The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index; none of the 23 sources its claims cite is checked for support yet.
Alzheimer's Disease Neuroimaging Initiative (ADNI)
Open accessAbstract
BACKGROUND: Neuroimaging measures and chemical biomarkers may be important indices of clinical progression in normal aging and mild cognitive impairment (MCI) and need to be evaluated longitudinally. OBJECTIVE: To characterize cross-sectionally and longitudinally clinical measures in normal controls, subjects with MCI, and subjects with mild Alzheimer disease (AD) to enable the assessment of the utility of neuroimaging and chemical biomarker measures. METHODS: A total of 819 subjects (229 cognitively normal, 398 with MCI, and 192 with AD) were enrolled at baseline and followed for 12 months using standard cognitive and functional measures typical of clinical trials. RESULTS: The subjects with MCI were more memory impaired than the cognitively normal subjects but not as impaired as the subjects with AD. Nonmemory cognitive measures were only minimally impaired in the subjects with MCI. The subjects with MCI progressed to dementia in 12 months at a rate of 16.5% per year. Approximately 50% of the subjects with MCI were on antidementia therapies. There was minimal movement on the Alzheimer's Disease Assessment Scale-Cognitive Subscale for the normal control subjects, slight movement for the subjects with MCI of 1.1, and a modest change for the subjects with AD of 4.3. Baseline CSF measures of Abeta-42 separated the 3 groups as expected and successfully predicted the 12-month change in cognitive measures. CONCLUSION: The Alzheimer's Disease Neuroimaging Initiative has successfully recruited cohorts of cognitively normal subjects, subjects with mild cognitive impairment (MCI), and subjects with Alzheimer disease with anticipated baseline characteristics. The 12-month progression rate of MCI was as predicted, and the CSF measures heralded progression of clinical measures over 12 months.
Overview
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Written from the paper’s full text.
TL;DR
This ADNI clinical characterization paper reports baseline and 12-month clinical, cognitive, and CSF biomarker data for 819 participants: 229 cognitively normal, 398 with MCI, and 192 with mild AD. It shows expected group differences at baseline, a 16.5% 12-month MCI-to-AD progression rate, and baseline CSF Aβ-42 predicting 12-month cognitive change in normal and MCI groups.
Key findings
- At baseline, ADAS-Cog total scores were 6.2 ± 2.9 in controls, 11.5 ± 4.4 in MCI, and 18.6 ± 6.3 in AD (p < 0.001); MCI subjects were most impaired on memory items and only mildly impaired in nonmemory domains.
- Over 12 months, 64 of 398 MCI subjects progressed to AD, a rate of 16.5% (95% CI 11.9%–20.5%); normal controls progressed to MCI at an estimated 1.4% (95% CI 0%–3.2%).
- ADAS-Cog 12-month change was −0.5 ± 3.0 in controls, 1.1 ± 4.4 in MCI, and 4.3 ± 6.6 in AD (p < 0.001).
- Baseline CSF Aβ-42 decreased across groups (controls 206 ± 5, MCI 164 ± 4, AD 143 ± 4 pg/mL; p < 0.001), and higher baseline Aβ-42 was associated with better ADAS-Cog performance and smaller 12-month change in normal subjects and MCI.
Methods
- Multicenter ADNI cohort of 819 subjects aged 55–90 enrolled at baseline and classified as cognitively normal, MCI, or mild AD using standard clinical, cognitive, and functional criteria; subjects were followed for 12 months with cognitive and functional measures, MRI/PET in subsets, and lumbar puncture in a minimum of 20% per site.
- Baseline group comparisons used nonparametric tests (Kruskal-Wallis) with Hochberg multiple comparisons and exact tests for proportions; 12-month change was summarized as conversion proportion, baseline-to-12-month mean or percent difference, and rate per year from mixed-effects regression models.
- Annual conversion rates were estimated using a hybrid interval-censored method; CSF Aβ-42 was related to diagnosis, ADAS-Cog performance, and 12-month ADAS-Cog change.
Limitations
- The paper states that a 12-month change period is not sufficient to draw conclusions about the likelihood of clinical change, and that the ADNI cohort likely does not represent “typical” community subjects because participants were more highly educated, mostly white, and had a high proportion of APOE4 carriers in the MCI and AD groups.
- The paper also states that the MCI criteria required a stringent degree of memory impairment, so these subjects were likely further along the clinical spectrum and more likely to progress than less-impaired MCI subjects.
- Evident from the text: CSF analyses were limited to subsets (baseline n = 114 controls, 199 MCI, 102 AD), and some subjects lacked 12-month follow-up diagnoses (17 controls and 41 MCI subjects), which limits interpretation of conversion and biomarker results.
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Claims the paper makes
53 Own result · 8 Sources not found · 8 Own interpretation · 5 No basis given · 3 Not checked yet · 3 Own method
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“successfully predicted the 12-month change in cognitive measures.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Abstract
plaincorrelationaln = 819- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“but they looked more like the AD group for change in digit span, category fluency, and trail making”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Concomitant medications.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Nonmemory cognitive measures were only minimally impaired in the subjects with MCI.”
