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 1,801–1,900 of 2,570
Quach, B., Salehi, S. N., Roufegarinejad, R., Guleria, V. S., Mante, M., Florio, J. B., Rissman, R. A., & Winston, C. N. (2026) · Experimental Neurology · doi:10.1016/j.expneurol.2026.115893 · Cited by 2
Chang, T. S., Teng, E., Elashoff, D. A., & Grill, J. D. (2016) · Alzheimer Disease & Associated Disorders · doi:10.1097/wad.0000000000000150 · Cited by 2
Challenges in Dementia Studies
Ong, K. T. (2018) · InTech eBooks · doi:10.5772/intechopen.72866 · Cited by 2
Convergent systems-based approaches identify a role for OCIAD1 in Alzheimer's disease
Geertsma, H. M., & Rousseaux, M. W. C. (2020) · EBioMedicine · doi:10.1016/j.ebiom.2020.102627 · Cited by 2
“(http://creativecommons.org/licenses/by-nc-nd/4.0/) repositories (e.g. ADNI, the Alzheimer’s Disease Neuroimaging Initiative [10]) to determine if OCIAD1 is found in blood or cerebral spinal fluid and whether changes in its levels correlate with disease status.”
BackgroundInfluentialLo, Y. T., Kumar, S., Tan, L. Q., et al. (2021) · Neuroradiology · doi:10.1007/s00234-021-02698-8 · Cited by 2
Wang, G., Vance, D. E., & Li, W. (2021) · Journal of Aging Research and Lifestyle · doi:10.14283/jarlife.2021.5 · Cited by 2
Pan, H., Cao, J., Wu, C., Huang, F., Wu, P., Lang, J., & Liu, Y. (2022) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2022.958050 · Cited by 2
Fu, Y., Wang, Z.-T., Huang, L., Tan, C., Cao, X., & Tan, L. (2022) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2022.1008780 · Cited by 2
“Inclusion and exclusion criteria are described in detail online (see text footnote 1) (Petersen et al., 2010).”
BackgroundPölsterl, S., Wachinger, C., Initiative, T. A. D. N., & Initiative, T. J. A. D. N. (2022) · Alzheimer s & Dementia · doi:10.1002/alz.12825 · Cited by 2
Ganesh, A., & Vanamu, G. (2022) · Journal of Emerging Investigators · doi:10.59720/21-053 · Cited by 2
Automatic Neurocranial Landmarks Detection from Visible Facial Landmarks Leveraging 3D Head Priors
Schlesinger, O., Kundu, R., Goetz, S. M., et al. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-45249-9_2 · Cited by 2
An Adaptive Feature Fusion Network for Alzheimer’s Disease Prediction
Wei, S., Li, Y., & Yang, W. (2023) · Lecture notes in computer science · doi:10.1007/978-981-99-7108-4_23 · Cited by 2
Hu, Z., Zhang, X. C., Hou, X., Wang, L., Chen, H., Huang, L., Yang, D., Mo, Y., Xu, Y., & Bai, F. (2024) · Brain and Behavior · doi:10.1002/brb3.3408 · Cited by 2
Wu, Y., Chen, Y., Yang, Y., Lin, C., Su, S., Zhao, J., Wu, S., Wu, G., Liu, H., Liu, X., et al. (2024) · Cerebral Cortex · doi:10.1093/cercor/bhae030 · Cited by 2
Platero, C. A., Tohka, J., & Strange, B. A. (2023) · Current Alzheimer Research · doi:10.2174/0115672050295317240223162312 · Cited by 2
Sertkaya, M. E., & Ergen, B. (2024) · Fırat Üniversitesi Mühendislik Bilimleri Dergisi · doi:10.35234/fumbd.1411320 · Cited by 2
Bano, W., Pulli, E. P., Cantonas, L., Sorsa, A., Hämäläinen, J. A., Karlsson, H., Karlsson, L., Saukko, E., Sainio, T., Peuna, A., et al. (2024) · MethodsX · doi:10.1016/j.mex.2024.102789 · Cited by 2
“Pertaining to neuroimaging, various large multi-centre data exist, e.g. the Adolescent Brain Cognition Development (ABCD) study [ 4 ], UK Biobank [ 5 ], Alzheimer’s Disease Neuroimaging Initiative (ADNI) [ 6 ] etc.”
