al. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.118871 · Cited by 131
Mendelian Randomization Study of PCSK9 and HMG-CoA Reductase Inhibition and Cognitive Function
Rosoff, D. B., Bell, A. S., Jung, J., Wagner, J., Mavromatis, L. A., & Lohoff, F. W. (2022) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2022.05.041 · Cited by 126
APOE genotype and early β-amyloid accumulation in older adults without dementia
Lim, Y. Y., Mormino, E. C., Initiative, F. T. A. D. N., Initiative, A. D. N., Weiner, M., Aisen, P., Weiner, M., Aisen, P., Petersen, R., Jack, C. R., et al. (2017) · Neurology · doi:10.1212/wnl.0000000000004336 · Cited by 125
Self-citationAnatomically interpretable deep learning of brain age captures domain-specific cognitive impairment
Yin, C., Imms, P. E., Cheng, M., Amgalan, A., Chowdhury, N. F., Massett, R. J., Chaudhari, N. N., Chen, X., Thompson, P. M., Bogdan, P., et al. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2214634120 · Cited by 129
Self-citationRitchie, C. W., & Ritchie, K. A. (2012) · BMJ Open · doi:10.1136/bmjopen-2012-001893 · Cited by 124
Temporal Dynamics of β-Amyloid Accumulation in Aging and Alzheimer Disease
Jagust, W. J., Landau, S. M., & Jack, C. R. (2021) · Neurology · doi:10.1212/wnl.0000000000011524 · Cited by 125
Self-citationFerreira, D., Cavallin, L., Larsson, E., Muehlboeck, J., Mecocci, P., Vellas, B. J., Tsolaki, M., Kłoszewska, I., Soininen, H., Lovestone, S., et al. (2015) · Journal of Internal Medicine · doi:10.1111/joim.12358 · Cited by 119
Rahim, N., El–Sappagh, S., Ali, S., Muhammad, K., Ser, J. D., & Abuhmed, T. (2022) · Information Fusion · doi:10.1016/j.inffus.2022.11.028 · Cited by 131
Karantzoulis, S., Novitski, J., Gold, M., & RANDOLPH, C. C. (2013) · Archives of Clinical Neuropsychology · doi:10.1093/arclin/act057 · Cited by 120
Byun, M. S., Kim, S. E., Park, J., Yi, D., Choe, Y. M., Sohn, B. K., Choi, H. J., Baek, H., Han, J., Woo, J. I., et al. (2015) · PLoS ONE · doi:10.1371/journal.pone.0142756 · Cited by 121
“Details of inclusion/exclusion criteria and protocols have been previously described [28].”
Byun, M. S., Yi, D., Lee, J. H., Choe, Y. M., Sohn, B. K., Lee, J., Choi, H. J., Baek, H., Kim, Y. K., Lee, Y., et al. (2017) · Psychiatry Investigation · doi:10.4306/pi.2017.14.6.851 · Cited by 120
Associations of the Top 20 Alzheimer Disease Risk Variants With Brain Amyloidosis
Apostolova, L. G., Risacher, S. L., Duran, T., Stage, E., Goukasian, N., West, J. D., Do, T. M., Grotts, J. F., Wilhalme, H. M., Nho, K. T., et al. (2018) · JAMA Neurology · doi:10.1001/jamaneurol.2017.4198 · Cited by 130
Biological heterogeneity in ADNI amnestic mild cognitive impairment
Nettiksimmons, J., DeCarli, C. S., Landau, S. M., Beckett, L. A., & Initiative, A. D. N. (2014) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2013.09.003 · Cited by 124
Self-citationThe BIN1 rs744373 SNP is associated with increased tau-PET levels and impaired memory
Franzmeier, N., Rubinski, A., Neitzel, J., Ewers, M., (ADNI), T. A. D. N. I., Weiner, M. W., Aisen, P., Petersen, R., Jack, C. R., Jagust, W., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-09564-5 · Cited by 109
Self-citationAnimal Functional Magnetic Resonance Imaging: Trends and Path Toward Standardization
Mandino, F., Cerri, D. H., Garin, C. M., Straathof, M., van Tilborg, G. A. F., Chakravarty, M. M., Dhénain, M., Dijkhuizen, R. M., Gozzi, A., Heß, A., et al. (2020) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2019.00078 · Cited by 115
