The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index; none of the 23 sources its claims cite is checked for support yet.
Alzheimer's Disease Neuroimaging Initiative (ADNI)
Open accessAbstract
BACKGROUND: Neuroimaging measures and chemical biomarkers may be important indices of clinical progression in normal aging and mild cognitive impairment (MCI) and need to be evaluated longitudinally. OBJECTIVE: To characterize cross-sectionally and longitudinally clinical measures in normal controls, subjects with MCI, and subjects with mild Alzheimer disease (AD) to enable the assessment of the utility of neuroimaging and chemical biomarker measures. METHODS: A total of 819 subjects (229 cognitively normal, 398 with MCI, and 192 with AD) were enrolled at baseline and followed for 12 months using standard cognitive and functional measures typical of clinical trials. RESULTS: The subjects with MCI were more memory impaired than the cognitively normal subjects but not as impaired as the subjects with AD. Nonmemory cognitive measures were only minimally impaired in the subjects with MCI. The subjects with MCI progressed to dementia in 12 months at a rate of 16.5% per year. Approximately 50% of the subjects with MCI were on antidementia therapies. There was minimal movement on the Alzheimer's Disease Assessment Scale-Cognitive Subscale for the normal control subjects, slight movement for the subjects with MCI of 1.1, and a modest change for the subjects with AD of 4.3. Baseline CSF measures of Abeta-42 separated the 3 groups as expected and successfully predicted the 12-month change in cognitive measures. CONCLUSION: The Alzheimer's Disease Neuroimaging Initiative has successfully recruited cohorts of cognitively normal subjects, subjects with mild cognitive impairment (MCI), and subjects with Alzheimer disease with anticipated baseline characteristics. The 12-month progression rate of MCI was as predicted, and the CSF measures heralded progression of clinical measures over 12 months.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
This ADNI clinical characterization paper reports baseline and 12-month clinical, cognitive, and CSF biomarker data for 819 participants: 229 cognitively normal, 398 with MCI, and 192 with mild AD. It shows expected group differences at baseline, a 16.5% 12-month MCI-to-AD progression rate, and baseline CSF Aβ-42 predicting 12-month cognitive change in normal and MCI groups.
Key findings
- At baseline, ADAS-Cog total scores were 6.2 ± 2.9 in controls, 11.5 ± 4.4 in MCI, and 18.6 ± 6.3 in AD (p < 0.001); MCI subjects were most impaired on memory items and only mildly impaired in nonmemory domains.
- Over 12 months, 64 of 398 MCI subjects progressed to AD, a rate of 16.5% (95% CI 11.9%–20.5%); normal controls progressed to MCI at an estimated 1.4% (95% CI 0%–3.2%).
- ADAS-Cog 12-month change was −0.5 ± 3.0 in controls, 1.1 ± 4.4 in MCI, and 4.3 ± 6.6 in AD (p < 0.001).
- Baseline CSF Aβ-42 decreased across groups (controls 206 ± 5, MCI 164 ± 4, AD 143 ± 4 pg/mL; p < 0.001), and higher baseline Aβ-42 was associated with better ADAS-Cog performance and smaller 12-month change in normal subjects and MCI.
Methods
- Multicenter ADNI cohort of 819 subjects aged 55–90 enrolled at baseline and classified as cognitively normal, MCI, or mild AD using standard clinical, cognitive, and functional criteria; subjects were followed for 12 months with cognitive and functional measures, MRI/PET in subsets, and lumbar puncture in a minimum of 20% per site.
- Baseline group comparisons used nonparametric tests (Kruskal-Wallis) with Hochberg multiple comparisons and exact tests for proportions; 12-month change was summarized as conversion proportion, baseline-to-12-month mean or percent difference, and rate per year from mixed-effects regression models.
- Annual conversion rates were estimated using a hybrid interval-censored method; CSF Aβ-42 was related to diagnosis, ADAS-Cog performance, and 12-month ADAS-Cog change.
Limitations
- The paper states that a 12-month change period is not sufficient to draw conclusions about the likelihood of clinical change, and that the ADNI cohort likely does not represent “typical” community subjects because participants were more highly educated, mostly white, and had a high proportion of APOE4 carriers in the MCI and AD groups.
- The paper also states that the MCI criteria required a stringent degree of memory impairment, so these subjects were likely further along the clinical spectrum and more likely to progress than less-impaired MCI subjects.
- Evident from the text: CSF analyses were limited to subsets (baseline n = 114 controls, 199 MCI, 102 AD), and some subjects lacked 12-month follow-up diagnoses (17 controls and 41 MCI subjects), which limits interpretation of conversion and biomarker results.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
53 Own result · 8 Sources not found · 8 Own interpretation · 5 No basis given · 3 Not checked yet · 3 Own method
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“successfully predicted the 12-month change in cognitive measures.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Abstract
plaincorrelationaln = 819- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“but they looked more like the AD group for change in digit span, category fluency, and trail making”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Concomitant medications.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Nonmemory cognitive measures were only minimally impaired in the subjects with MCI.”
- Also stated in: § RESULTS Baseline characteristics. · hedged
“The neuropsychological battery indicated that, in general, subjects with MCI were more impaired than controls on the memory items and were only mildly impaired in nonmemory domains (table 3).”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Abstract
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Also stated in: § RESULTS Baseline characteristics. · hedged
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The sub-jects with AD were impaired in virtually all of the cognitive areas tested.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > RESULTS Baseline characteristics.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These scores indicated, however, that the subjects with MCI gained most of their error points on memory items.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > RESULTS Baseline characteristics.
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Baseline CSF measures of A-42 separated the 3 groups as expected”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Abstract
plaincorrelationaln = 819- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The next greatest area of impairment for the subjects with MCI resulted from the executive function domain.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > RESULTS Baseline characteristics.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The subjects with MCI progressed to dementia in 12 months at a rate of 16.5% per year.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Abstract
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The subjects with MCI also remained relatively stable as a group, and the subjects with mild AD showed a decline on most of these measures.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > DISCUSSION
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The interaction of A-42 and time also had a significant effect on ADAS-Cog scores in subjects with MCI ( p ϭ 0.00), but not in normal subjects.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Concomitant medications.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- It may state two separate claims as one. 20
- Probably not a substantial claim. 14
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The CDR sum of the boxes essentially did not change in the normal control group (0.1) and increased by approximately 0.7 points in the subjects with MCI and 1.5 points in the subjects with AD ( p Ͻ 0.001).”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Concomitant medications.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The subjects with MCI were more memory impaired than the cognitively normal subjects but not as impaired as the subjects with AD.”