BackgroundZhuang, X., Xu, G., Amei, A., Cordes, D., Wang, Z., Oh, E. C. T., & Initiative, A. D. N. (2024) · Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring · doi:10.1002/dad2.12597 · Cited by 2
Loizillon, S., Bottani, S., Mabille, S., Jacob, Y., Maire, A., Ströer, S., Dormont, D., Olivier Colliot, A., Burgos, N., Initiative, T. A. D. N., et al. (2024) · The Journal of Machine Learning for Biomedical Imaging · doi:10.59275/j.melba.2024-7fgd · Cited by 3
“The Alzheimer’s Disease Neuroimaging Initiative (ADNI) is a multi-site study of elderly individuals with normal cognition, mild cognitive impairment, or Alzheimer’s disease (Petersen et al., 2010).”
Harkness, B. M., Chen, S., Kim, K., et al. (2024) · Journal of Proteome Research · doi:10.1021/acs.jproteome.4c00339 · Cited by 2
Scarapicchia, V., Kwan, H., Czippel, A., & Gawryluk, J. R. (2024) · Brain Connectivity · doi:10.1089/brain.2023.0078 · Cited by 2
Farhadieh, M., Mozafar, M., Sanaaee, S., Sodeifi, P., Kousha, K., Zare, Y., Zare, S., Rad, N. M., Goharrizi, F. J., Allahverdloo, M., et al. (2024) · Archives of Gerontology and Geriatrics · doi:10.1016/j.archger.2024.105576 · Cited by 2
“Additionally, individuals must exhibit largely intact general cognition and functional performance and must not qualify for a diagnosis of dementia (Petersen et al., 2010).”
InfluentialQian, S., Zheng, Y., Jiang, T., Hou, J., Cao, R., Cai, J., Ma, E., Wang, W., Song, W., & Xie, C. (2024) · The Journals of Gerontology Series A · doi:10.1093/gerona/glae185 · Cited by 2
Evans, T., Vilor-Tejedor, N., Operto, G., et al. (2024) · Biological Psychiatry: Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2024.07.019 · Cited by 3
Self-citationDuan, X., Zhang, Y., Li, W., Su, N., & Sun, L. (2024) · Journal of Affective Disorders · doi:10.1016/j.jad.2024.08.204 · Cited by 2
Long, X., Yuan, M., Zhang, Z., & Fang, Y. (2024) · Archives of Gerontology and Geriatrics · doi:10.1016/j.archger.2024.105659 · Cited by 3
“First, all MCI subjects in ADNI were diagnosed with aMCI at enrollment (Petersen et al., 2010).”
Biophysics-Based Data Assimilation of Longitudinal Tau and Amyloid-$$\beta $$ PET Scans
Wen, Z., Ghafouri, A., & Biros, G. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72083-3_2 · Cited by 3
The Clinical Dementia Rating scale is useful but caution is needed
Petersen, R. C. (2024) · Nature Aging · doi:10.1038/s43587-024-00742-9 · Cited by 2
Self-citationDing, C. H., Yu, L., Wang, H., & Xie, Y. (2024) · Journal of Electronic Imaging · doi:10.1117/1.jei.33.6.063002 · Cited by 3
Chen, Z., Adegboro, A. A., Gu, L., & Li, X. (2024) · Insights into Imaging · doi:10.1186/s13244-024-01848-9 · Cited by 2
TransferGWAS of T1-weighted brain MRI data from UK Biobank
Rakowski, A., Monti, R., & Lippert, C. (2024) · PLoS Genetics · doi:10.1371/journal.pgen.1011332 · Cited by 3
“The first model used for feature extraction was trained on 4,480 T1-weighted scans from the ADNI dataset [49].”
Current challenges and opportunities for tractography
Rheault, F., Poulin, P., Caron, A. V., St‐Onge, E., Schilling, K. G., Petit, L., Dell’Acqua, F., Leemans, A., & Descoteaux, M. (2025) · Elsevier eBooks · doi:10.1016/b978-0-12-818894-1.00037-9 · Cited by 2
Nowrangi, M. A., Leoutsakos, J., Yan, H., Bakker, A., Manning, K. J., Rebok, G. W., Rosenberg, P. B., & Kamath, V. J. (2025) · Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring · doi:10.1002/dad2.12583 · Cited by 2
“30 For the purposes of this analysis, we combined both EMCI and LMCI, as distinguished by ADNI, into MCI.”