Spatial patterns of brain amyloid- burden and atrophy rate associations in mild cognitive impairment
Tosun, D., Schuff, N. W., Mathis, C. A., Jagust, W., Weiner, M. W., & Initiative, A. D. N. (2011) · Brain · doi:10.1093/brain/awr044 · Cited by 122
Self-citationPRECISION MEDICINE - THE GOLDEN GATE FOR DETECTION, TREATMENT AND PREVENTION OF ALZHEIMER’S DISEASE
Hampel, H. J., O’Bryant, S. E., Castrillo, J. I., Ritchie, C. W., Rojkova, K., Broich, K., Benda, N., Nisticò, R., Frank, R., Dubois, B., et al. (2016) · The Journal of Prevention of Alzheimer s Disease · doi:10.14283/jpad.2016.112 · Cited by 117
Brain-age prediction: A systematic comparison of machine learning workflows
More, S., Antonopoulos, G., Hoffstaedter, F., Caspers, J., Eickhoff, S. B., Patil, K. R., & Initiative, T. A. D. N. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.119947 · Cited by 118
Gunawardena, K. A. N. N. P., Rajapakse, R. N., & Kodikara, N. D. (2017) · International Conference on Mechatronics and Machine Vision in Practice (M2VIP) · doi:10.1109/m2vip.2017.8211486 · Cited by 116
Predicting Aggressive Decline in Mild Cognitive Impairment
Tosto, G., Zimmerman, M. E., Carmichael, O. T., & Brickman, A. M. (2014) · JAMA Neurology · doi:10.1001/jamaneurol.2014.667 · Cited by 118
Xu, J., Mao, C., Hou, Y., Luo, Y., Binder, J. L., Zhou, Y., Bekris, L. M., Shin, J., Hu, M., Wang, F., et al. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.111717 · Cited by 115
Nonlinear registration of longitudinal images and measurement of change in regions of interest
Holland, D., & Dale, A. M. (2011) · Medical Image Analysis · doi:10.1016/j.media.2011.02.005 · Cited by 114
Type 2 diabetes mellitus is associated with brain atrophy and hypometabolism in the ADNI cohort
Li, W., Risacher, S. L., Huang, E. S., Saykin, A. J., & Initiative, A. D. N. (2016) · Neurology · doi:10.1212/wnl.0000000000002950 · Cited by 113
Genetic risk for alzheimer disease is distinct from genetic risk for amyloid deposition
Leonenko, G., Shoai, M., Bellou, E., Sims, R., Williams, J., Hardy, J., Escott‐Price, V., & Initiative, T. A. D. N. (2019) · Annals of Neurology · doi:10.1002/ana.25530 · Cited by 114
“Details of the ADNI design, participant recruitment, clinical testing, and additional methods have been previously reported elsewhere.(23,24)”
MethodDerby, C. A., Burns, L. C., Wang, C., Katz, M. J., Zimmerman, M. E., L’Italien, G., Guo, Z., Berman, R., & Lipton, R. B. (2013) · Neurology · doi:10.1212/wnl.0b013e31828ab2c9 · Cited by 118
Sex-specific genetic predictors of Alzheimer’s disease biomarkers
(ADNI), A. D. N. I., Deming, Y., The Alzheimer Disease Genetics Consortium (ADGC), Dumitrescu, L., Barnes, L. L., Thambisetty, M., Kunkle, B., Gifford, K. A., Bush, W. S., Chibnik, L. B., et al. (2018) · Acta Neuropathologica · doi:10.1007/s00401-018-1881-4 · Cited by 110
Lim, Y. Y., Jaeger, J., Harrington, K., Ashwood, T., Ellis, K. A., Stöffler, A., Szoeke, C. E. I., Lachovitzki, R., Martins, R. N., Villemagne, V. L., et al. (2013) · Archives of Clinical Neuropsychology · doi:10.1093/arclin/act021 · Cited by 117
“, 1998), there is also agreement that these same measures do not possess sufficient sensitivity to detect cognitive change in the earlier stages of the disease (Jelic, Kivipelto, & Winblad, 2006; Petersen et al., 2010).”
