The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index; none of the 23 sources its claims cite is checked for support yet.
Alzheimer's Disease Neuroimaging Initiative (ADNI)
Open accessAbstract
BACKGROUND: Neuroimaging measures and chemical biomarkers may be important indices of clinical progression in normal aging and mild cognitive impairment (MCI) and need to be evaluated longitudinally. OBJECTIVE: To characterize cross-sectionally and longitudinally clinical measures in normal controls, subjects with MCI, and subjects with mild Alzheimer disease (AD) to enable the assessment of the utility of neuroimaging and chemical biomarker measures. METHODS: A total of 819 subjects (229 cognitively normal, 398 with MCI, and 192 with AD) were enrolled at baseline and followed for 12 months using standard cognitive and functional measures typical of clinical trials. RESULTS: The subjects with MCI were more memory impaired than the cognitively normal subjects but not as impaired as the subjects with AD. Nonmemory cognitive measures were only minimally impaired in the subjects with MCI. The subjects with MCI progressed to dementia in 12 months at a rate of 16.5% per year. Approximately 50% of the subjects with MCI were on antidementia therapies. There was minimal movement on the Alzheimer's Disease Assessment Scale-Cognitive Subscale for the normal control subjects, slight movement for the subjects with MCI of 1.1, and a modest change for the subjects with AD of 4.3. Baseline CSF measures of Abeta-42 separated the 3 groups as expected and successfully predicted the 12-month change in cognitive measures. CONCLUSION: The Alzheimer's Disease Neuroimaging Initiative has successfully recruited cohorts of cognitively normal subjects, subjects with mild cognitive impairment (MCI), and subjects with Alzheimer disease with anticipated baseline characteristics. The 12-month progression rate of MCI was as predicted, and the CSF measures heralded progression of clinical measures over 12 months.
Overview
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Written from the paper’s full text.
TL;DR
This ADNI clinical characterization paper reports baseline and 12-month clinical, cognitive, and CSF biomarker data for 819 participants: 229 cognitively normal, 398 with MCI, and 192 with mild AD. It shows expected group differences at baseline, a 16.5% 12-month MCI-to-AD progression rate, and baseline CSF Aβ-42 predicting 12-month cognitive change in normal and MCI groups.
Key findings
- At baseline, ADAS-Cog total scores were 6.2 ± 2.9 in controls, 11.5 ± 4.4 in MCI, and 18.6 ± 6.3 in AD (p < 0.001); MCI subjects were most impaired on memory items and only mildly impaired in nonmemory domains.
- Over 12 months, 64 of 398 MCI subjects progressed to AD, a rate of 16.5% (95% CI 11.9%–20.5%); normal controls progressed to MCI at an estimated 1.4% (95% CI 0%–3.2%).
- ADAS-Cog 12-month change was −0.5 ± 3.0 in controls, 1.1 ± 4.4 in MCI, and 4.3 ± 6.6 in AD (p < 0.001).
- Baseline CSF Aβ-42 decreased across groups (controls 206 ± 5, MCI 164 ± 4, AD 143 ± 4 pg/mL; p < 0.001), and higher baseline Aβ-42 was associated with better ADAS-Cog performance and smaller 12-month change in normal subjects and MCI.
Methods
- Multicenter ADNI cohort of 819 subjects aged 55–90 enrolled at baseline and classified as cognitively normal, MCI, or mild AD using standard clinical, cognitive, and functional criteria; subjects were followed for 12 months with cognitive and functional measures, MRI/PET in subsets, and lumbar puncture in a minimum of 20% per site.
- Baseline group comparisons used nonparametric tests (Kruskal-Wallis) with Hochberg multiple comparisons and exact tests for proportions; 12-month change was summarized as conversion proportion, baseline-to-12-month mean or percent difference, and rate per year from mixed-effects regression models.
- Annual conversion rates were estimated using a hybrid interval-censored method; CSF Aβ-42 was related to diagnosis, ADAS-Cog performance, and 12-month ADAS-Cog change.
Limitations
- The paper states that a 12-month change period is not sufficient to draw conclusions about the likelihood of clinical change, and that the ADNI cohort likely does not represent “typical” community subjects because participants were more highly educated, mostly white, and had a high proportion of APOE4 carriers in the MCI and AD groups.
- The paper also states that the MCI criteria required a stringent degree of memory impairment, so these subjects were likely further along the clinical spectrum and more likely to progress than less-impaired MCI subjects.
- Evident from the text: CSF analyses were limited to subsets (baseline n = 114 controls, 199 MCI, 102 AD), and some subjects lacked 12-month follow-up diagnoses (17 controls and 41 MCI subjects), which limits interpretation of conversion and biomarker results.
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Claims the paper makes
53 Own result · 8 Sources not found · 8 Own interpretation · 5 No basis given · 3 Not checked yet · 3 Own method
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“successfully predicted the 12-month change in cognitive measures.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Abstract
plaincorrelationaln = 819- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“but they looked more like the AD group for change in digit span, category fluency, and trail making”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Concomitant medications.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Nonmemory cognitive measures were only minimally impaired in the subjects with MCI.”
- Also stated in: § RESULTS Baseline characteristics. · hedged
“The neuropsychological battery indicated that, in general, subjects with MCI were more impaired than controls on the memory items and were only mildly impaired in nonmemory domains (table 3).”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Abstract
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Also stated in: § RESULTS Baseline characteristics. · hedged
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The sub-jects with AD were impaired in virtually all of the cognitive areas tested.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > RESULTS Baseline characteristics.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These scores indicated, however, that the subjects with MCI gained most of their error points on memory items.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > RESULTS Baseline characteristics.