Sanderson‐Cimino, M. E., Gross, A. L., Gaynor, L. S., Paolillo, E. W., Saloner, R., Albert, M., Apostolova, L. G., Boersema, B., Boxer, A. L., Boeve, B. F., et al. (2026) · Alzheimer s Research & Therapy · doi:10.1186/s13195-026-02100-w · Cited by 2
Self-citationWu, Y., Sun, S., Zhang, C., Ma, X., Zhu, X., Li, Y., Lin, L., & Fu, Z. (2025) · Bioengineering · doi:10.3390/bioengineering12060607 · Cited by 4
“However, this study is subject to several limitations: (1) The dataset employed in this study is characterized by racial representativeness imbalance [48,49].”
Multilayer Integration of Networks Toolbox (MINT)
Sarraf, S., Avelar‐Pereira, B., Hosseini, S. M. H., & Initiative, A. D. N. (2025) · Communications Biology · doi:10.1038/s42003-025-08269-4 · Cited by 2
Asante, J., Nagel, C., & Barger, S. W. (2025) · Journal of Alzheimer s Disease · doi:10.1177/13872877251351629 · Cited by 2
Ocen, S., Muchemi, L., & Yohannis, M. A. (2025) · Neuroscience & Medicine · doi:10.4236/nm.2025.163013 · Cited by 2
MAN-GNN: An interpretable biomarker architecture for neurodevelopmental disorders
Han, Q., Ye, H., Bai, M., Wang, L., Sun, Y., Song, Z., Zhao, J. M., Shi, L., & Kuang, Z. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.108110 · Cited by 3
DesRuisseaux, L. A., & Suchy, Y. (2025) · The Clinical Neuropsychologist · doi:10.1080/13854046.2025.2589273 · Cited by 2
“Notably, past research has found that some cognitive markers may be more sensitive than cerebrospinal fluid biomarkers and structural brain metrics for predicting conversion to dementia (Chang & Brainerd, 2022; Ewers et al., 2012; Gomar et al., 2011).”
Yang, H., Tan, T., Tan, S., Yang, W., Cai, K., Chen, C., & Sun, Y. (2025) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc58881.2025.11343720 · Cited by 2
A Deep Learning Framework for the Prediction of Conversion to Alzheimer Disease
Ostellino, S., Benso, A., & Politano, G. M. M. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-07704-3_32 · Cited by 2
Merlin, G., & Pattabiraman, V. (2023) · Advances in computer science research · doi:10.2991/978-94-6463-250-7_18 · Cited by 2
Del envejecimiento a la enfermedad de alzheimer
Noa, J. A. S., Rodríguez, J. J. L., Pérez, S. S., & Romero, J. S. (2011) · DOAJ (DOAJ: Directory of Open Access Journals) · Cited by 2
Leveraging Nonlinear Dynamic Models to Predict Progression of Neuroimaging Biomarkers
Sun, M., Zeng, D., & Wang, Y. (2019) · Biometrics · doi:10.1111/biom.13109 · Cited by 2
“On the other hand, it has long been recognized that certain biomarkers (physiological, biochemical, and anatomical) manifest specific aspects of the disease pathology (Petersen et al ., 2010), so integrating these biomarkers into the diagnostic process is potentially useful.”
BackgroundABCD: Alzheimer’s disease Biomarkers Comprehensive Database
Kumar, A., Bansal, A., & Singh, T. R. (2019) · 3 Biotech · doi:10.1007/s13205-019-1888-0 · Cited by 2
Genetics and Neuroimaging in Schizophrenia
Jacobs, G. R., & Voineskos, A. N. (2020) · Springer eBooks · doi:10.1007/978-3-030-35206-6_16 · Cited by 2
Ko, H., Park, S., Kwak, S., & Ihm, J. (2020) · Journal of Personalized Medicine · doi:10.3390/jpm10040197 · Cited by 2
Seo, H., Brand, L., Wang, H., & Nie, F. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i1.16130 · Cited by 2
Lu, K., Yong, K. X. X., Skorupinska, I., et al. (2021) · Muscle & Nerve · doi:10.1002/mus.27426 · Cited by 2
Exploration of salient risk factors involved in mild cognitive impairment
Samson, A. D., Shen, K., Grady, C. L., McIntosh, A. R., & Initiative, A. D. N. (2022) · European Journal of Neuroscience · doi:10.1111/ejn.15665 · Cited by 3
Bhaskaran, B., & Anandan, K. (2020) · IGI Global eBooks · doi:10.4018/978-1-7998-3441-0.ch030 · Cited by 2
Fountain‐Zaragoza, S., Horn, O., Thorn, K. E., Kraal, A. Z., & Benitez, A. M. (2023) · The Journal of Prevention of Alzheimer s Disease · doi:10.14283/jpad.2023.19 · Cited by 2
Cai, Y., Fu, P., & Zhang, X. (2023) · Neuroscience Letters · doi:10.1016/j.neulet.2023.137161 · Cited by 3
“Further procedures have been reported [24] (http://adni.loni.usc. edu/methods/documents/).”