- Also stated in: § RESULTS Baseline characteristics. · hedged
“The neuropsychological battery indicated that, in general, subjects with MCI were more impaired than controls on the memory items and were only mildly impaired in nonmemory domains (table 3).”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Abstract
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Also stated in: § RESULTS Baseline characteristics. · hedged
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The sub-jects with AD were impaired in virtually all of the cognitive areas tested.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > RESULTS Baseline characteristics.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These scores indicated, however, that the subjects with MCI gained most of their error points on memory items.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > RESULTS Baseline characteristics.
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Baseline CSF measures of A-42 separated the 3 groups as expected”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Abstract
plaincorrelationaln = 819- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The next greatest area of impairment for the subjects with MCI resulted from the executive function domain.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > RESULTS Baseline characteristics.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The subjects with MCI progressed to dementia in 12 months at a rate of 16.5% per year.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Abstract
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The subjects with MCI also remained relatively stable as a group, and the subjects with mild AD showed a decline on most of these measures.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > DISCUSSION
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The interaction of A-42 and time also had a significant effect on ADAS-Cog scores in subjects with MCI ( p ϭ 0.00), but not in normal subjects.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Concomitant medications.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- It may state two separate claims as one. 20
- Probably not a substantial claim. 14
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The CDR sum of the boxes essentially did not change in the normal control group (0.1) and increased by approximately 0.7 points in the subjects with MCI and 1.5 points in the subjects with AD ( p Ͻ 0.001).”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Concomitant medications.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The subjects with MCI were more memory impaired than the cognitively normal subjects but not as impaired as the subjects with AD.”
- Also stated in: § RESULTS Baseline characteristics. · hedged
“These scores indicated, however, that the subjects with MCI gained most of their error points on memory items.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Abstract
plainn = 819- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Also stated in: § RESULTS Baseline characteristics. · hedged
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In general, the normal group stayed nearly the same, and the AD group worsened significantly compared to the normal controls.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Concomitant medications.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“and more like the normal controls for the clock test and global CDR.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Concomitant medications.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Overall, subjects with MCI had a mean total Alzheimer's Disease Assessment Scale-Cognitive subscale (ADAS-Cog) score of 11.5, while the normal control subjects had a score of 6.2 and the subjects with mild AD had a score of 18.7 ( p Ͻ 0.001, table 3).”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > RESULTS Baseline characteristics.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“cholinesterase inhibitors-normal controls 0%, subjects with MCI 43.7%, subjects with AD 84.9%”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Concomitant medications.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“A total of 819 subjects were recruited and received a baseline evaluation as part of the ADNI study.”
- Also stated in: § RESULTS Baseline characteristics. · plain
“There were 229 normal control subjects, 398 subjects with MCI, and 192 subjects with mild AD enrolled.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > RESULTS Baseline characteristics.
plainn = 819- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Also stated in: § RESULTS Baseline characteristics. · plain
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The estimated premorbid verbal IQ of these subjects was quite high, at almost 120 for the normal control subjects, 116 for the subjects with MCI, and 114 for the subjects with AD.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > RESULTS Baseline characteristics.
hedgedn = 819- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In all cases except the Hachinski Scale and Geriatric Depression Scale, the mean scores for the 3 groups differed significantly at p Ͻ 0.001, with normal controls performing best, subjects with AD worst, and subjects with MCI in the middle.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > RESULTS Baseline characteristics.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The MCI group was more variable; in most cases they worsened more than the normal group but not as much as the AD group”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Concomitant medications.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Approximately 50% of the subjects with MCI were on antidementia therapies.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Abstract
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Most of the subjects in the study were white, and this was approximately equivalent across the groups.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > RESULTS Baseline characteristics.
plainn = 819- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“There was minimal movement on the Alzheimer's Disease Assessment Scale-Cognitive Subscale for the normal control subjects, slight movement for the subjects with MCI of 1.1, and a modest change for the subjects with AD of 4.3.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Abstract
plainn = 819- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As shown in table 2, the mean age of the 3 groups was equivalent at approximately 75 years.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > RESULTS Baseline characteristics.
plainn = 819- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“there were more men than women in the MCI group (64.6% men vs 35.4% women).”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > RESULTS Baseline characteristics.
plainn = 398- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“There was somewhat less variability on the global measures, while some of the individual neuropsychological tests such as the AVLT showed considerable variability among the subjects despite relatively modest mean group changes.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > DISCUSSION
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“CSF A-42 levels were assessed at baseline and were correlated with clinical diagnoses, performance on the ADAS-Cog, and changes in performance on the ADAS-Cog over 1 year (table 3).”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Concomitant medications.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Delayed recall improved by 5.3% in the normal control group and declined by 4.8% in the subjects with MCI and 7.2% in the subjects with AD.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Concomitant medications.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“As such, the baseline characteristics of the subjects indicated that the 3 groups were generally well educated, intelligent, and mostly white, and perform in a fashion similar to subjects recruited for typical clinical trials.[25][26][27][28]”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > DISCUSSION
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The CSF A-42 data correlated with the diagnostic groups in the expected fashion with levels being lower in the subjects with MCI than in the normal subjects.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > DISCUSSION
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: CSF Aβ-42 level is a valid biomarker of Alzheimer's disease pathology and progression.