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Baseline CSF measures of A-42 separated the 3 groups as expected”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Abstract
plaincorrelationaln = 819- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The next greatest area of impairment for the subjects with MCI resulted from the executive function domain.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > RESULTS Baseline characteristics.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The subjects with MCI progressed to dementia in 12 months at a rate of 16.5% per year.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Abstract
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The subjects with MCI also remained relatively stable as a group, and the subjects with mild AD showed a decline on most of these measures.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > DISCUSSION
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The interaction of A-42 and time also had a significant effect on ADAS-Cog scores in subjects with MCI ( p ϭ 0.00), but not in normal subjects.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Concomitant medications.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- It may state two separate claims as one. 20
- Probably not a substantial claim. 14
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The CDR sum of the boxes essentially did not change in the normal control group (0.1) and increased by approximately 0.7 points in the subjects with MCI and 1.5 points in the subjects with AD ( p Ͻ 0.001).”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Concomitant medications.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The subjects with MCI were more memory impaired than the cognitively normal subjects but not as impaired as the subjects with AD.”
- Also stated in: § RESULTS Baseline characteristics. · hedged
“These scores indicated, however, that the subjects with MCI gained most of their error points on memory items.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Abstract
plainn = 819- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Also stated in: § RESULTS Baseline characteristics. · hedged
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In general, the normal group stayed nearly the same, and the AD group worsened significantly compared to the normal controls.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Concomitant medications.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“and more like the normal controls for the clock test and global CDR.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Concomitant medications.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Overall, subjects with MCI had a mean total Alzheimer's Disease Assessment Scale-Cognitive subscale (ADAS-Cog) score of 11.5, while the normal control subjects had a score of 6.2 and the subjects with mild AD had a score of 18.7 ( p Ͻ 0.001, table 3).”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > RESULTS Baseline characteristics.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“cholinesterase inhibitors-normal controls 0%, subjects with MCI 43.7%, subjects with AD 84.9%”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Concomitant medications.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“A total of 819 subjects were recruited and received a baseline evaluation as part of the ADNI study.”
- Also stated in: § RESULTS Baseline characteristics. · plain
“There were 229 normal control subjects, 398 subjects with MCI, and 192 subjects with mild AD enrolled.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > RESULTS Baseline characteristics.
plainn = 819- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Also stated in: § RESULTS Baseline characteristics. · plain
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The estimated premorbid verbal IQ of these subjects was quite high, at almost 120 for the normal control subjects, 116 for the subjects with MCI, and 114 for the subjects with AD.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > RESULTS Baseline characteristics.
hedgedn = 819- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In all cases except the Hachinski Scale and Geriatric Depression Scale, the mean scores for the 3 groups differed significantly at p Ͻ 0.001, with normal controls performing best, subjects with AD worst, and subjects with MCI in the middle.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > RESULTS Baseline characteristics.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The MCI group was more variable; in most cases they worsened more than the normal group but not as much as the AD group”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Concomitant medications.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Approximately 50% of the subjects with MCI were on antidementia therapies.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Abstract
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Most of the subjects in the study were white, and this was approximately equivalent across the groups.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > RESULTS Baseline characteristics.
plainn = 819- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“There was minimal movement on the Alzheimer's Disease Assessment Scale-Cognitive Subscale for the normal control subjects, slight movement for the subjects with MCI of 1.1, and a modest change for the subjects with AD of 4.3.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Abstract
plainn = 819- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As shown in table 2, the mean age of the 3 groups was equivalent at approximately 75 years.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > RESULTS Baseline characteristics.
plainn = 819- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“there were more men than women in the MCI group (64.6% men vs 35.4% women).”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > RESULTS Baseline characteristics.
plainn = 398- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“There was somewhat less variability on the global measures, while some of the individual neuropsychological tests such as the AVLT showed considerable variability among the subjects despite relatively modest mean group changes.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > DISCUSSION
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“CSF A-42 levels were assessed at baseline and were correlated with clinical diagnoses, performance on the ADAS-Cog, and changes in performance on the ADAS-Cog over 1 year (table 3).”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Concomitant medications.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Delayed recall improved by 5.3% in the normal control group and declined by 4.8% in the subjects with MCI and 7.2% in the subjects with AD.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Concomitant medications.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“As such, the baseline characteristics of the subjects indicated that the 3 groups were generally well educated, intelligent, and mostly white, and perform in a fashion similar to subjects recruited for typical clinical trials.[25][26][27][28]”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > DISCUSSION
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The CSF A-42 data correlated with the diagnostic groups in the expected fashion with levels being lower in the subjects with MCI than in the normal subjects.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > DISCUSSION
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: CSF Aβ-42 level is a valid biomarker of Alzheimer's disease pathology and progression.
None of the sources cited for this claim have been checked yet.