- Also stated in: § RESULTS Baseline characteristics. · hedged
“These scores indicated, however, that the subjects with MCI gained most of their error points on memory items.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Abstract
plainn = 819- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Also stated in: § RESULTS Baseline characteristics. · hedged
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In general, the normal group stayed nearly the same, and the AD group worsened significantly compared to the normal controls.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Concomitant medications.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“and more like the normal controls for the clock test and global CDR.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Concomitant medications.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Overall, subjects with MCI had a mean total Alzheimer's Disease Assessment Scale-Cognitive subscale (ADAS-Cog) score of 11.5, while the normal control subjects had a score of 6.2 and the subjects with mild AD had a score of 18.7 ( p Ͻ 0.001, table 3).”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > RESULTS Baseline characteristics.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“cholinesterase inhibitors-normal controls 0%, subjects with MCI 43.7%, subjects with AD 84.9%”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Concomitant medications.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“A total of 819 subjects were recruited and received a baseline evaluation as part of the ADNI study.”
- Also stated in: § RESULTS Baseline characteristics. · plain
“There were 229 normal control subjects, 398 subjects with MCI, and 192 subjects with mild AD enrolled.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > RESULTS Baseline characteristics.
plainn = 819- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Also stated in: § RESULTS Baseline characteristics. · plain
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The estimated premorbid verbal IQ of these subjects was quite high, at almost 120 for the normal control subjects, 116 for the subjects with MCI, and 114 for the subjects with AD.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > RESULTS Baseline characteristics.
hedgedn = 819- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In all cases except the Hachinski Scale and Geriatric Depression Scale, the mean scores for the 3 groups differed significantly at p Ͻ 0.001, with normal controls performing best, subjects with AD worst, and subjects with MCI in the middle.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > RESULTS Baseline characteristics.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The MCI group was more variable; in most cases they worsened more than the normal group but not as much as the AD group”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Concomitant medications.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Approximately 50% of the subjects with MCI were on antidementia therapies.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Abstract
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Most of the subjects in the study were white, and this was approximately equivalent across the groups.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > RESULTS Baseline characteristics.
plainn = 819- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“There was minimal movement on the Alzheimer's Disease Assessment Scale-Cognitive Subscale for the normal control subjects, slight movement for the subjects with MCI of 1.1, and a modest change for the subjects with AD of 4.3.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Abstract
plainn = 819- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As shown in table 2, the mean age of the 3 groups was equivalent at approximately 75 years.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > RESULTS Baseline characteristics.
plainn = 819- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“there were more men than women in the MCI group (64.6% men vs 35.4% women).”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > RESULTS Baseline characteristics.
plainn = 398- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“There was somewhat less variability on the global measures, while some of the individual neuropsychological tests such as the AVLT showed considerable variability among the subjects despite relatively modest mean group changes.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > DISCUSSION
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“CSF A-42 levels were assessed at baseline and were correlated with clinical diagnoses, performance on the ADAS-Cog, and changes in performance on the ADAS-Cog over 1 year (table 3).”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Concomitant medications.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Delayed recall improved by 5.3% in the normal control group and declined by 4.8% in the subjects with MCI and 7.2% in the subjects with AD.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Concomitant medications.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“As such, the baseline characteristics of the subjects indicated that the 3 groups were generally well educated, intelligent, and mostly white, and perform in a fashion similar to subjects recruited for typical clinical trials.[25][26][27][28]”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > DISCUSSION
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The CSF A-42 data correlated with the diagnostic groups in the expected fashion with levels being lower in the subjects with MCI than in the normal subjects.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > DISCUSSION
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: CSF Aβ-42 level is a valid biomarker of Alzheimer's disease pathology and progression.
None of the sources cited for this claim have been checked yet.
In the paper
“They tend to be more highly educated and the proportion of APOE4 carriers was quite high in the MCI and AD groups but is consistent with subjects recruited for clinical trials.25”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > DISCUSSION
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“On the memory measures such as the AVLT, there was virtually no change across 12 months in learning in the normal control group with a decreased learning of 1.3 items in the subjects with MCI and 3.7 fewer items learned in the AD group.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Concomitant medications.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“An additional 8 subjects with MCI reverted to a normal classification over that interval, and 41 did not have a follow-up diagnosis.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Concomitant medications.
plainn = 398- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“may allow detection of the neurodegenerative process at an earlier point in the spectrum”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Abstract
What the sources say
- Not matchedcites [6]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [7]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [8]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Not matched
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“All hypothesis tests were 2-sided at level 0.05, and models were validated both graphically and analytically.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Data analysis.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Most investigators believe that Alzheimer disease (AD) is a slowly evolving process that likely begins years to decades before the clinical symptoms are manifest.1”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Abstract
What the sources say
- Not matchedcites [1]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
- Not matched
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“A 12month change period is not a sufficient amount of time to draw conclusions regarding the likelihood of clinical change.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > DISCUSSION
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“has been a target for clinical trials.[2][3][4][5]”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Abstract
What the sources say
- Not matchedcites [2]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [3]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [4]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [5]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The diagnostic conversions over the 12-month period must be viewed with some caution.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > DISCUSSION
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This might be a useful technique to reduce the numbers of subjects used in clinical trials to detect a clinical effect of therapeutic interventions.”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > DISCUSSION
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
- Rests on an assumption: CSF Aβ-42 level is a valid biomarker of Alzheimer's disease pathology and progression.
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“Mild cognitive impairment (MCI) represents an attempt to characterize subjects at an early clinical phase”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Abstract
What the sources say
- Not matchedcites [2]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [3]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [4]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [5]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
- Not matched
The paper relies on this without support.
The expected 12-month progression rate for MCI was approximately 16.5% per year.
“The 12-month progression rate of MCI was as predicted”
“The subjects with MCI progressed to dementia in 12 months at a rate of 16.5% per year.”
The paper relies on this without support.