Kadry, S., Jessy, V. E., Rajinikanth, V., et al. (2023) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-023-04662-1 · Cited by 2
Comparison of the 10-, 14- and 20-Item CES-D Scores as Predictors of Cognitive Decline
Zinkunegi, A. J., Langhough, R. E., Johnson, S. C., Mueller, K. D., & Bruno, D. (2023) · Brain Sciences · doi:10.3390/brainsci13111530 · Cited by 2
“…performance on one or more tests fell below values used in other studies as cut-points for clinical MCI diagnoses (e.g., WMS-R Logical Memory II, [66]: story A score <9: AD Neuroimaging Initiative, [67]); or (3) an abnormal informant report indicating subjective cognitive or functional decline.”
MethodMulti-scale convolutional neural networks for brain disease diagnosis
Hu, Z., Gao, L., Tong, Y., et al. (2023) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm58861.2023.10385297 · Cited by 2
Bibliometric Insights Into the Evolution of Atrial Fibrillation and Dementia Research 2002-2022
Zhao, Y., & Wang, D. (2024) · Medical Science Monitor · doi:10.12659/msm.943239 · Cited by 2
Omics and Network-based Approaches in Understanding HD Pathogenesis
Christodoulou, C. C., & Papanicolaou, E. Z. (2024) · IntechOpen eBooks · doi:10.5772/intechopen.1001983 · Cited by 2
Clinical validity of IntelliSpace Cognition digital assessment platform in mild cognitive impairment
Huijbers, W., van Elswijk, G., Spaltman, M., Cornelis, M., Schmand, B. A., Alnaji, B., Yargeau, M., Harlock, S., Dorn, R. P., Ajtai, B., et al. (2024) · Frontiers in Psychology · doi:10.3389/fpsyg.2024.1451843 · Cited by 2
Padovese, B., Salvadeo, D. H. P., & Pedronette, D. C. G. (2016) · IEEE International Conference on Bioinformatics and Bioengineering (BIBE) · doi:10.1109/bibe.2016.52 · Cited by 2
Xi, J., Ding, D., Zhao, Q., Liang, X., Zheng, L., Guo, Q., Hong, Z., Fu, H., Xu, J., & Xiao, Q. (2020) · Current Alzheimer Research · doi:10.2174/1567205017666200317095608 · Cited by 2
Elman, J. A., Vuoksimaa, E., Franz, C. E., & Kremen, W. S. (2020) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2020.05.015 · Cited by 3
Predicting future cognitive decline with hyperbolic stochastic coding
Zhang, J., Dong, Q., Shi, J., Li, Q., Stonnington, C. M., Gutman, B. A., Chen, K., Reiman, E. M., Caselli, R. J., Thompson, P. M., et al. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102009 · Cited by 3
“To identify patients as early as possible in the course of MCI, ADNI subdivided MCI into “early” and “late” stages based on the Wechsler Memory Scale-Revised (WMS-R) Logical Memory II Story A score ( Petersen et al., 2010 ).”
Mandal, P. K., Sandal, K., Shukla, D., Tripathi, M., Singh, K., & Roy, S. (2020) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2020.571039 · Cited by 2
“IDA-LONI is a hub comprising approximately 138 studies from various disease datasets with new studies added overtime (Petersen et al., 2010).”
BackgroundFunctional Connectome Fingerprint Gradients in Young AdultsPreprint
Tipnis, U., Abbas, K., Tran, E., Amico, E., Shen, L., Kaplan, A. D., & Goñi, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.05212 · Cited by 5
A snapshot of brain and cognition in healthy mid-life and older adultsPreprint
Borne, L., Lupton, M. K., Guo, C. C., Mosley, P. E., Adam, R. J., Ceslis, A., Bourgeat, P., Fazlollahi, A., Maruff, P. T., Rowe, C. C., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.01.20.476706 · Cited by 2
“Participants completed a battery of standardized tests selected to assess multidomain cognitive functions (memory, language, visuospatial, attention, processing speed, social cognition and executive function; Lupton et al., 2021; McKhann et al., 2011; Petersen et al., 2010).”