None of the sources cited for this claim have been checked yet.
In the paper
“They tend to be more highly educated and the proportion of APOE4 carriers was quite high in the MCI and AD groups but is consistent with subjects recruited for clinical trials.25”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > DISCUSSION
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“On the memory measures such as the AVLT, there was virtually no change across 12 months in learning in the normal control group with a decreased learning of 1.3 items in the subjects with MCI and 3.7 fewer items learned in the AD group.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Concomitant medications.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“An additional 8 subjects with MCI reverted to a normal classification over that interval, and 41 did not have a follow-up diagnosis.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Concomitant medications.
plainn = 398- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“may allow detection of the neurodegenerative process at an earlier point in the spectrum”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Abstract
What the sources say
- Not matchedcites [6]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [7]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [8]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
hedgedconditional- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Not matched
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“All hypothesis tests were 2-sided at level 0.05, and models were validated both graphically and analytically.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Data analysis.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Most investigators believe that Alzheimer disease (AD) is a slowly evolving process that likely begins years to decades before the clinical symptoms are manifest.1”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Abstract
What the sources say
- Not matchedcites [1]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
- Not matched
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“A 12month change period is not a sufficient amount of time to draw conclusions regarding the likelihood of clinical change.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > DISCUSSION
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“has been a target for clinical trials.[2][3][4][5]”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Abstract
What the sources say
- Not matchedcites [2]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [3]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [4]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [5]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The diagnostic conversions over the 12-month period must be viewed with some caution.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > DISCUSSION
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This might be a useful technique to reduce the numbers of subjects used in clinical trials to detect a clinical effect of therapeutic interventions.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > DISCUSSION
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
- Rests on an assumption: CSF Aβ-42 level is a valid biomarker of Alzheimer's disease pathology and progression.
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“Mild cognitive impairment (MCI) represents an attempt to characterize subjects at an early clinical phase”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Abstract
What the sources say
- Not matchedcites [2]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [3]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [4]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [5]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
- Not matched
The paper relies on this without support.
The expected 12-month progression rate for MCI was approximately 16.5% per year.
“The 12-month progression rate of MCI was as predicted”
“The subjects with MCI progressed to dementia in 12 months at a rate of 16.5% per year.”
The paper relies on this without support.
The baseline characteristics observed in the recruited cohorts matched the characteristics anticipated for normal, MCI, and AD cohorts.
“The Alzheimer's DiseaseNeuroimaging Initiative has successfully recruited cohorts of cognitively normal subjects, subjects with mild cognitive impairment (MCI), and subjects with Alzheimer disease with anticipated baseline characteristics.”
The paper relies on this without support.
The MCI subjects have an underlying Alzheimer's disease process that drives clinical progression.
“As such, these subjects were likely further down the clinical spectrum with respect to the underlying AD process such that they would be more likely to progress than subjects with less impairment in the MCI spectrum.”
The paper relies on this without support.
CSF Aβ-42 level is a valid biomarker of Alzheimer's disease pathology and progression.
“The CSF A-42 data correlated with the diagnostic groups in the expected fashion with levels being lower in the subjects with MCI than in the normal subjects.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Another issue is that the vast majority of data from PET and CSF are from selected participants recruited through tertiary care dementia centers [176].”
From NIA‐AA Research Framework: Toward a biological definition of Alzheimer's disease (2018) · cites this paper as [176]
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated major: it would touch a result — a value a result rests on, reported and recomputed p-values on opposite sides of the significance level, two passages that conflict with no sign which is right, or a cited work retracted before this paper was published.
In the paper
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Concomitant medications.
“Among the 398 subjects with MCI at baseline, 64 progressed to AD at 12 months”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Concomitant medications.
“Table 5 Baseline measures on subjects according to progression status at 1 year Assortment variable Nonprogressors, mean ؎ SD (n ؍ 329) Progressors, mean ؎ SD (n ؍ 69)”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > RESULTS Baseline characteristics.
“the subjects with mild AD had a score of 18.7”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > RESULTS Baseline characteristics.
“ADAS-Cog total 6.2 Ϯ 2.9 11.5 Ϯ 4.4 18.1 18.6 Ϯ 6.3 14.0 Ͻ0.001 a, b, c”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Concomitant medications.
“subjects with MCI (r ϭ Ϫ0.22; 95% CI Ϫ0.035 to Ϫ0.08)”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Concomitant medications.
“95% CI Ϫ0.035 to Ϫ0.08”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > RESULTS Baseline characteristics.
“In all cases except the Hachinski Scale and Geriatric Depression Scale, the mean scores for the 3 groups differed significantly at p Ͻ 0.001”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > RESULTS Baseline characteristics.