The baseline characteristics observed in the recruited cohorts matched the characteristics anticipated for normal, MCI, and AD cohorts.
“The Alzheimer's DiseaseNeuroimaging Initiative has successfully recruited cohorts of cognitively normal subjects, subjects with mild cognitive impairment (MCI), and subjects with Alzheimer disease with anticipated baseline characteristics.”
The paper relies on this without support.
The MCI subjects have an underlying Alzheimer's disease process that drives clinical progression.
“As such, these subjects were likely further down the clinical spectrum with respect to the underlying AD process such that they would be more likely to progress than subjects with less impairment in the MCI spectrum.”
The paper relies on this without support.
CSF Aβ-42 level is a valid biomarker of Alzheimer's disease pathology and progression.
“The CSF A-42 data correlated with the diagnostic groups in the expected fashion with levels being lower in the subjects with MCI than in the normal subjects.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Another issue is that the vast majority of data from PET and CSF are from selected participants recruited through tertiary care dementia centers [176].”
From NIA‐AA Research Framework: Toward a biological definition of Alzheimer's disease (2018) · cites this paper as [176]
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated major: it would touch a result — a value a result rests on, reported and recomputed p-values on opposite sides of the significance level, two passages that conflict with no sign which is right, or a cited work retracted before this paper was published.
In the paper
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Concomitant medications.
“Among the 398 subjects with MCI at baseline, 64 progressed to AD at 12 months”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Concomitant medications.
“Table 5 Baseline measures on subjects according to progression status at 1 year Assortment variable Nonprogressors, mean ؎ SD (n ؍ 329) Progressors, mean ؎ SD (n ؍ 69)”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > RESULTS Baseline characteristics.
“the subjects with mild AD had a score of 18.7”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > RESULTS Baseline characteristics.
“ADAS-Cog total 6.2 Ϯ 2.9 11.5 Ϯ 4.4 18.1 18.6 Ϯ 6.3 14.0 Ͻ0.001 a, b, c”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Concomitant medications.
“subjects with MCI (r ϭ Ϫ0.22; 95% CI Ϫ0.035 to Ϫ0.08)”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > Concomitant medications.
“95% CI Ϫ0.035 to Ϫ0.08”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > RESULTS Baseline characteristics.
“In all cases except the Hachinski Scale and Geriatric Depression Scale, the mean scores for the 3 groups differed significantly at p Ͻ 0.001”
§ Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical characterization > RESULTS Baseline characteristics.
“GDS Score 0.8 Ϯ 1.1 1.6 Ϯ 1.4 7.3 1.7 Ϯ 1.4 0.6 Ͻ0.001 a, b”
Citations
Cites
McKhann, G., Drachman, D., Folstein, M., Katzman, R., Price, D., & Stadlan, E. M. (1984) · Neurology · doi:10.1212/wnl.34.7.939 · Cited by 27,915
Development and validation of a geriatric depression screening scale: a preliminary report
Yesavage, J. A., Brink, T. L., Rose, T. L., Lum, O., Huang, V., Adey, M., & Leirer, V. O. (1982) · Journal of Psychiatric Research · doi:10.1016/0022-3956(82)90033-4 · Cited by 15,025
Mild Cognitive Impairment: Clinical Characterization and Outcome
Petersen, R. C., Smith, G. E., Waring, S. C., Ivnik, R. J., Tangalos, E. G., & Kokmen, E. (1999) · Archives of Neurology · doi:10.1001/archneur.56.3.303 · Cited by 8,980
Self-citationRandom-Effects Models for Longitudinal Data
Laird, N. M., & Ware, J. H. (1982) · Biometrics · doi:10.2307/2529876 · Cited by 8,923
VALIDITY OF THE TRAIL MAKING TEST AS AN INDICATOR OF ORGANIC BRAIN DAMAGE
Reitan, R. M. (1958) · Perceptual and Motor Skills · doi:10.2466/pms.1958.8.3.271 · Cited by 8,167
Mild cognitive impairment as a diagnostic entity
Petersen, R. C. (2004) · Journal of Internal Medicine · doi:10.1111/j.1365-2796.2004.01388.x · Cited by 7,864
Self-citationThe Neuropsychiatric Inventory: Comprehensive assessment of psychopathology in dementia
Cummings, J. L., Mega, M., Gray, K., Rosenberg-Thompson, S., Carusi, D. A., & Gornbein, J. (1994) · Neurology · doi:10.1212/wnl.44.12.2308 · Cited by 7,568
A new rating scale for Alzheimer's disease
(1984) · American Journal of Psychiatry · doi:10.1176/ajp.141.11.1356 · Cited by 4,175
Gauthier, S. G., Reisberg, ., Zaudig, M., Petersen, R. C., Ritchie, K. A., Broich, K., Belleville, S., Brodaty, H., Bennett, D. A., Chertkow, H. M., et al. (2006) · The Lancet · doi:10.1016/s0140-6736(06)68542-5 · Cited by 4,094
Self-citationL’examen Clinique en Psychologie
Rubé, P. (1959) · American Journal of Psychotherapy · doi:10.1176/appi.psychotherapy.1959.13.4.989 · Cited by 3,299
Cited by
Sorted by citations
Items 1,701–1,800 of 2,570
Generating novel pituitary datasets from open-source imaging data and deep volumetric segmentation
Gologorsky, R., Harake, E. S., von Oiste, G. G., Nasir-Moin, M., Couldwell, W. T., Oermann, E. K., & Hollon, T. C. (2022) · Pituitary · doi:10.1007/s11102-022-01255-7 · Cited by 4
Improving the level of autism discrimination with augmented data by GraphRNN
Sun, H., He, Q., Qi, S., et al. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.106141 · Cited by 3
Lee, D., Yun, C., Kang, S.-H., et al. (2022) · Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment · doi:10.1016/j.nima.2022.167779 · Cited by 3
“In addition, a clinical T1-weighted image was obtained from the ADNI database [23] to observe whether denoising performance has a similar tendency when the 2D and 3D MMWF were applied to clinical data.”