In the paper
“They tend to be more highly educated and the proportion of APOE4 carriers was quite high in the MCI and AD groups but is consistent with subjects recruited for clinical trials.25”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > DISCUSSION
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“On the memory measures such as the AVLT, there was virtually no change across 12 months in learning in the normal control group with a decreased learning of 1.3 items in the subjects with MCI and 3.7 fewer items learned in the AD group.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Concomitant medications.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“An additional 8 subjects with MCI reverted to a normal classification over that interval, and 41 did not have a follow-up diagnosis.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Concomitant medications.
plainn = 398- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“may allow detection of the neurodegenerative process at an earlier point in the spectrum”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Abstract
What the sources say
- Not matchedcites [6]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [7]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [8]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
hedgedconditional- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Not matched
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“All hypothesis tests were 2-sided at level 0.05, and models were validated both graphically and analytically.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Data analysis.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Most investigators believe that Alzheimer disease (AD) is a slowly evolving process that likely begins years to decades before the clinical symptoms are manifest.1”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Abstract
What the sources say
- Not matchedcites [1]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
- Not matched
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“A 12month change period is not a sufficient amount of time to draw conclusions regarding the likelihood of clinical change.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > DISCUSSION
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“has been a target for clinical trials.[2][3][4][5]”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Abstract
What the sources say
- Not matchedcites [2]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [3]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [4]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [5]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The diagnostic conversions over the 12-month period must be viewed with some caution.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > DISCUSSION
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This might be a useful technique to reduce the numbers of subjects used in clinical trials to detect a clinical effect of therapeutic interventions.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > DISCUSSION
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
- Rests on an assumption: CSF Aβ-42 level is a valid biomarker of Alzheimer's disease pathology and progression.
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“Mild cognitive impairment (MCI) represents an attempt to characterize subjects at an early clinical phase”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Abstract
What the sources say
- Not matchedcites [2]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [3]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [4]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [5]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
- Not matched
The paper relies on this without support.
The expected 12-month progression rate for MCI was approximately 16.5% per year.
“The 12-month progression rate of MCI was as predicted”
“The subjects with MCI progressed to dementia in 12 months at a rate of 16.5% per year.”
The paper relies on this without support.
The baseline characteristics observed in the recruited cohorts matched the characteristics anticipated for normal, MCI, and AD cohorts.
“The Alzheimer's DiseaseNeuroimaging Initiative has successfully recruited cohorts of cognitively normal subjects, subjects with mild cognitive impairment (MCI), and subjects with Alzheimer disease with anticipated baseline characteristics.”
The paper relies on this without support.
The MCI subjects have an underlying Alzheimer's disease process that drives clinical progression.
“As such, these subjects were likely further down the clinical spectrum with respect to the underlying AD process such that they would be more likely to progress than subjects with less impairment in the MCI spectrum.”
The paper relies on this without support.
CSF Aβ-42 level is a valid biomarker of Alzheimer's disease pathology and progression.
“The CSF A-42 data correlated with the diagnostic groups in the expected fashion with levels being lower in the subjects with MCI than in the normal subjects.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Another issue is that the vast majority of data from PET and CSF are from selected participants recruited through tertiary care dementia centers [176].”
From NIA‐AA Research Framework: Toward a biological definition of Alzheimer's disease (2018) · cites this paper as [176]
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated major: it would touch a result — a value a result rests on, reported and recomputed p-values on opposite sides of the significance level, two passages that conflict with no sign which is right, or a cited work retracted before this paper was published.
In the paper
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Concomitant medications.
“Among the 398 subjects with MCI at baseline, 64 progressed to AD at 12 months”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Concomitant medications.
“Table 5 Baseline measures on subjects according to progression status at 1 year Assortment variable Nonprogressors, mean ؎ SD (n ؍ 329) Progressors, mean ؎ SD (n ؍ 69)”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > RESULTS Baseline characteristics.
“the subjects with mild AD had a score of 18.7”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > RESULTS Baseline characteristics.
“ADAS-Cog total 6.2 Ϯ 2.9 11.5 Ϯ 4.4 18.1 18.6 Ϯ 6.3 14.0 Ͻ0.001 a, b, c”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Concomitant medications.
“subjects with MCI (r ϭ Ϫ0.22; 95% CI Ϫ0.035 to Ϫ0.08)”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Concomitant medications.
“95% CI Ϫ0.035 to Ϫ0.08”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > RESULTS Baseline characteristics.
“In all cases except the Hachinski Scale and Geriatric Depression Scale, the mean scores for the 3 groups differed significantly at p Ͻ 0.001”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > RESULTS Baseline characteristics.
“GDS Score 0.8 Ϯ 1.1 1.6 Ϯ 1.4 7.3 1.7 Ϯ 1.4 0.6 Ͻ0.001 a, b”
Citations
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 2,001–2,100 of 2,570
Statistical structural inference from edge weights using a mixture of gamma distributions
Wang, J., & Hancock, E. R. (2023) · Journal of Complex Networks · doi:10.1093/comnet/cnad038 · Cited by 1
Shankar, V. G., & Sisodia, D. S. (2023) · Studies in computational intelligence · doi:10.1007/978-3-031-46341-9_3 · Cited by 1
Bhattacharjee, A., & Roy, P. K. (2024) · Brain Disorders · doi:10.1016/j.dscb.2023.100112 · Cited by 1
“The Alzheimer ’ s Disease Neuroimaging Initiative (ADNI) database was used to obtain structural MRI and DTI data for cognitively normal (CN) and Mild Alzheimer ’ s Disease dementia (AD) subjects used in the present study [15].”
Convolutional Neural Network Based Alzheimer's Disease Detection Using OIASIS Dataset
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Unsupervised Analysis of Alzheimer Disease Medical Data
Ribarska, S., & Georgieva, O. (2023) · International Conference on Computational Intelligence and Intelligent Systems (CIIS) · doi:10.1145/3638209.3638238 · Cited by 1
“Oneof these large and long-term projects for medical data accumulation is Alzheimer’s Disease Neuroimaging Initiative (ADNI) database [1].”