BackgroundMethodTran-Chi, V.-L., Amrapala, A., Nantachai, G., et al. (2022) · medRxiv · doi:10.1101/2022.06.09.22276176 · Cited by 2
Automated diagnosis of AD using OCT and OCTA: A systematic reviewPreprint
Turkan, Y., & Faik Boray, T. E. K. (2023) · doi:10.22541/au.168412879.91914539/v1 · Cited by 2
Wang, T., Arnold, M., Huynh, K., et al. (2023) · medRxiv · doi:10.1101/2023.06.07.23291081 · Cited by 2
Solving the Pervasive Problem of Protocol Non-Compliance in MRI using an Open-Source tool mrQAPreprint
Sinha, H., & Raamana, P. R. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.07.17.548591 · Cited by 2
Generalizable links between symptoms of borderline personality disorder and functional connectivityPreprint
Shafiei, G., Keller, A. S., Bertolero, M. A., Shanmugan, S., Bassett, D. S., Chen, A. A., Covitz, S. C., Houghton, A., Luo, A. C., Mehta, K. P., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.08.03.551534 · Cited by 2
Yang, Z., Wen, J., Erus, G., Govindarajan, S. T., Melhem, R., Mamourian, E., Cui, Y., Srinivasan, D., Abdulkadir, A., Parmpi, P., et al. (2023) · medRxiv · doi:10.1101/2023.12.29.23300642 · Cited by 2
Self-citationEditorial: Genetics and biomarkers of Alzheimer's disease in Asian populations
Zhou, X., Mok, K. Y., & Fu, A. K. Y. (2024) · Frontiers in Neuroscience · doi:10.3389/fnins.2024.1357783 · Cited by 2
Hanna, E. B., Partarrieu, S., Berenbaum, A., et al. (2024) · Scientific Data · doi:10.1038/s41597-024-03800-4 · Cited by 2
Bio-AD: A Transfer Learning Approach for Diagnosis of Alzheimer’s Diseases in MRI ScansPreprint
Fareed, M. M. S., Ahmed, G., Zikria, S., & Jamal, S. A. (2024) · Preprints.org · doi:10.20944/preprints202408.2010.v1 · Cited by 2
Guarnier, G., Reinelt, J. D., Molloy, E. N., Mihai, P. G., Einaliyan, P., Valk, S. L., Modestino, A. E., Ugolini, M., Mueller, K., Wu, Q., et al. (2024) · medRxiv · doi:10.1101/2024.09.23.24314186 · Cited by 2
Factors associated with age at tau pathology onset and time from tau onset to dementiaPreprint
Heston, M. B., Teague, J. P., Cody, K. A., Deming, Y. K., Chavez, E. R. D., Morse, J., Chin, N. A., Engelman, C. D., Chappell, R. J., Langhough, R. E., et al. (2025) · medRxiv · doi:10.1101/2025.03.11.25323773 · Cited by 2
Porter, T., Armstrong, A. M., Milicic, L., Vacher, M. A., Fernandez, S., O’Brien, E. K., Doré, V., Bourgeat, P., Shishegar, R., Xia, Y., et al. (2025) · Research Square · doi:10.21203/rs.3.rs-7540786/v1 · Cited by 2
Faul, J. D., Crimmins, E. M., Kim, J. K., Thyagarajan, B., King, J. W., Weir, D. R., & Langa, K. M. (2026) · medRxiv · doi:10.64898/2026.01.07.26343604 · Cited by 2
Construction and Application of Large-Scale Image Database in Orthopedic Surgery
Otake, Y., Takao, M., Yokota, F., Fukuda, N., Uemura, K., Sugano, N., & Sato, Y. (2018) · Computer Assisted Orthopaedic Surgery for Hip and Knee · doi:10.1007/978-981-10-5245-3_16 · Cited by 1
Genetic drivers of progression in Alzheimer’s disease are distinct from disease risk
Cohen, C. E., Fernandez, S., Yaman, U., Ehyaei, A. R., Kodosaki, E., Askarova, A., Porter, T., O’Brien, E., Study, A. I. B. A. L., Initiative, A. D. N., et al. (2026) · Alzheimer's Research & Therapy · doi:10.1186/s13195-026-02036-1 · Cited by 1
Neuroimaging and molecular biomarkers of dementia
Polyakova, T., & Араблинский, А. В. (2017) · S S Korsakov Journal of Neurology and Psychiatry · doi:10.17116/jnevro20171176216-22 · Cited by 1
Osteoarthritis and APOE4 Interact to Influence Memory Decline in Non-Demented Older People
Zhou, K., Wu, P., & Initiative, A. D. N. (2022) · Journal of Alzheimer s Disease · doi:10.3233/jad-220138 · Cited by 1
“Recruitment procedures have been reported [10].”