“GDS Score 0.8 Ϯ 1.1 1.6 Ϯ 1.4 7.3 1.7 Ϯ 1.4 0.6 Ͻ0.001 a, b”
Citations
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Cited by
Sorted by citations
Items 1,601–1,700 of 2,570
Perivascular Space Predicts Brain Hypometabolism of Individuals with Underlying Amyloid Pathology
Shi, X., Initiative, A. D. N., Zhou, N., Sun, B., Wu, Y., Hu, Y., & Ning, Y. (2022) · Journal of Alzheimer s Disease · doi:10.3233/jad-220426 · Cited by 4
“A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how-to-apply/ADNI-Acknowledgement-List.pdf Detailedinformationofenrollmentprotocols for ADNI have been described previously [10].”
Group-shrinkage feature selection with a spatial network for mining DNA methylation data
Tang, X., Mo, Z., Chang, C., & Qian, X. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.106573 · Cited by 4
Wang, M., Chekouo, T., Ismail, Z., et al. (2023) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2023.03.009 · Cited by 4
Duff, K. M., Dixon, A. M., Embree, L. M., & Hoffman, J. M. (2023) · Journal of Clinical and Experimental Neuropsychology · doi:10.1080/13803395.2023.2216920 · Cited by 4
Unsupervised [18F]Flortaucipir cutoffs for tau positivity and staging in Alzheimer’s disease
Quattrini, G., Ferrari, C., Pievani, M., Geviti, A., Ribaldi, F., Scheffler, M., Frisoni, G. B., Garibotto, V., Marizzoni, M., & Initiative, A. D. N. (2023) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-023-06280-7 · Cited by 4
A universal neocortical mask for Centiloid quantification
Bourgeat, P., Doré, V., Rowe, C. C., et al. (2023) · Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring · doi:10.1002/dad2.12457 · Cited by 4
“…Lifestyle (AIBL) study, 14 the Open Access Series of Imaging Studies (OASIS-3), 15 and the Alzheimer’s DiseaseNeuroimagingInitiative(ADNI)database 16 (adni.loni.usc.edu). scans were acquired using one of five tracers: 11 C-PiB, 18 F-florbetapir (FBP), 18 F-florbetaben (FBB), 18 F-flutemetamol…”
MethodSelf-citationA robust harmonization approach for cognitive data from multiple aging and dementia cohorts
Giorgio, J., Tanna, A., Malpetti, M., White, S. R., Wang, J., Baker, S. L., Landau, S. M., Tanaka, T., Chen, C., Rowe, J. B., et al. (2023) · Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring · doi:10.1002/dad2.12453 · Cited by 4
Self-citationWortha, S. M., Schulz, J., Hanna, J., Schwarz, C., Stubbe, B., Frenzel, S., Bülow, R., Friedrich, N., Nauck, M. A., Völzke, H., et al. (2023) · GeroScience · doi:10.1007/s11357-023-00886-3 · Cited by 4
Dynamic Structural Brain Network Construction by Hierarchical Prototype Embedding GCN Using T1-MRI
Leng, Y., Cui, W., Bai, C., Chen, Z., Zheng, Y., & Zheng, J. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43993-3_12 · Cited by 4
LSOR: Longitudinally-Consistent Self-Organized Representation Learning
Ouyang, J., Zhao, Q., Adeli, E., Peng, W., Zaharchuk, G., & Pohl, K. M. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43907-0_27 · Cited by 4
Propagation and Attribution of Uncertainty in Medical Imaging Pipelines
Feiner, L., Menten, M. J., Hammernik, K., Hager, P., Huang, W., Rueckert, D., Braren, R., & Kaissis, G. A. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-44336-7_1 · Cited by 5
Criticality in Alzheimer’s and healthy brains: insights from phase-ordering
Palutla, A., Seth, S., Ashwin, S. S., & Krishnan, M. (2023) · Cognitive Neurodynamics · doi:10.1007/s11571-023-10033-5 · Cited by 4
Edwards, L., Thomas, K. R., Weigand, A. J., Edmonds, E. C., Clark, A. L., Brenner, E. K., Banks, S. J., Gilbert, P. E., Nation, D. A., Delano‐Wood, L., et al. (2024) · Cerebral Circulation - Cognition and Behavior · doi:10.1016/j.cccb.2024.100206 · Cited by 4
“Briefly, among participants without dementia per ADNI criteria [33], raw scores on six neuropsychological tests were converted to age, sex, and education-adjusted z-scores based on normative data from a ‘robust normal ’ control group of participants who remained cognitively normal throughout their…”
MethodZsuffa, J. A., Katz, S., Koszovácz, V., Berente, D. B., Kamondi, A., Csukly, G., Mangialasche, F., Rocha, A. S. L., Kivipelto, M., & Horvath, A. A. (2024) · Scientific Reports · doi:10.1038/s41598-024-52856-0 · Cited by 5
Feng, Y., Chandio, B. Q., Villalón‐Reina, J. E., Benavidez, S. M., Chattopadhyay, T., Chehrzadeh, S., Laltoo, E., Thomopoulos, S. I., Joshi, H., Venkatasubramanian, G., et al. (2024) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc53108.2024.10781681 · Cited by 4
Li, X., Fernandes, B. S., Liu, A., Chen, J., Chen, X., Zhao, Z., & Dai, Y. (2025) · Alzheimer s & Dementia · doi:10.1002/alz.70779 · Cited by 5
Associating Cognition With Amyloid Status Using Partially Ordered Set Analysis
Carr, S. J. A., Jaeger, J., Bian, S., He, P., Maserejian, N. N., Wang, W., Maruff, P. T., EnayetAllah, A. E., Wang, Y., Chen, Z., et al. (2019) · Frontiers in Neurology · doi:10.3389/fneur.2019.00976 · Cited by 4
Kucharczak, F., Suau, M., Strauss, O., Bouallègue, F. B., & Mariano‐Goulart, D. (2020) · Annals of Nuclear Medicine · doi:10.1007/s12149-020-01490-7 · Cited by 4
Prediction of Medical Conditions Using Machine Learning Approaches: Alzheimer’s Case Study
Stoleru, G. I., & Iftene, A. (2022) · Mathematics · doi:10.3390/math10101767 · Cited by 5
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Self-citationBasal forebrain volume reliably predicts the cortical spread of Alzheimer’s degenerationPreprint
Fernández-Cabello, S., Kronbichler, M., Dijk, K. R. A. V., Goodman, J. A., Spreng, R. N., Schmitz, T. W., & Initiative, A. D. N. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/676544 · Cited by 4
“5-1, MMSE 20-26) (Aisen et al., 2010, 2015; Petersen et al., 2010).”