BackgroundMethodYang, M., Wang, M., Zhao, X., Xu, F., Liang, S., Wang, Y., Wang, N., Sambou, M. L., Jiang, Y., & Dai, J. (2024) · Epigenomics · doi:10.2217/epi-2023-0343 · Cited by 1
Loizillon, S., Mabille, S., Bottani, S., et al. (2024) · SPIE Medical Imaging: Image Processing · doi:10.1117/12.3005781 · Cited by 1
Unsupervised Anomaly Detection for Mild Cognitive Impairment Using Diffusion Model
Lei, Y., Nieuwoudt, M., Matsumoto, H., Zhong, N., Yin, P., & Wang, S. (2023) · Asia Conference on Cognitive Engineering and Intelligent Interaction (CEII) · doi:10.1109/ceii60565.2023.00016 · Cited by 1
“To achieve the early detection of abnormalities in the onset of AD, we employ Florbetapir PET imaging data from the ADNI dataset [39].”
MethodWang, J., Blair, E. M., Forman, J. H., et al. (2024) · Journal of Applied Gerontology · doi:10.1177/07334648241253465 · Cited by 1
Yang, X., Wang, Z., Li, H., Qin, W., Liu, N., Liu, Z., Wang, S., Xu, J., Wang, J., & Initiative, A. D. N. (2024) · Advanced Science · doi:10.1002/advs.202309889
Machine learning and neuroimaging: Understanding the human brain in health and disease
Gu, Z., Jamison, K. W., Sabuncu, M. R., & Kuceyeski, A. F. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-13480-7.00010-7 · Cited by 1
Dubbelman, M. A., Vromen, E. M., Tijms, B. M., Berkhof, J. H., Ottenhoff, L., Vijverberg, E. G. B., Prins, N. D., van der Flier, W. M., & Sikkes, S. A. M. (2024) · Alzheimer s & Dementia Translational Research & Clinical Interventions · doi:10.1002/trc2.12485 · Cited by 1
Han, T., Peng, Y., Du, Y., Li, Y., Wang, Y., Sun, W., Cui, L., & Peng, Q. (2024) · Frontiers in Neuroscience · doi:10.3389/fnins.2024.1388391 · Cited by 1
Convergence of Accelerated Brain Volume Decline in Normal Aging and Alzheimer’s Disease Pathology
Avelar‐Pereira, B., Phillips, C. M., Hosseini, S. M. H., & Initiative, A. D. N. (2024) · Journal of Alzheimer s Disease · doi:10.3233/jad-231458 · Cited by 1
Robotic frameless brain biopsy system enhanced by facial mesh registration
Aydoğmuş, Ö., & Talu, M. F. (2024) · Expert Systems · doi:10.1111/exsy.13739 · Cited by 1
Gómez-Valadés, A., Martínez‐Tomás, R., García‐Herranz, S., et al. (2024) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2024.1378281 · Cited by 1
Paschali, M., Jiang, Y. Y., Siegel, S., González, C., Pohl, K. M., Chaudhari, A. S., & Zhao, Q. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-74561-4_3 · Cited by 1
Automated detection of motion artifacts in brain MR images using deep learning
Jimeno, M. M., Ravi, K. S., Fung, M., Oyekunle, D., Ogbole, G. I., Vaughan, J. T., & Geethanath, S. (2024) · NMR in Biomedicine · doi:10.1002/nbm.5276 · Cited by 4
Mohamed, A. A., Saleh, Y. N. M., & Abdel-Hamid, A. (2024) · IEEE Access · doi:10.1109/access.2024.3502909 · Cited by 1
Effects of Target Images on Outputs of Style-Transfer Generative Models for Brain MRI Harmonization
Narula, S., Gadewar, S. P., Haddad, E., et al. (2024) · International Symposium on Medical Information Processing and Analysis (SIPAIM) · doi:10.1109/sipaim62974.2024.10783646 · Cited by 1
Wang, J.-R., Debora, A., Chen, L., Chen, H., Zhao, X.-M., Yu, M.-Y., & Yang, Y.-J. (2025) · Journal of Alzheimer's Disease · doi:10.1177/13872877241305800 · Cited by 1
Lin, J., Wu, D., & Huang, L. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.107514 · Cited by 1
Gayathri, L., Muralidhara, B. L., & Rajesh, B. (2025) · International Journal of Computational and Experimental Science and Engineering · doi:10.22399/ijcesen.1077 · Cited by 1
Biomarkers of Neurotoxicity and Disease
Slikker, W., Liachenko, S. M., Zhang, X., Wáng, C., Liu, F., Xu, J., & Wu, L. (2025) · Elsevier eBooks · doi:10.1016/b978-0-323-95488-4.00233-3 · Cited by 1
El-Geneedy, M., Akram, S., & Gamel, S. A. (2024) · International Conference on Future Telecommunications and Artificial Intelligence (IC-FTAI) · doi:10.1109/ic-ftai62324.2024.10950034 · Cited by 1
Petersen, K. K., Nallapu, B. T., Lipton, R. B., Grober, E. H., Davatzikos, C. A., Harvey, D. J., Nasrallah, I. M., Ezzati, A., Initiative, A. D. N., Petersen, K. K., et al. (2025) · The Journals of Gerontology Series B · doi:10.1093/geronb/gbaf085 · Cited by 1
Self-citationZhang, C., Li, B., Ng, K. P., Huang, G., Wang, X., Kong, M., & Ba, M. (2025) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2025.1547394 · Cited by 1
Brain MRI signatures across sex and CSF Alzheimer’s disease biomarkers
Cheng, Y., He, Y., Gopinath, K. K., Billot, B., Iglesias, J. E., Wu, C., Dodge, H. H., Wills, A.-M. A., Carlyle, B. C., Kivisäkk, P., et al. (2025) · Brain Communications · doi:10.1093/braincomms/fcaf210 · Cited by 1
Saleh, H., McCann, M., Breslin, J. G., & El–Sappagh, S. (2025) · Journal of Advances in Information Technology · doi:10.12720/jait.16.6.854-868 · Cited by 1
Sato, K., Niimi, Y., Ihara, R., Iwata, A., Suzuki, K., Iwatsubo, T., & Initiative, A. D. N. (2025) · Journal of Alzheimer s Disease · doi:10.1177/13872877251350718 · Cited by 1