BackgroundMethodBrain-age is associated with progression to dementia in memory clinic patients
Biondo, F., Jewell, A., Pritchard, M., Aarsland, D., Steves, C. J., Mueller, C., & Cole, J. H. (2022) · NeuroImage Clinical · doi:10.1016/j.nicl.2022.103175 · Cited by 115
Toussaint, P., Perlbarg, V., Bellec, P., Désarnaud, S., Lacomblez, L., Doyon, J., Habert, M., & Benali, H. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.03.091 · Cited by 112
“Eligibility criteria were as follows (see Petersen et al., 2010, for a thorough description of participant recruitment and classi fi cation protocol).”
Suárez‐Calvet, M., Capell, A., Araque Caballero, M. Á., Morenas‐Rodríguez, E., Fellerer, K., Franzmeier, N., Kleinberger, G., Eren, E., Deming, Y., Piccio, L., et al. (2018) · EMBO Molecular Medicine · doi:10.15252/emmm.201809712 · Cited by 109
“Inclusion and exclusion criteria for the ADNI study were previously described comprehensively (Petersen et al, 2010).”
BackgroundSelf-citationAlzheimer's Disease Risk Assessment Using Large-Scale Machine Learning Methods
Casanova, R., Hsu, F., Sink, K. M., Rapp, S. R., Williamson, J. D., Resnick, S. M., Espeland, M. A., & Initiative, A. D. N. (2013) · PLoS ONE · doi:10.1371/journal.pone.0077949 · Cited by 107
Ouellet, É., Boller, B., Corriveau‐Lecavalier, N., Cloutier, S., & Belleville, S. (2018) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2018.03.010 · Cited by 108
Learning disentangled representations in the imaging domain
Liu, X., Sanchez, P., Thermos, S., O’Neil, A. Q., & Tsaftaris, S. A. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102516 · Cited by 130
Tosun, D., Landau, S. M., Aisen, P. S. S., Petersen, R. C., Mintun, M. A., Jagust, W., Weiner, M. W., & Initiative, A. D. N. (2017) · Brain · doi:10.1093/brain/awx046 · Cited by 110
Self-citationMcEvoy, L. K., Holland, D., Hagler, D. J., Fennema‐Notestine, C., Brewer, J. B., & Dale, A. M. (2011) · Radiology · doi:10.1148/radiol.11101975 · Cited by 107
Free Water in White Matter Differentiates MCI and AD From Control Subjects
Dumont, M., Roy, M., Jodoin, P., Morency, F. C., Houde, J., Xie, Z., Bauer, C., Samad, T. A., Dijk, K. R. A. V., Goodman, J. A., et al. (2019) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2019.00270 · Cited by 111
“MCI subjects included early and late MCI with impaired memory and a CDR of 0.5, while AD subjects met criteria for dementia and had a CDR between 0.5 and 1 [31].”
ResultInfluentialBrain tumor image generation using an aggregation of GAN models with style transfer
Mukherkjee, D., Saha, P., Kaplun, D., Sinitca, A., & Sarkar, R. (2022) · Scientific Reports · doi:10.1038/s41598-022-12646-y · Cited by 106
Beckett, L. A., Donohue, M., Wang, C., Aisen, P. S. S., Harvey, D. J., Saito, N. H., & Initiative, A. D. N. (2015) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2015.05.004 · Cited by 108
Self-citation
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 ›
Are you an author, or do you see a problem with this page? Request a review