An Isolated Data Island Benchmark Suite for Federated Learning.Preprint
Liang, Y., Guo, Y., Gong, Y., et al. (2020) · arXiv (Cornell University) · Cited by 4
NormVAE: Normative Modeling on Neuroimaging Data using Variational Autoencoders.Preprint
Kumar, S., & Sotiras, A. (2021) · arXiv (Cornell University) · Cited by 4
Xu, J., Hou, Y., Zhou, Y., Hu, M., & Cheng, F. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.10.20.465087 · Cited by 4
“High-throughput DNA/RNA sequencing technologies have rapidly led to a robust body of genetic and genomic data in multiple national AD genome projects, including the Alzheimer’s Disease Sequencing Project (ADSP) [7] and the Alzheimer’s Disease Neuroimaging Initiative (ADNI) [8].”
BackgroundInfluentialNantachai, G., Vasupanrajit, A., Tunvirachaisakul, C., Solmi, M., & Maes, M. H. J. (2021) · medRxiv · doi:10.1101/2021.11.22.21266698 · Cited by 4
Novel genomic loci influence patterns of structural covariance in the human brainPreprint
Wen, J., Nasrallah, I. M., Abdulkadir, A., Satterthwaite, T. D., Yang, Z., Erus, G., Robert‐Fitzgerald, T., Singh, A., Sotiras, A., Boquet-Pujadas, A., et al. (2022) · medRxiv · doi:10.1101/2022.07.20.22277727 · Cited by 4
Huang, J., Wang, Y., Stein, T. D., Ang, T. F. A., Zhu, Y., Tao, Q., Lunetta, K. L., Mez, J. B., Au, R., Farrer, L. A., et al. (2023) · Research Square · doi:10.21203/rs.3.rs-3376348/v1 · Cited by 4
A new AI-assisted data standard accelerates interoperability in biomedical researchPreprint
Long, R. A., Ballard, S. L., Shah, S. I., et al. (2024) · medRxiv · doi:10.1101/2024.10.17.24315618 · Cited by 4
Bron, E. E., Klein, S., Papma, J. M., Jiskoot, L. C., Venkatraghavan, V., Linders, J., Aalten, P., Deyn, P. P. D., Biessels, G. J., Claassen, J. A. H. R., et al. (2021) · NeuroImage Clinical · doi:10.1016/j.nicl.2021.102712 · Cited by 132
Alzheimer’s disease and the mathematical mind
Thompson, T., Vigil, B. Z., & Young, R. S. K. (2024) · Brain Multiphysics · doi:10.1016/j.brain.2024.100094 · Cited by 3
Sato, K., Niimi, Y., Ihara, R., Suzuki, K., Iwata, A., & Iwatsubo, T. (2024) · Journal of Alzheimer's Disease · doi:10.3233/jad-231426 · Cited by 3
Hu, Y., Zhu, T., Yuan, M., Zhu, H., & Zhang, W. (2024) · Cerebral Cortex · doi:10.1093/cercor/bhae423 · Cited by 4
Deng, D., Zhang, L., Feng, H., Chinchilli, V. M., Chen, C., & Wang, M. (2025) · Biometrics · doi:10.1093/biomtc/ujae168 · Cited by 3
A stacking classifier for distinguishing stages of Alzheimer’s disease from a subnetwork perspective
Li, G., Chen, B., Sun, W., et al. (2025) · Cognitive Neurodynamics · doi:10.1007/s11571-025-10221-5 · Cited by 3
Advancing the Reliability of Ultra-Low Field MRI Brain Volume Analysis Using CycleGAN
Hsu, P., Marchetto, E., Sodickson, D. K., et al. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-79103-1_6 · Cited by 3
Gillis, G., Bhalerao, G. V., Blane, J., Mitchell, R., Pretorius, P. M., McCracken, C., Nichols, T. E., Smith, S. M., Miller, K. L., Alfaro‐Almagro, F., et al. (2025) · Human Brain Mapping · doi:10.1002/hbm.70151 · Cited by 3
Ghodhbani, H., Rimer, S., Ouahada, K., & Alimi, A. M. (2025) · IEEE Access · doi:10.1109/access.2025.3552593 · Cited by 4
Aljuhani, M. (2025) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2025.1547185 · Cited by 4
“Although a neuropathological diagnosis is required to confirm the diagnosis of AD, studies have shown high sensitivity and specificity using neuroimaging and neuropsychological assessments for determining disease progression to AD (Petersen et al., 2010; Davatzikos et al., 2001; Fan et al., 2008).”