Wen, Z., Ghafouri, A., Biros, G., & (ADNI), T. A. D. N. I. (2025) · Brain Informatics · doi:10.1186/s40708-025-00264-z · Cited by 1
Ocen, S., Muchemi, L., & Yohannis, M. A. (2025) · Advances in Alzheimer s Disease · doi:10.4236/aad.2025.143004 · Cited by 1
Lyu, Y., Zhang, J., Zhang, L., Ruan, W., Liu, T., Zhu, D., & Initiative, A. D. N. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-04965-0_33 · Cited by 1
Region-wise stacking ensembles for estimating brain-age using structural MRI
Antonopoulos, G., More, S., Eickhoff, S. B., Raimondo, F., & Patil, K. R. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.111182 · Cited by 1
Federated Learning for Predicting Mild Cognitive Impairment to Dementia Conversion
Sharma, G., Moradi, E., Pajula, J., Hilvo, M., & Tohka, J. (2025) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc58623.2025.11254632 · Cited by 1
Orlichenko, A., Ding, S., Johns, E., Gu, Z., Tian, X., Li, X., & Zhao, Y. (2026) · Imaging Neuroscience · doi:10.1162/imag.a.1408 · Cited by 1
Pre-clinical Diagnosis of Neurodegenerative Diseases
Oizumi, H., & Takeda, A. (2015) · Nihon Naika Gakkai Zasshi · doi:10.2169/naika.104.323 · Cited by 1
Computer-Based Assessment of Alzheimer’s Disease Employing fMRI and/or EEG: A Comprehensive Review
Tripoliti, E. E., Zervakis, M. E., & Fotiadis, D. I. (2014) · Neuromethods · doi:10.1007/7657_2014_70 · Cited by 1
Redox Based-Peripheral Biomarkers in Alzheimer’s Disease: Challenges, Limits and Prospects
Perrotte, M., Pincemail, J., Haddad, M., & Ramassamy, C. (2016) · Oxidative stress in applied basic research and clinical practice · doi:10.1007/978-3-319-33486-8_19 · Cited by 1
Quantum Edge Entropy for Alzheimer’s Disease Analysis
Wang, J., Wilson, R. C., & Hancock, E. R. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-97785-0_43 · Cited by 1
Network Models in Neuroimaging: A Survey of Multimodal Applications
Mancini, M., & Cercignani, M. (2018) · Fundamenta Informaticae · doi:10.3233/fi-2018-1732 · Cited by 2
Demenzfrüherkennung: nur zerstreut oder schon krank?
Dodel, R. C., & Deuschl, G. (2020) · MMW - Fortschritte der Medizin · doi:10.1007/s15006-020-0011-0 · Cited by 1
Texture and signal features from hippocampal T2 maps as biomarkers for MCI to AD progression
Trejo-Castro, A. I., Caballero-Luna, R. A., Garnica-López, J. A., et al. (2020) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm49941.2020.9313538 · Cited by 1
Optimizing MRI Registration using Software/Hardware Co-Design Model on FPGA
Farouk, Y., & Rady, S. (2020) · International Journal of Innovative Technology and Exploring Engineering · doi:10.35940/ijitee.b8300.1210220 · Cited by 1
Mining relations between neuropsychological data to characterize Alzheimer’s disease
Pabón, G. A., Giraldo, D. L., & Castro, E. R. (2021) · International Symposium on Medical Information Processing and Analysis (SIPAIM) · doi:10.1117/12.2606298 · Cited by 1
ATRI and ACTC: Academic Programs to Accelerate Alzheimer’s Disease Drug Development
Aisen, P. S. S., Raman, R., Rafii, M. S., Sperling, R. A., & Petersen, R. C. (2022) · Cambridge University Press eBooks · doi:10.1017/9781108975759.016 · Cited by 1
Self-citationYamamoto, Y., Hirano, J., Ueda, R., Yoshitake, H., Yamagishi, M., Kimura, M., Kamiya, K., Shino, M., Mimura, M., & Yamagata, B. (2022) · Psychiatry and Clinical Neurosciences Reports · doi:10.1002/pcn5.45 · Cited by 2
Fast Feature Extraction from Large Scale Connectome Data Sets Using Zero Crossing Counts Over Graphs
Petrantonakis, P. C., & Kompatsiaris, I. Y. (2022) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco55093.2022.9909570 · Cited by 1
Analyzing Brain Atrophy in Dementia Using Computer Vision
Chaudhary, N., & Verma, S. S. (2022) · International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) · doi:10.1109/icrito56286.2022.9964994 · Cited by 1
Biomarkers and Alzheimer spectrum
Takeda, M. (2011) · Psychiatry and Clinical Neurosciences · doi:10.1111/j.1440-1819.2011.02197.x · Cited by 1
Reinforcement Learning in Individuals at Risk for Alzheimer's Disease
Figueroa, C. M. (2013) · e-Publications@Marquette (Marquette University) · Cited by 1
Lippa, C. F., & Possin, K. L. (2016) · Wiley eBooks · doi:10.1002/9781118797662.ch6 · Cited by 1
Gene Set Analysis of SNP Data from Genome‐wide Association Studies
Liu, S., Zeng, P., & Liu, Z. (2017) · doi:10.1002/9781118782392.ch24 · Cited by 1
A blood-based signature of cerebrospinal fluid A β 1–42 statusPreprint
Goudey, B., Fung, B. J., Schieber, C., Initiative, A. D. N., & Faux, N. G. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/190207 · Cited by 1
TIMELINE OF BRAIN ALTERATIONS IN ALZHEIMER’S DISEASE ACROSS THE ENTIRE LIFESPANPreprint
Coupé, P., Manjón, J. V., Lanuza, E., Catheline, G., & Initiative, A. D. N. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/229195 · Cited by 1
Modelling the Neuroanatomical Progression of Alzheimer's Disease and Posterior Cortical AtrophyPreprint
Marinescu, R. V. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.04805 · Cited by 1
“The general ADNI inclusion criteria has been described in [262].”