The transfer learning gap: quantifying transfer learning in a medical image case
Camacho, J. G. L., Bento, M., & Frayne, R. (2023) · International Symposium on Medical Information Processing and Analysis (SIPAIM) · doi:10.1117/12.2670071 · Cited by 2
Leach, J. M., Yi, N., Aban, I., et al. (2023) · Journal of Applied Statistics · doi:10.1080/02664763.2023.2258301 · Cited by 1
Wu, J., Zhang, H., Zhu, X., et al. (2023) · doi:10.1117/12.2687618 · Cited by 1
Classification of Alzheimer’s Diseases’ MRI Brain Images Leveraging 3D Convolutional Neural Networks
Quang-Tran, V., Trung-Tin, N., Thuan, B. T. N., Trung-Tin, B., Thanh-Hoan, N., & Ngo, L. T. (2023) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-3-031-44630-6_38 · Cited by 1
Mikhailichenko, N., Lai, T., Wei, J. C., Takahashi, T., Takanari, J., & Goto, K. (2023) · Journal of Pharmacy & Pharmacognosy Research · doi:10.56499/jppres23.1742_12.1.154 · Cited by 2
Early Detection of Alzheimer's Disease using Neuro Imaging and Deep Learning Techniques
Ahamed, A. U. F., Hamdhee, A., Aashiq, M. N. M., Hirshan, R., & Kumara, W. G. C. W. (2023) · SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI) · doi:10.1109/slaai-icai59257.2023.10365034 · Cited by 1
Tagmazian, A. A., Schwarz, C., Lange, C., Pitkänen, E., Vuoksimaa, E., & Initiative, T. A. D. N. (2024) · European Journal of Neuroscience · doi:10.1111/ejn.16332 · Cited by 1
Estimation of the Biological Age of the Human Brain Using Multitask Self-Supervised Learning
Sajjadi, Z., Hamzebeigi, S., & Soryani, M. (2024) · Iranian Conference on Machine Vision and Image Processing · doi:10.1109/mvip62238.2024.10491175 · Cited by 1
Optimizing MRI Preprocessing Techniques for Enhanced Alzheimer’s Disease Detection
Khayari, D. Y. R. A., & Shamsi, H. A. N. A. (2024) · International Journal of Emerging Multidisciplinaries Computer Science & Artificial Intelligence · doi:10.54938/ijemdcsai.2024.03.1.289 · Cited by 1
Li, Q., Fu, Y., Cui, X.-J., Wang, Z.-T., Tan, L., & Initiative, A. D. N. (2024) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2024.1339163 · Cited by 1
Tang, J., Cao, Z., Lei, M., Yu, Q., Mai, Y., Xu, J., Liao, W., Ruan, Y., Shi, L., Yang, L., et al. (2024) · Archives of Gerontology and Geriatrics · doi:10.1016/j.archger.2024.105582 · Cited by 2
“Despite the average yearly conversion rate from MCI to AD being around 10%-20% (Davis et al., 2018; Petersen et al., 2010), not all individuals diagnosed withMCI eventually progress to AD with deteriorative cognition, and some of them remain stable for a long time similar to healthy aging (Koepsell…”
The Association Between Surgery and Mild Cognitive Impairment: Insight from a Case-Control Study
Yu, J., Yu, J., Tang, W., et al. (2024) · Journal of Alzheimer s Disease · doi:10.3233/jad-240467 · Cited by 1
Yang, Z., Su, Y., Rouhi, R., Farazi, M., Zhu, W., Chen, Y., Reiman, E. M., Caselli, R. J., Chen, K., Wang, Y., et al. (2024) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi56570.2024.10635797 · Cited by 1
Gao, Y., Zhang, H., & Hu, Y. (2024) · Journal of Clinical Neuroscience · doi:10.1016/j.jocn.2024.110828 · Cited by 1
Multi-parametric MRI to FMISO PET Synthesis for Hypoxia Prediction in Brain Tumors
Perlo, D., Kanli, G., Boudissa, S., et al. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72744-3_12 · Cited by 1
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