Annual stability of the plasma Aß40/42 ratio and associated factors
Nakamura, T., Kawarabayashi, T., Nakahata, N., Itoh, K., Ihara, K., Nakaji, S., Ikeda, Y., Takatama, M., & Shoji, M. (2023) · Annals of Clinical and Translational Neurology · doi:10.1002/acn3.51770 · Cited by 5
Revisiting reliable change with Iverson (2001)
Duff, K. M. (2023) · The Clinical Neuropsychologist · doi:10.1080/13854046.2023.2202333 · Cited by 3
Zinkunegi, A. J., Bruno, D., Betthauser, T. J., Langhough, R. E., Asthana, S., Chin, N. A., Hermann, B. P., Johnson, S. C., & Mueller, K. D. (2023) · The Clinical Neuropsychologist · doi:10.1080/13854046.2023.2223389 · Cited by 3
Awotunde, J. B., Sur, S. N., Imoize, A. L., Misra, S., & Gaber, T. (2023) · Lecture notes in electrical engineering · doi:10.1007/978-981-99-1983-3_32 · Cited by 3
Deep Learning Enabled Classification of Cognitive Impairment Stages Using MRI Images
Jismi, K., Ananda Resmi, S., & Ramanujan, A. (2023) · International Conference on Computational Intelligence and Sustainable Engineering Solutions (CISES) · doi:10.1109/cises58720.2023.10183447 · Cited by 3
“CNN is a multi-layer neural network that consists of a convolution layer, pooling layer, and fully connected layer [18] [19], which is an excellent feature extractor, which minimizes the complications in expensive feature engineering, which made it a good choice for medical image processing and is highly efficient for…”
BackgroundHaley, A. P., Clark, A. L., Duarte, A., & Initiative, A. D. N. (2023) · Psychosomatic Medicine · doi:10.1097/psy.0000000000001245 · Cited by 3
Predicting cognitive decline in a low-dimensional representation of brain morphology
Lamontagne‐Caron, R., Desrosiers, P., Potvin, O., Doyon, N., & Duchesne, S. (2023) · Scientific Reports · doi:10.1038/s41598-023-43063-4 · Cited by 3
Temporal Supervised Contrastive Learning for Modeling Patient Risk Progression
Noroozizadeh, S., Weiss, J. C., & Chen, G. H. (2023) · PubMed · doi:10.48550/arxiv.2312.05933 · Cited by 3
“We also evaluate our approach using the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset (Petersen et al., 2010), encompassing 11,651 hospital visits across 1,346 patients, tracking AD progression through 6-month interval follow-ups.”
Nabizadeh, F., Ward, R. T., Balabandian, M., Kankam, S. B., & Pourhamzeh, M. (2023) · Current Journal of Neurology · doi:10.18502/cjn.v22i4.14526 · Cited by 3
Kolla, S., Falakshahi, H., Abrol, A., et al. (2024) · Sensors · doi:10.3390/s24030814 · Cited by 3
“[10] investigates hub identification in healthy aging subjects’ brain networks using four measurements and explores the impact of various parcellation schemes, revealing consistent hub identification within the same type of brain network.”
BackgroundA Two-Species Model for Abnormal Tau Dynamics in Alzheimer’s Disease
Wen, Z., Ghafouri, A., & Biros, G. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-47425-5_7 · Cited by 3
Ishaaq, N., Nafis, M. T., & Reyaz, A. (2024) · Advances in medical diagnosis, treatment, and care (AMDTC) book series · doi:10.4018/979-8-3693-3679-3.ch009 · Cited by 4
Teng, J., McKenna, M. R., Gbadeyan, O., Prakash, R. S., & Initiative, A. D. N. (2024) · Network Neuroscience · doi:10.1162/netn_a_00373 · Cited by 6
“To our knowledge, there have been little systematic differences in protocols across the various sites (Nir et al., 2013), and thus, data harmonization was not commonly performed across ADNI studies (Jack et al., 2008, 2015; Weber et al., 2021).”
Orlichenko, A., Su, K., Tian, Q., Shen, H., Deng, H.-W., & Wang, Y. (2024) · Journal of medical imaging · doi:10.1117/1.jmi.11.2.024010 · Cited by 3
Wang, H., Chen, C., Kuo, C., Huang, T., Kuo, K., Chuang, T., Lin, Y., Chung, H., & Initiative, A. D. N. (2024) · NMR in Biomedicine · doi:10.1002/nbm.5169 · Cited by 3
Deep Residual-in-Residual Model-Based PET Image Super-Resolution with Motion Blur
Chen, J.-C., Zhao, J., Tian, X., Chen, S., Yu-ling, W., Han, D., & Lin, Y. (2024) · Electronics · doi:10.3390/electronics13132582 · Cited by 4
Federated Learning Governance using Eclipse Dataspace Components Connectors
Mitrovska, A., Rosas, M. F., Safari, P., Shariati, B., Fischer, J., & Freund, R. E. (2024) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata62323.2024.10826122 · Cited by 4
IA en santé : guide pour l’interniste averti
Barba, T., Robert, M., & Hot, A. (2025) · La Revue de Médecine Interne · doi:10.1016/j.revmed.2025.02.002 · Cited by 4
Qiang, Q., Skudder‐Hill, L., Toyota, T., Huang, Z., Wei, W., & Adachi, H. (2025) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2025.02.008 · Cited by 3
AI-Generated Brain Scans for Synthetic Healthcare Data
Leong, W. Y. (2025) · IEEE International Colloquium on Signal Processing & Its Applications (CSPA) · doi:10.1109/cspa64953.2025.10933351 · Cited by 3
The longitudinal effects of global and regional brain measurements on cognitive abilities
Hosseininasabnajar, F., Kakinami, L., & Initiative, A. D. N. (2025) · Scientific Reports · doi:10.1038/s41598-025-08152-6 · Cited by 4
Rhodes, E., Insel, P. S., Kassel, M. T., Kryza‐Lacombe, M., Butters, M. A., Bickford, D. D., Tosun, D., Morin, R. T., Aisen, P. S. S., Rosen, H. J., et al. (2025) · American Journal of Geriatric Psychiatry · doi:10.1016/j.jagp.2025.08.008 · Cited by 4
Self-citationCerebral perivascular spaces as predictors of dementia risk and accelerated brain atrophy
Barisano, G., Iv, M., Choupan, J., & Gephart, M. H. (2025) · Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition · doi:10.58530/2025/4721 · Cited by 3