Deep Recurrent Disease Progression Model for Conversion-Time Prediction of Alzheimer's Disease.Preprint
Jung, W., Jun, E., & Suk, H. (2020) · arXiv (Cornell University) · Cited by 1
FMRI Functional Connectivity Evaluation in Alzheimer’s Stages: Linear and Non-Linear ApproachesPreprint
Ahmadi, H., Fatemizadeh, E., & Nasrabadi, A. M. (2021) · Research Square · doi:10.21203/rs.3.rs-189491/v1 · Cited by 1
Overgaard, S. M. (2020) · University of Minnesota Digital Conservancy (University of Minnesota) · Cited by 1
Liu, S., Masurkar, A. V., Rusinek, H., Chen, J., Zhang, B., Zhu, W., Fernandez‐Granda, C., Razavian, N., & Initiative, A. D. N. (2021) · medRxiv · doi:10.1101/2021.05.28.21257318 · Cited by 1
Potential utility of plasma p-tau and NfL as surrogate biomarkers for preventive clinical trialsPreprint
Ferreira, P. C. L., Ferrari‐Souza, J. P., Tissot, C., Bellaver, B., Leffa, D. T., Lussier, F. Z., Povala, G., Therriault, J., Benedet, A. L., Ashton, N. J., et al. (2022) · medRxiv · doi:10.1101/2022.08.17.22278853 · Cited by 1
Tau-PET abnormality change along with Alzheimer’s disease: a topological perspectivePreprint
Ding, J., Shen, C., Wang, Z., Yang, Y., Fakhri, G. E., Lu, J., Liang, D., Zheng, H., Zhou, Y., & Sun, T. (2023) · Research Square · doi:10.21203/rs.3.rs-2756518/v1 · Cited by 1
How is sleep-disordered breathing linked with biomarkers of Alzheimer’s disease?Preprint
Akradi, M., Farzane-Daghigh, T., Ebneabbasi, A., Bi, H., Drzezga, A. E., Mander, B. A., Eickhoff, S. B., Tahmasian, M., & Initiative, T. A. D. N. (2023) · medRxiv · doi:10.1101/2023.08.16.23294054 · Cited by 1
Tremblay, C., Rahayel, S., Pastor‐Bernier, A., St‐Onge, F., Vo, A., Rheault, F., Daneault, V., Morys, F., Rajah, M. N., Villeneuve, S., et al. (2024) · medRxiv · doi:10.1101/2024.03.01.24303606 · Cited by 1
Connecting genomic and proteomic signatures of amyloid burden in the brainPreprint
Puerta, R., de Rojas, I., García‐González, P., Olivé, C., Sotolongo‐Grau, Ó., García‐Sánchez, A., García‐Gutiérrez, F., Montrreal, L., Tartari, J. P., Sanabria, Á., et al. (2024) · medRxiv · doi:10.1101/2024.09.06.24313124 · Cited by 1
Sanderson‐Cimino, M. E., Possin, K. L., Mungas, D., Paolillo, E. W., Asken, B. M., Tsoy, E., Jarrott, S., Cobigo, Y., Saloner, R., Casaletto, K. B., et al. (2024) · medRxiv · doi:10.1101/2024.10.23.24315997 · Cited by 1
Venkataraman, A. V., Wright, P. K., Jewell, A., Webb, C., Zhang, Y., Patil, S. K., Cardoso, M. J., Cole, J. H., Kempton, M. J., Marshall, C. R., et al. (2025) · medRxiv · doi:10.1101/2025.01.07.25319950 · Cited by 1
Wilson, D., Copeland, K. A. F., Vasko, A.-J., Hesterberg, L. K., Khare, M., Wolfe, M., Sheehy, P., Verberk, I. M. W., Miller, M., & Teunissen, C. E. (2025) · medRxiv · doi:10.1101/2025.03.31.25324984 · Cited by 1
Kinreich, S., Bingly, A., & Pandey, G. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.05.09.653177 · Cited by 1
“Details of the ADNI design, participant recruitment, clinical testing, and additional methods have been published previously 22,27 and are available at www.adni-info.org.”
DICOM datasets for reproducible neuroimaging research across manufacturers and software versions
Rorden, C., Béranger, B., Cheng, H., et al. (2025) · Scientific Data · doi:10.1038/s41597-025-05503-w · Cited by 1
Brown, C., Cousins, K. A. Q., Korecka, M., McGrew, E., Chen‐Plotkin, A. S., Detre, J. A., McMillan, C. T., Lee, E. B., Das, S. R., Mechanic-Hamilton, D. J., et al. (2025) · medRxiv · doi:10.1101/2025.09.04.25334935 · Cited by 1
Self-citationAdvancing Human Population Genomics with DNA Foundation ModelsPreprint
Zhu, R., Zhou, X., de Aquino, M. M., Engchuan, W., Zhou, H., Cheng, H., Initiative, A. D. N., Hardy, J., Tang, H., Scherer, S. W., et al. (2025) · medRxiv · doi:10.1101/2025.09.09.25335438 · Cited by 1
“Now, with the advent of large biobanks and disease-specific cohorts (e.g., ADNI ( 23 )), alongside Human Pangenome Project ( 24 ) delivering an unprecedented, ancestry-spanning atlas of genetic variation, there is opportunity to deploy sequence-based DNA foundation models on population genomic data.”