A survey of early detection and interpretable diagnosis of Alzheimer’s disease
Haddada, K., Khedher, M. I., & Jemai, O. (2025) · Multimedia Tools and Applications · doi:10.1007/s11042-025-20894-z · Cited by 3
Malek‐Ahmadi, M. H., Schack, K., Duff, K. M., et al. (2025) · Alzheimer Disease & Associated Disorders · doi:10.1097/wad.0000000000000672 · Cited by 3
Sreepada, L. P., Brown, C. A., Das, S. R., Yushkevich, P. A., Wolk, D. A., & McMillan, C. T. (2025) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2025.06.003 · Cited by 3
“Accelerated: BAG was at least 0.5 standard deviation (SD) above the mean; Decelerated: BAG was at least 0.5 SD below the mean; Neutral: BAG was within 0.5 SD of the mean.”
InfluentialFu, J., Zheng, Y., Dey, N., et al. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103669 · Cited by 3
“In turn, in the AD cohort, BrLP performed better in most of the regions, as it was trained partially on ADNI, whereas our method was not.”
InfluentialFederated graph neural networks in non-IID scenarios—A comprehensive survey
Shaikh, A. A. S., & Samet, S. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.131007 · Cited by 6
Jann, K., Park, G., Ringman, J. M., & Kim, H. (2025) · Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring · doi:10.1002/dad2.70159 · Cited by 3
Mitochondrial methylcytosines as blood-based biomarkers for Alzheimer’s disease dementia prognosis
Gascón‐Bayarri, J., Mosquera, J. L., Blanch, M., Martí, P., Fontal, B., Trapero, C., Rojo, N., Rico, I., Campdelacreu, J., Fowler, C., et al. (2025) · iScience · doi:10.1016/j.isci.2025.113418 · Cited by 3
Bunyamin, J., Sinclair, B., Wittayacharoenpong, T., et al. (2025) · Epilepsia · doi:10.1111/epi.18614 · Cited by 3
Utility of Harmonisation for Fixel‐Based Metrics in Travelling Subjects and Alzheimer's Disease Data
Zou, R., Kamagata, K., Mito, R., Takabayashi, K., Andica, C., Uchida, W., Guo, S., Kitagawa, T., Fujita, S., Uematsu, A., et al. (2025) · Human Brain Mapping · doi:10.1002/hbm.70408 · Cited by 3
Diffusion Models for Neuroimaging Data Augmentation: Assessing Realism and Clinical Relevance
Mallardi, G., Calefato, F., Lanubile, F., et al. (2025) · Journal of Medical Systems · doi:10.1007/s10916-025-02300-1 · Cited by 3
The role of genomic‐informed risk assessments in predicting dementia outcomes
Andrews, S. J., Tolosa‐Tort, P., Jonson, C., Fulton‐Howard, B., Renton, A. E., Yokoyama, J. S., Yaffe, K. S., & Initiative, A. D. N. (2025) · Alzheimer s & Dementia · doi:10.1002/alz.70826 · Cited by 6
Classification of Alzheimer’s disease using advanced deep learning and ensemble techniques
Desai, V. C., Shetty, S., Sujithra, T., & Manoj, T. (2025) · Soft Computing · doi:10.1007/s00500-025-10953-w · Cited by 5
Nie, Y., Cui, Q., Li, W., Lü, Y., & Yu, W. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.108415 · Cited by 4
Detecting Alzheimer's disease using digital virtual reality cognitive tests
Fang, L., Fan, X., Li, A., Cai, Y., Sun, P., Wei, Z., Chen, H., Yu, K., Lan, G., González‐Ortiz, F., et al. (2025) · Alzheimer s & Dementia · doi:10.1002/alz.71039 · Cited by 3
“27 In the STAR cohort, participants were clinically diagnosed with CU, MCI, and dementia following the criteria of the National Institute on Aging and Alzheimer’s Association.”