A hierarchical model for identifying mild cognitive impairment
Wang, B., Du, L., Zhang, J., & Chen, P. (2015) · International Conference on Natural Computation (ICNC) · doi:10.1109/icnc.2015.7378057 · Cited by 3
Zhang, J., & Shi, Y. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87234-2_67 · Cited by 3
Artificial Intelligence in Alzheimer’s Drug Discovery
Cheng, F., & Cummings, J. L. (2022) · Cambridge University Press eBooks · doi:10.1017/9781108975759.007 · Cited by 5
Choe, Y. M., Suh, G., Lee, B. C., Choi, I.-G., Lee, J. H., Kim, H. S., Hwang, J., & Kim, J. W. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.912891 · Cited by 4
Xu, J., Guan, X., Wen, J., Zhang, M., Xu, X., & Initiative, A. D. N. (2022) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2022.943702 · Cited by 3
Bartolo, N. D., Mortimer, N., Manter, M. A., Sanchez, N., Riley, M. M., O’Malley, T. T., & Hooker, J. M. (2022) · ACS Chemical Neuroscience · doi:10.1021/acschemneuro.2c00607 · Cited by 3
Gauthier, S. G., Leuzy, A., & Rosa‐Neto, P. (2013) · Neurodegenerative Diseases · doi:10.1159/000353748 · Cited by 3
Malpas, C. B., Saling, M. M., Velakoulis, D., Desmond, P. M., Hicks, R. J., & O'Brien, T. J. (2015) · Journal of Computer Assisted Tomography · doi:10.1097/rct.0000000000000256 · Cited by 3
Multi-tasks Deep Learning Model for classifying MRI images of AD/MCI Patients
Kumar, S. S., & Malaiyappan, N. (2017) · International Journal of Engineering and Technology · doi:10.21817/ijet/2017/v9i3/1709030158 · Cited by 3
Feature ranking for mild cognitive impairment and Alzheimer's disease diagnosis
Domashenko, D., Manko, M., Popov, A. O., Krashenyi, I., Ramı́rez, J., & Górriz, J. M. (2017) · Signal Processing Symposium (SPSympo) · doi:10.1109/sps.2017.8053649 · Cited by 3
“MRI-image database from [20] was used to obtain data.”
MethodMartínez‐Torteya, A., Trejo-Castro, A. I., Celaya-Padill, J. M., & Tamez‐Peña, J. G. (2019) · IEEE International Conference on Bioinformatics and Bioengineering (BIBE) · doi:10.1109/bibe.2019.00043 · Cited by 3
Mathies, F. L., Lange, C., Maeurer, A., Apostolova, I. I., Klutmann, S., & Buchert, R. (2020) · Journal of Alzheimer s Disease · doi:10.3233/jad-200530 · Cited by 3
“However, the vast majority of studies on the use of FDG PET for the etiological diagnosis of CUCI have focused on memory clinic outpatient settings and employed restrictive eligibility criteria resulting in patient samples with only minor comorbidities [17].”
Paul, R. H., Garcia-Egan, P. M., Bolzenius, J. D., & Mannarino, J. A. (2020) · Current topics in behavioral neurosciences · doi:10.1007/7854_2020_185 · Cited by 4
Intraperitoneal hyperthermia in the management of pseudomyxoma peritonei.
Hsu, K.-H., Chou, C.-Y., & Chang, Y.-C. (2007) · PubMed · Cited by 3
Medical Imaging-based Techniques for Alzheimer's Disease Detection: A Survey
Zaabi, M., Smaoui, N., Hariri, W., et al. (2021) · International Multi-Conference on Systems, Signals & Devices (SSD) · doi:10.1109/ssd52085.2021.9429494 · Cited by 3
Blum, D., Hepp, T., Belov, V., Goya‐Maldonado, R., la Fougère, C., & Reimold, M. (2023) · Human Brain Mapping · doi:10.1002/hbm.26246 · Cited by 2
“We collected 3024 multi-center brain-MRI scans (T1-weighted, 3 Tesla) from the freely accessible image databases ADNI ((Petersen et al., 2010), http://adni.loni.usc.edu/), AIBL ((Ellis et al., 2009), https://aibl.csiro.au), ABIDE ((Di Martino et al., 2014), https://fcon_ 1000.projects.nitrc.org/indi/abide/), PPMI ((In…”
MethodKidney Function Impacts Plasma Alzheimer’s Biomarkers In A Cognitively Normal Multi-Ethnic Cohort
Hall, J. R., Petersen, M. E., Johnson, L. A., & Sid OBryant for the HABS-HD Study Team (2023) · Archives of Nephrology and Urology · doi:10.26502/anu.2644-2833058 · Cited by 3
Kress, G. T., Popa, E. S., Thompson, P. M., Bookheimer, S. Y., Thomopoulos, S. I., Ching, C. R. K., Zheng, H., Hirsh, D. A., Merrill, D. A., Panos, S. E., et al. (2023) · NeuroImage Clinical · doi:10.1016/j.nicl.2023.103458 · Cited by 3
“Participants were diagnosed at baseline and reassessed at each study visit (Petersen et al., 2010).”
MethodBhaskaran, B., & Anandan, K. (2017) · International Journal of Software Science and Computational Intelligence · doi:10.4018/ijssci.2017100104 · Cited by 3
Investigation of Genome-Wide Association SNPs and Alzheimer’s Disease
Mikhail, M., Sayed, A. Y., Mabrouk, M. S., & El-Baz, A. S. (2020) · Cited by 3
Infinitesimal Drift Diffeomorphometry Models for Population Shape Analysis
Lee, B. C., Tward, D. J., Hu, Z., Trouvé, A., & Miller, M. I. (2020) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00439 · Cited by 3
Positioning Personal Polygenic Risk score against the population backgroundPreprint
Leonenko, G., Baker, E. A., Schmidt, K. M., Escott‐Price, V., & Initiative, T. A. D. N. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/813170 · Cited by 3
Predicting future regional tau accumulation in asymptomatic and early Alzheimer’s diseasePreprint
Giorgio, J., Jagust, W. J., Baker, S. L., Landau, S. M., Peter Tiňo, B., Kourtzi, Z., & Initiative, A. D. N. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.21203/rs.3.rs-60371/v1 · Cited by 3
Self-citationLin, W., Donohue, M., Insel, P. S., Schwartzman, A., & Thompson, W. K. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.03.10.434854 · Cited by 3
“The classification of stages of disease progression in ADNI was given as follows [18].”