Azaraein, M. H., Zarrazvand, E., Heydari, F., Asimi, N., Ramezannezhad, E., Rahimipour, D., Janeshin, K., Mahdian, A., Sichani, M. F., Farhadi, S., et al. (2025) · Research Square · doi:10.21203/rs.3.rs-7327653/v1 · Cited by 1
Yakoub, Y., Qiu, T., Peyrot, C., Salvadó, G., Villeneuve, S., Binette, A. P., & Initiative, A. D. N. (2025) · medRxiv · doi:10.1101/2025.09.30.25337003 · Cited by 1
Andrews, D., Golchi, S., Collins, D. L., & Initiative, T. A. D. N. (2025) · medRxiv · doi:10.1101/2025.10.28.25338899 · Cited by 1
Marawi, T., Rai, H., Kumar, R., Vandeloo, K. L., Yep, R., Alexander, M. W., Boshmaf, S. Z., Chen, S., Gopinath, G., Malhotra, S., et al. (2025) · medRxiv · doi:10.1101/2025.10.31.25338992 · Cited by 1
Ellis, R. J., Airaud, A., Jasodanand, V. H., Kowshik, S. S., Bellitti, M., Kolachalama, V. B., Estiri, H., Glymour, M. M., Dufouil, C., Sperling, R. A., et al. (2025) · medRxiv · doi:10.1101/2025.11.19.25340533 · Cited by 1
Tran-Chi, V.-L., Maes, M., Nantachai, G., Hemrungrojn, S., Solmi, M., & Tunvirachaisakul, C. (2023) · medRxiv · doi:10.1101/2023.11.03.23297890 · Cited by 1
Ambroise, C. (2024) · theses.fr (ABES) · doi:10.70675/d2529d35z93e6z4d60zb2c5z752d5f0edda3 · Cited by 1
Kim, Y., Heo, W., Park, S. J., Kim, Y., Cho, Y. E., Lee, Y.-W., & Kim, J. (2026) · Research Square · doi:10.21203/rs.3.rs-9499795/v2 · Cited by 2
Chakraborty, P. (2026) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.70501 · Cited by 1
Role of Cellular Prion Protein in the Amyloid-β Oligomer Pathophysiology of Alzheimer’s Disease
Kaufman, A. C., & Strittmatter, S. M. (2012) · Springer eBooks · doi:10.1007/978-1-4614-5305-5_3
The Role of Alzheimer Societies in the United States
O'Hara, R. M., & Powers, B. N. (2010) · doi:10.1002/9780470669600.ch67
ROLE OF GENOMIC COPY NUMBER VARIATION IN ALZHEIMER'S DISEASE AND MILD COGNITIVE IMPAIRMENT
Swaminathan, S. (2013) · IUScholarWorks (Indiana University) · doi:10.7912/c2/1949
Mitochondrial DNA, anti-tuberculosis drugs-induced hepatotoxicity and Alzheimer's disease
Schuck, P. F. (2014) · Anais da Academia Brasileira de Ciências · doi:10.1590/0001-37652014862
Power Calculations for Two-Wave, Change from Baseline to Follow-Up Study Designs
Edland (2012) · International Journal of Statistics in Medical Research · doi:10.6000/1929-6029.2012.01.01.03
Welcher Marker ist am präzisesten?
Yakushev, I. Y. (2012) · InFo Neurologie + Psychiatrie · doi:10.1007/s15005-012-0459-2
Modeling the Early Identification and Intervention of Alzheimer’s Disease
Michaud, T. L. (2015) · University of Minnesota Digital Conservancy (University of Minnesota)
Nudelman, K. N. H. (2015) · IUScholarWorks (Indiana University) · doi:10.7912/c2/1966
Interview with Dr Ronald Petersen, 7 February 2013
Katz, S., & Peters, K. R. (2014) · Dementia · doi:10.1177/1471301214562143
Longitudinal Effects of Cognitive Reserve and Vascular Risks in Aging and Dementia
Lo, Y. Y. (2012) · eScholarship (California Digital Library)
Thomas, R. G., Albert, M., Petersen, R. C., & Aisen, P. S. S. (2016)
Self-citationPredicting Cognitive Decline in Patients with TIA and Minor Stroke
Munir, M. A. (2016) · Libraries and Cultural Resources (University of Calgary) · doi:10.11575/prism/27401
Memory decline and Aβ amyloid as markers of neurodegeneration in preclinical Alzheimer's disease
Lim, Y. Y. (2013) · Minerva Access (University of Melbourne)
Diagnosis of Alzheimer’s disease
Rosa‐Neto, P., Rowley, J., Leuzy, A., Mohades, S., Shin, M., Dauar, M. T., & Gauthier, S. G. (2012) · doi:10.2217/ebo.12.66
Cognition and Motor Function: A Novel Outcome Measure for Studies on Pre-Dementia Syndromes
Kueper, J. K. (2017) · Scholarship@Western (Western University)
The article considers algorithms and methods for the classification of neurodegenerative diseases, I. P. A. D. A. D. S. A. C. F. C. O. S. S., Vasilchuk, O., & Fedorov, A. (2017) · International Conference on Information Technology and Nanotechnology (ITNT) · doi:10.18287/1613-0073-2017-1904-300-305
Difference of Alzheimer's disease sub-groups using two features from intensity size zone matrix
Lee, S.-H., & Park, H. (2017) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2017.8037493
“The groups were divided according to Mini-Mental State Examination (MMSE) score, Clinical Dementia Rating (CDR) score, and ADNI criteria [10].”