Brembati, V., Crescenti, D., Geviti, A., Rossini, E., Cazzaniga, F. A., Moda, F., Zanier, E. R., Guerrera, G., Battistini, L., Baiardi, S., et al. (2026) · Alzheimer s Research & Therapy · doi:10.1186/s13195-025-01897-2 · Cited by 4
Alzheimer's disease polygenic risk in early‐ and late‐onset Alzheimer's disease
Pentchev, J. V., Jackson, T., Khan, N., Rosewood, T. J., Huang, Y.-N., Nho, K. T., Saykin, A. J., Eloyan, A., Taurone, A., Thangarajah, M., et al. (2026) · Alzheimer s & Dementia · doi:10.1002/alz.71066 · Cited by 3
Self-citationSundermann, E. E., Banks, S. J., Bondi, M. W., Martinez, M. A., Biegon, A. R., Rotblatt, L. J., & Hildebrandt, T. (2026) · Biology of Sex Differences · doi:10.1186/s13293-025-00820-6 · Cited by 3
Liu, W.-Z., Xue, M., Sheng, Z.-H., Wei, Y.-Q., Ma, Y., Li, J., Song, J., Chi, S., Ou, Y., & Initiative, A. D. N. (2026) · Journal of Translational Medicine · doi:10.1186/s12967-026-07939-z · Cited by 3
Robust brain age estimation from structural MRI with contrastive learning
Barbano, C. A., Dufumier, B., Duchesnay, É., Grangetto, M., & Gori, P. (2026) · Pattern Recognition Letters · doi:10.1016/j.patrec.2026.02.032 · Cited by 3
Alhasan, A. S., Alhasan, M. S., Milburn, J. M., Ghunaim, H. A., Khalil, M., Almaghraby, A., Alharthi, O. A., Hamoud, S., Essibayi, M. A., Elhassan, Y. H., et al. (2026) · The journal of nutrition health & aging · doi:10.1016/j.jnha.2026.100823 · Cited by 4
“…’ s disease that offers us a valuable opportunity to evaluate and investigate the longitudinal relationship between brain structural imaging modalities, brain metabolic activity from imaging and biomarker data, as well as clinical data with cognitive outcomes across the disease spectrum [10 – 13].”
A Cloud-Aware Scalable Architecture for Distributed Edge-Enabled BCI Biosensor System
Ghosh, S., Bhuvanakantham, R., Sindhujaa, P., Bhuvana, H. P., Mohan, A., Gulyás, B. Z., Máthé, D., & Padmanabhan, P. (2026) · Biosensors · doi:10.3390/bios16030157 · Cited by 3
“…when systems are designed to interoperate with large public datasets and benchmarking ecosystems (e.g., TUH EEG [47] for large-scale clinical EEG, and ADNI [48] for longitudinal neuroimaging and biomarker trajectories), which implicitly demand robust storage, indexing, and access control patterns.”
Neuropathological correlates of age and sex differences in 18F-flortaucipir PET
Coomans, E. M., Smith, R. A., Pawlik, D., Hauer, K. O., Palmqvist, S., Stomrud, E., Mattsson, N., Tremblay, C., Serrano, G. E., Beach, T. G., et al. (2026) · Brain · doi:10.1093/brain/awag136 · Cited by 3
Health system learning enables generalist neuroimaging models
Kondepudi, A., Rao, A. T., Zhao, C., Lyu, Y., Harake, E. S., Banerjee, S., Ogle, J. D., Joshi, R. S., Meißner, A.-K., Hou, X., et al. (2026) · Nature Medicine · doi:10.1038/s41591-026-04497-1 · Cited by 17
Cho, H. E., Choi, S. H., Lim, H. S., et al. (2019) · Archives of Gerontology and Geriatrics · doi:10.1016/j.archger.2019.103993 · Cited by 3
PercolationDF: A percolation-based medical diagnosis framework
Jiang, J., Yu, X., Lin, Y., & Guan, Y. (2022) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2022273 · Cited by 3
Self Assessment Memory Scale (SAMS), a new simple method for evaluating memory function
Kowa, H., Uchimura, M., Ohashi, A., Hiroe, M., & Ono, R. (2022) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2022.1024497 · Cited by 3
“In developing a new tool for estimating memory function, we aimed to develop a method that has a high correlation with the LM II score, which has been widely used (Petersen et al., 2010; Iwatsubo et al., 2018), and can be evaluated in a short time.”
MethodReview of Template-Based Neuroimaging Tools in Neuro-Oncology: Novel Insights
Germann, J., Yang, A. Z., Chow, C. T., Santyr, B. G., Samuel, N., Vetkas, A., Sarica, C., Elias, G. J. B., Voisin, M. R., Kucharczyk, W., et al. (2022) · Onco · doi:10.3390/onco3010001 · Cited by 3
Weitzner, D. S., & Calamia, M. R. (2023) · Journal of Clinical and Experimental Neuropsychology · doi:10.1080/13803395.2023.2214298 · Cited by 4
Does Pre-training on Brain-Related Tasks Results in Better Deep-Learning-Based Brain Age Biomarkers?