MethodSelf-citationAzcona, E. A., Besson, P., Wu, Y., Kurani, A. S., Bandt, S. K., Parrish, T. B., Katsaggelos, A. K., & Initiative, A. D. N. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.04.15.440008 · Cited by 3
“(2015) employ BrainPrint to yield extensive characterizations of brain anatomy using structure-specific shape descriptors with samples from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset (Petersen et al., 2010) to identify unique individuals (3000 subjects) with a 99.”
MethodInfluentialAyvaz, D. S., & Baytaş, İ. M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.08794 · Cited by 3
Li, B., Yadav, S., Zhou, S. W., Du, Y., Qian, L., Karas, Z., Zhang, Y., Shen, L., Lee, J. J., Vardarajan, B. N., et al. (2023) · medRxiv · doi:10.1101/2023.04.06.23288268 · Cited by 3
Demystifying the Risk of Reidentification in Neuroimaging Data – A technical and regulatory analysisPreprint
Jwa, A. S., Koyejo, S., & Poldrack, R. A. (2023) · doi:10.31219/osf.io/73mwd · Cited by 3
Liu, Q., Zeng, W., Hongtu Zhu, B., Li, L., Wong, W. H., & Initiative, A. D. N. (2024) · medRxiv · doi:10.1101/2024.10.03.24314824 · Cited by 3
A Multimodal Foundation Model for Discovering Genetic Associations with Brain Imaging PhenotypesPreprint
Reyes, D. M., Burch, M., Parida, L., et al. (2024) · medRxiv · doi:10.1101/2024.11.02.24316653 · Cited by 3
Multi-organ AI Endophenotypes Chart the Heterogeneity of Pan-disease in the Brain, Eye, and HeartPreprint
Boquet-Pujadas, A., Anagnostakis, F., Yang, Z., Tian, Y., Duggan, M. R., Erus, G., Srinivasan, D., Joynes, C. M., Bai, W., Patel, P. J., et al. (2025) · medRxiv · doi:10.1101/2025.08.09.25333350 · Cited by 3
Ghosal, S., Barhomi, Y., Ganapathi, T., Krystosik, A., Krishnan, L., Guntury, S., Li, D., Initiative, A. D. N., Casale, F. P., & Karaletsos, T. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.10.31.685862 · Cited by 4
Knowing What You Know in Brain Segmentation Using Bayesian Deep Neural Networks
McClure, P., Rho, N., Lee, J. A., Kaczmarzyk, J., Zheng, C. Y., Ghosh, S., Nielson, D. M., Thomas, A. G., Bandettini, P. A., & Pereira, F. (2019) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2019.00067 · Cited by 59
“These networks predict a manual or an automated segmentation from the structural volumes (Roy et al. (2018b), Fedorov et al. (2017b), Fedorov et al. (2017a), and Dolz et al. (2018) used FreeSurfer, and Petersen et al. (2010) used GIF (Cardoso et al., 2015)).”
MethodExplainable Dementia Prediction using Functional Neuroimages and Risk Factors
Shi, Z., Dan, T., Smith, P. J., et al. (2024) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi56570.2024.10635658 · Cited by 2
A 3D decoupling Alzheimer’s disease prediction network based on structural MRI
Wei, S., Yang, W., Wang, E., Wang, S., & Li, Y. (2025) · Health Information Science and Systems · doi:10.1007/s13755-024-00333-3 · Cited by 3
Finney, C. A., Brown, D. A., Shvetcov, A., et al. (2025) · Translational Psychiatry · doi:10.1038/s41398-025-03247-0 · Cited by 2
A Deep Ensemble Learning Approach for Automatic AD Detection
Balamurugan, A. G., & Gomathi, N. (2025) · Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) · doi:10.2174/0123520965354299241225100818 · Cited by 2
Finney, C. A., Initiative, A. D. N., & Shvetcov, A. (2026) · npj Aging · doi:10.1038/s41514-026-00390-w · Cited by 3
Functional Connectivity Disruptions in Alzheimer’s Disease: A Maximum Flow Perspective
Stubby, E. T., Razavi, S. M., & Khanmohammadi, S. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-82768-6_20 · Cited by 2
Ding, Y., Zhang, F., Li, M., & Wang, J. (2025) · Intelligent Medicine · doi:10.1016/j.imed.2024.11.005 · Cited by 2
Machine Learning for Early Prediction of Cognitive Decline in Alzheimer's Disease
Palma, L. D., Nisio, A. D., Lanzolla, A. M. L., Matarrese, P., Pich, E. M., & Attivissimo, F. (2025) · IEEE International Symposium on Medical Measurements and Applications (MeMeA) · doi:10.1109/memea65319.2025.11068006 · Cited by 3
Towards Real Time Alzheimer's Diagnosis: A PSO ‐ GA ‐Driven Deep Learning Solution for Telemedicine
Kumar, A., Ahmad, F., & Alam, B. (2025) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.70180 · Cited by 2
Huang, J., Wang, Y., Stein, T. D., Ang, T. F. A., Zhu, Y., Tao, Q., Lunetta, K. L., Mez, J., Au, R., Farrer, L. A., et al. (2025) · Translational Psychiatry · doi:10.1038/s41398-025-03542-w · Cited by 8
Hou, X., Zhou, K., Wu, Y., et al. (2025) · npj Parkinson s Disease · doi:10.1038/s41531-025-01127-4 · Cited by 2
Iqbal, Z., Rahman, M. M., Zia, Q., Popov, P., Fu, Z., Calhoun, V. D., & Plis, S. (2025) · Brain Sciences · doi:10.3390/brainsci15090954 · Cited by 2
“For AD classification, we utilized two comprehensive datasets: the OASIS dataset [38] (823 subjects: 651 HCs and 172 patients) and the ADNI dataset [39] (499 subjects: 433 HCs and 66 patients).”