MethodWeiner, M. W., Veitch, D. P., Hayes, J., Neylan, T. C., Grafman, J. H., Aisen, P. S. S., Petersen, R. C., Jack, C. R., Jagust, W., Trojanowski, J. Q., et al. (2014)
Self-citationLee, S.-H., Son, S.-J., Kim, M., & Park, H. (2017) · IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) · doi:10.1109/nssmic.2017.8533138
“[4] R. C. Petersen, P. S. Aisen, and L. A. Beckett, “Alzheimer ’ s Disease Neuroimaging Initiative ( ADNI ) Clinical characterization,” 2013.”
MethodInfluential
Metadata
- Authors
- Petersen, R. C., Aisen, P. S., Beckett, L. A., Donohue, M. C., Gamst, A. C., Harvey, D. J., Jack, C. R., Jagust, W. J., Shaw, L. M., Toga, A. W., Trojanowski, J. Q., & Weiner, M. W.
- Affiliations
- Mayo Clinic (US)
- ORCID
- Venue
- Neurology
- Published
- 2010-01-19
- Volume
- 74
- Issue
- 3
- Pages
- 201-209
- ISSN
- 0028-3878 (ISSN-L)
- 1526-632X
- PMC
- PMC2809036
- OpenAlex
- W2122328291
- Cited by
- 2,572
- References
- 27
- Citations by year
- 2026: 312 · 2025: 391 · 2024: 327 · 2023: 254 · 2022: 233 · 2021: 209 · 2020: 182 · 2019: 121 · 2018: 85 · 2017: 67 · 2016: 71 · 2015: 66 · 2014: 45 · 2013: 63 · 2012: 59
- FWCI
- 26.56
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Neurology (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (submittedVersion)
- PubMed Central (submittedVersion)
- Funders
- Alzheimer's Disease Neuroimaging Initiative (AG024904)
- National Institutes of Health (5r01ag027984-03, 5r01ag030048-05, n01mh22005-16-0-1, 5r01dk075128-05, 5u01ag006786-38, 5r01mh083552-05, 5r01ca104573-05, 5p30ag010124-30, 5p01ag009215-20, 5r01ag011378-25, 5p41rr023953-04, 5u01ag024904-10, 5u01ag029213-05, 5r01ag016381-08, 5r01eb006266-04, 5r01ag010897-27, AG024904, 5p01ag019724-23, 5p30ca093373-23, 5p50ag016574-20)
- National Institute on Aging (AG027984, U01-AG-024904, R01AG11378, P30AG010129, U19 AG010483, U01‐AG10483, R01-AG030048, P50‐AG16574, P01 & AG019724, U01AG06786, P50-AG23501, RC1AG035427, AG 010483, R01AG031224, AG031224, P01 AG 17586-10, 1 U01 AG024904, AG 024904, UO1AG029213-01, P30AG036468, 2 P50 AG016570, 1 U01 AG 024904-05, P30 AG 10124-18, R01 AG031252, P01AG017586, U01AG010483, 1 U01 AG24904, 1P01 AG-19724-07, R01AG027859, P50AG023501, AG027859, P01 AG012435, AG-11378, 2P50AG005133, RC2AG036535, P01-AG19724, P01 AG 09215-20, R01-AG 10897, 2P30AG10129, R01 AG029672, R01 AG16381)
- National Heart, Lung, and Blood Institute (HL-095089, R01HL095089)
- National Institute of Mental Health (U01MH083506, MH 22005, MH062512, MH083506, R01MH079752, R01MH069433, MH083552, MH079752, R01 MH069259, 5 R01 MH071940, MH069433, R01MH072641, P30MH062512)
- National Institute on Drug Abuse (1 R01 DA017830)
- National Cancer Institute (2 P30 CA93373, CA104573)
- National Institute of Diabetes and Digestive and Kidney Diseases (DK075128)
- National Institute of Neurological Disorders and Stroke (1 R01 NS050792, RL1NS062412, P50NS053488, 1 R01 NS049194, RC2NS069368, R01NS031966)
- National Institute of Biomedical Imaging and Bioengineering (1 U54 EB05149, 1 R01 EB 006266-01, U54EB005149, P01EB001955, 5R01EB002138-07)
- U.S. National Library of Medicine (2 R01LM005639)
- National Center for Research Resources (5 P41 RR013642, P41 RR-023953, U24RR021760, U54RR021813, R24 RR021382, R24 RR021992, KL2RR024144)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01HD050735, 1R01 HD053893-01)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Alzheimer's disease research and treatments
- Health Sciences › Medicine › Oncology › Cancer-related cognitive impairment studies
- Keywords
- Alzheimer's disease, mild cognitive impairment, neuroimaging biomarkers, cerebrospinal fluid biomarkers, amyloid-beta 42, longitudinal cognitive change, normal aging, memory impairment, ADAS-Cog
- MeSH terms
- Aged, Aged, 80 and over, Alzheimer Disease, Cross-Sectional Studies, Diagnostic Imaging, Female, Follow-Up Studies, Humans, Longitudinal Studies, Male, Middle Aged, Biomarkers
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-18, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-18 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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