Pacheco, B. M., de Oliveira, V. H. R., Antunes, A. B. F., Pedro, S. D. D. S., Silva, D., & Initiative, A. D. N. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-45389-2_13 · Cited by 3
Fan, X., Cai, Y., Zhao, L., Liu, W., Luo, Y., Au, L. W. C., Shi, L., & Mok, V. (2024) · Journal of Alzheimer s Disease · doi:10.3233/jad-230574 · Cited by 4
“…databases ( n = 4,124), including three publicly available databases (the Alzheimer’s Disease Neuroimaging Initiative (ADNI), http://adni-info.org [21]; the Australian Imaging, Biomarker & Lifestyle Flag-ship Study of Ageing (AIBL), http://aibl.csiro.au [22]; the Open Access Series of Imaging…”
Ly, M. T., Adler, J. S., Loy, A. F. T., Edmonds, E. C., Bondi, M. W., & Delano‐Wood, L. (2024) · Journal of the International Neuropsychological Society · doi:10.1017/s135561772301144x · Cited by 4
Associations of tau, Aβ, and brain volume of the Papez circuit with cognition in Alzheimer’s disease
Feng, Y., Laraib, A., Lin, X., Li, Q., Zhan, J., & Li, X. (2024) · European Archives of Psychiatry and Clinical Neuroscience · doi:10.1007/s00406-024-01827-7 · Cited by 3
Frau, L., Jonaitis, E. M., Langhough, R. E., Zuelsdorff, M. L., Okonkwo, O. C., & Bruno, D. (2024) · The Clinical Neuropsychologist · doi:10.1080/13854046.2024.2388904 · Cited by 4
TUMSyn: A Text-Guided Generalist Model for Customized Multimodal MR Image Synthesis
Wang, Y., Xiong, H., Xie, Y., Liu, J., Wang, Q., Liu, Q., & Shen, D. G. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73471-7_13 · Cited by 3
SOM2LM: Self-Organized Multi-Modal Longitudinal Maps
Ouyang, J., Zhao, Q., Adeli, E., Zaharchuk, G., & Pohl, K. M. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72069-7_38 · Cited by 3
Leng, Y., Cui, W., Chen, B., Jiang, X., Peng, Y., & Zheng, J. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.125809 · Cited by 3
Chen, Y.-H., Wang, Z., Liu, X.-P., Mao, Z., & Initiative, A. D. N. (2025) · Journal of Alzheimer s Disease · doi:10.1177/13872877251326286 · Cited by 3
Schaefer, S. Y., Reed, A. S., & Duff, K. M. (2025) · Journal of Alzheimer s Disease · doi:10.1177/13872877251320379 · Cited by 4
Brain morphology mediating the effects of common genetic risk variants on Alzheimer’s disease
Breddels, E. M., Snihirova, Y., Pishva, E., Gülöksüz, S., Blokland, G. A. M., Luykx, J. J., Andreassen, O. A., Linden, D. E. J., van der Meer, D., & Initiative, A. D. N. (2025) · Journal of Alzheimer s Disease Reports · doi:10.1177/25424823251328300 · Cited by 3
Linking genomic and proteomic signatures to brain amyloid burden: insights from GR@ACE/DEGESCO
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Liu, H., Shi, L., Gao, Y., et al. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3555721 · Cited by 3
Luan, Y., Zheng, L., Denecke, J., Dehsarvi, A., Roemer, S. N., Dewenter, A., Steward, A., Shcherbinin, S., Svaldi, D. O., Kotari, V., et al. (2025) · Alzheimer s & Dementia · doi:10.1002/alz.70170 · Cited by 3
Lashkary, M., Bansal, K., Jain, S., Shukla, P. K., Tekchandani, H., & Dhaka, V. S. (2025) · Procedia Computer Science · doi:10.1016/j.procs.2025.04.278 · Cited by 4
Chen, Y., Wang, Z., Liu, X., Mao, Z., & Initiative, A. D. N. (2025) · Molecular Neurobiology · doi:10.1007/s12035-025-05007-z · Cited by 4
Bi, C., Zheng, X. H., Zhang, Y., Zhou, S., Song, J., Shang, H., & Shen, B. (2025) · Alzheimer s & Dementia · doi:10.1002/alz.70282 · Cited by 2
Comprehensive Review of Multimodal Techniques for Neurodegenerative Disease Diagnosis
Alouani, Z., Beriich, M., Saleh, S., Ouhmida, A., Cherradi, B., & Bouattane, O. (2025) · International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET) · doi:10.1109/iraset64571.2025.11008353 · Cited by 3
“As one of the most referenced datasets in neurodegenerative disease research, the Alzheimer’s Disease Neuroimaging Initiative (ADNI) [39] aims to determine the changes which occur in Alzheimer's disease through the longitudinal collection of data.”
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DTI versus NODDI White Matter Microstructural Biomarkers of Alzheimer’s Disease
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BackgroundA Bayesian Disease Progression Model for Clinical Trajectories
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Essential tremor and cognitive decline
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The functional activities questionnaire
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Classification of Alzheimer's Disease using Low Frequency Fluctuation of rs-fMRI Signals
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Strategies for graded motor imagery for clients with phantom limb pain and cognitive impairment
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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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