Wang, X., Ye, T., Zhou, W., Zhang, J., & Initiative, A. D. N. (2025) · Neurology · doi:10.1212/wnl.0000000000214041 · Cited by 2
Multi-modal orthogonal fusion network via cross-layer guidance for Alzheimer’s disease diagnosis
Zhao, Y., Jiang, B., Chen, Y., Luo, Y., & Tang, J. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.108091 · Cited by 4
Mono-Modalizing Extremely Heterogeneous Multi-modal Medical Image Registration
Choo, K., Han, H., Kim, J.-Y., et al. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-04971-1_41 · Cited by 2
Latent Causal Modeling for 3D Brain MRI Counterfactuals
Peng, W., Xia, T., Ribeiro, F. D. S., Bosschieter, T. M., Adeli, E., Zhao, Q., Glocker, B., & Pohl, K. M. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-05472-2_19 · Cited by 2
Vilor‐Tejedor, N., Rodrigo, A., Genius, P., Rodríguez‐Fernández, B., Anastasi, F., Pelkmans, W., Navarro, A., Adams, H. H. H., Wisse, L. E. M., Gispert, J. D., et al. (2025) · NeuroImage Clinical · doi:10.1016/j.nicl.2025.103889 · Cited by 3
Luan, Y., Huang, Q., Wang, J., Guan, Y., Li, B., Franzmeier, N., Xie, F., & Ewers, M. (2025) · Brain · doi:10.1093/brain/awaf425 · Cited by 5
Zheng, Y.-W., Tan, C., Sun, S., Ma, X., Gong, J.-N., Huang, L., Tan, L., Zhang, X. H., Luo, X., & Xu, W. (2025) · Journal of Affective Disorders · doi:10.1016/j.jad.2025.120646 · Cited by 2
Plasma lipid metabolites as biomarkers of early white matter degeneration in Alzheimer's disease
Haghighi, A. S., Nasiri, H., Ghayourvahdat, A., Azimizonuzi, H., Ghasemi, N., Mansouri, M., Bazkiaei, A. M., Amirian, M. A., Zeinali, S., Gozali, H., et al. (2025) · Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring · doi:10.1002/dad2.70217 · Cited by 2
AI models, bias and data sharing efforts to tackle Alzheimer's disease and related dementias
Kolachalama, V. B., Sureshkumar, V., & Au, R. (2025) · The Journal of Prevention of Alzheimer s Disease · doi:10.1016/j.tjpad.2025.100400 · Cited by 3
Monserratt, L. H., Morales, J. A., Mohammed, A., Ovalle-Eliseo, S., Islas-Huerta, G., Perez, L. A., & Díaz‐Santos, M. (2025) · Alzheimer s & Dementia · doi:10.1002/alz.70918 · Cited by 2
“59,60 Ithasbeenincludedinlarge landmark studies such as the Alzheimer’s Disease Neuroimaging Initiative (ADNI) 61 and Alzheimer’s Disease Cooperative Study (ADCS) 62 and in numerous multicenter trials funded by pharmaceutical companies, such as EXPEDITION 1 and 2, 63 EMERGE, 64 ENGAGE, 64 and CLARITY.”
Enhanced monitoring of Alzheimer's disease brain atrophy using composite value ratios of volumes
Saguer, I. L., & Oxtoby, N. P. (2025) · Brain Communications · doi:10.1093/braincomms/fcaf497 · Cited by 3
Rabl, M., Hartog, W. L., van der Flier, W. M., Pijnenburg, Y. A. L., Teunissen, C. E., Tsolaki, M., Freund‐Levi, Y., Vandenberghe, R. R., Froelich, L., Streffer, J. R., et al. (2025) · Alzheimer s & Dementia · doi:10.1002/alz.71054 · Cited by 4
Kim, H. J., Kim, K. W., Seo, S. W., Lee, J., Jung, S. J., Han, J. W., Jang, H., Jhoo, J. H., Kim, J. L., Yang, H. W., et al. (2025) · Alzheimer s & Dementia · doi:10.1002/alz.71040 · Cited by 2
Plasma Aβ42/40 predicts progression from Aβ-amyloid negative to positive PET scans
Feizpour, A., Doré, V., Bourgeat, P., Doecke, J. D., Cánovas, R., Laws, S. M., Porter, T., Huang, K., Fowler, C. J., Martins, R. N., et al. (2026) · The Journal of Prevention of Alzheimer s Disease · doi:10.1016/j.tjpad.2025.100455 · Cited by 4
Self-citationLu, W., Gong, Q., Chen, Y., Qiu, S., & An, J. (2026) · Neural Networks · doi:10.1016/j.neunet.2026.108542 · Cited by 2
Multi-organ AI endophenotypes chart the heterogeneity of brain, eye and heart pan-disease
Wen, J., Davatzikos, C. A., Tian, Y., Rafii, M. S., Aisen, P., Walker, K. A., Zalesky, A., Ferrucci, L., Zeng, J., Boquet-Pujadas, A., et al. (2026) · Nature Mental Health · doi:10.1038/s44220-025-00560-x · Cited by 5
Self-citationWang, X., Ye, T., Dai, B., Zhang, J., Zhou, W., & Initiative, A. D. N. (2026) · Journal of Alzheimer s Disease · doi:10.1177/13872877251406131 · Cited by 2
Parthiv, J. P., Harshavardhanan, P., & Nair, R. (2025) · IEEE International Conference on Advanced Computing Technologies (ICACT) · doi:10.1109/icact67549.2025.11351391 · Cited by 2
Bloch, L., Borys, K., Nensa, F., & Friedrich, C. M. (2026) · NeuroImage · doi:10.1016/j.neuroimage.2026.121813 · Cited by 2
Harmonizing neuropsychological test data across prospective studies
Shishegar, R., Doecke, J. D., Lim, Y. Y., Bourgeat, P., Doré, V., Tallapragada, B., Laws, S. M., Porter, T., Burnham, S., Feizpour, A., et al. (2026) · Alzheimer s & Dementia · doi:10.1002/alz.71186 · Cited by 2
“This study was conducted using data drawn from three large international studies, the AIBL, 16,32 ADNI, 33,34 and the Knight Alzheimer’s Disease Research Center Open Access Series of Imaging Studies-3 (OASIS).”
Self-citationGenetic risk in Alzheimer’s disease
Pan, Y., Cho, H., Lou, Q., Zhang, W., Asken, B. M., & Song, Q. (2026) · npj Systems Biology and Applications · doi:10.1038/s41540-026-00665-8 · Cited by 3
Ali, M. B. G., & Smiti, A. (2026) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2026.102730 · Cited by 2
Alqarni, A. A., Alkhybari, E. M., Alshuhri, M. S., Aljuhani, M., Hadadi, I., Alosaimi, M. H., Alshaari, H., Alqahtani, M. M., & Abd-Elghany, A. A. (2026) · Frontiers in Neurology · doi:10.3389/fneur.2026.1680116 · Cited by 2
“ADNI participants have provided informed written consent across all recruitment cites as described elsewhere (21), and we have been granted access to the ADNI data archived at LONI.”
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. (2026) · Annals of Neurology · doi:10.1002/ana.78194 · Cited by 4
Self-citationTatar, A.-S., Colniță, A., Brezeștean, I.-A., & Boca, S. (2026) · Biophysical Reviews · doi:10.1007/s12551-026-01428-9 · Cited by 2
Wang, C., Piao, S., Chen, Z. P., et al. (2026) · Medical Image Analysis · doi:10.1016/j.media.2026.104099 · Cited by 2
Andrews, D., Golchi, S., Collins, D. L., & Initiative, F. T. A. D. N. (2026) · Alzheimer s Research & Therapy · doi:10.1186/s13195-026-02024-5 · Cited by 6
A survey on multimodal explainable Artificial Intelligence
Giovannoni, C., Metta, C., Monreale, A., & Rinzivillo, S. (2026) · Intelligent Systems with Applications · doi:10.1016/j.iswa.2026.200671 · Cited by 2
Lakra, A., Singh, N., Vashishth, S., Ambasta, R. K., & Kumar, P. (2026) · Ageing Research Reviews · doi:10.1016/j.arr.2026.103189 · Cited by 3
Metadata
- Authors
- Petersen, R. C., Aisen, P. S., Beckett, L. A., Donohue, M. C., Gamst, A. C., Harvey, D. J., Jack, C. R., Jagust, W. J., Shaw, L. M., Toga, A. W., Trojanowski, J. Q., & Weiner, M. W.
- Affiliations
- Mayo Clinic (US)
- ORCID
- Venue
- Neurology
- Published
- 2010-01-19
- Volume
- 74
- Issue
- 3
- Pages
- 201-209
- ISSN
- 0028-3878 (ISSN-L)
- 1526-632X
- PMC
- PMC2809036
- OpenAlex
- W2122328291
- Cited by
- 2,572
- References
- 27
- Citations by year
- 2026: 312 · 2025: 391 · 2024: 327 · 2023: 254 · 2022: 233 · 2021: 209 · 2020: 182 · 2019: 121 · 2018: 85 · 2017: 67 · 2016: 71 · 2015: 66 · 2014: 45 · 2013: 63 · 2012: 59
- FWCI
- 26.56
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Neurology (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (submittedVersion)
- PubMed Central (submittedVersion)
- Funders
- Alzheimer's Disease Neuroimaging Initiative (AG024904)
- National Institutes of Health (5r01ag027984-03, 5r01ag030048-05, n01mh22005-16-0-1, 5r01dk075128-05, 5u01ag006786-38, 5r01mh083552-05, 5r01ca104573-05, 5p30ag010124-30, 5p01ag009215-20, 5r01ag011378-25, 5p41rr023953-04, 5u01ag024904-10, 5u01ag029213-05, 5r01ag016381-08, 5r01eb006266-04, 5r01ag010897-27, AG024904, 5p01ag019724-23, 5p30ca093373-23, 5p50ag016574-20)
- National Institute on Aging (AG027984, U01-AG-024904, R01AG11378, P30AG010129, U19 AG010483, U01‐AG10483, R01-AG030048, P50‐AG16574, P01 & AG019724, U01AG06786, P50-AG23501, RC1AG035427, AG 010483, R01AG031224, AG031224, P01 AG 17586-10, 1 U01 AG024904, AG 024904, UO1AG029213-01, P30AG036468, 2 P50 AG016570, 1 U01 AG 024904-05, P30 AG 10124-18, R01 AG031252, P01AG017586, U01AG010483, 1 U01 AG24904, 1P01 AG-19724-07, R01AG027859, P50AG023501, AG027859, P01 AG012435, AG-11378, 2P50AG005133, RC2AG036535, P01-AG19724, P01 AG 09215-20, R01-AG 10897, 2P30AG10129, R01 AG029672, R01 AG16381)
- National Heart, Lung, and Blood Institute (HL-095089, R01HL095089)
- National Institute of Mental Health (U01MH083506, MH 22005, MH062512, MH083506, R01MH079752, R01MH069433, MH083552, MH079752, R01 MH069259, 5 R01 MH071940, MH069433, R01MH072641, P30MH062512)
- National Institute on Drug Abuse (1 R01 DA017830)
- National Cancer Institute (2 P30 CA93373, CA104573)
- National Institute of Diabetes and Digestive and Kidney Diseases (DK075128)
- National Institute of Neurological Disorders and Stroke (1 R01 NS050792, RL1NS062412, P50NS053488, 1 R01 NS049194, RC2NS069368, R01NS031966)
- National Institute of Biomedical Imaging and Bioengineering (1 U54 EB05149, 1 R01 EB 006266-01, U54EB005149, P01EB001955, 5R01EB002138-07)
- U.S. National Library of Medicine (2 R01LM005639)
- National Center for Research Resources (5 P41 RR013642, P41 RR-023953, U24RR021760, U54RR021813, R24 RR021382, R24 RR021992, KL2RR024144)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01HD050735, 1R01 HD053893-01)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Alzheimer's disease research and treatments
- Health Sciences › Medicine › Oncology › Cancer-related cognitive impairment studies
- Keywords
- Alzheimer's disease, mild cognitive impairment, neuroimaging biomarkers, cerebrospinal fluid biomarkers, amyloid-beta 42, longitudinal cognitive change, normal aging, memory impairment, ADAS-Cog
- MeSH terms
- Aged, Aged, 80 and over, Alzheimer Disease, Cross-Sectional Studies, Diagnostic Imaging, Female, Follow-Up Studies, Humans, Longitudinal Studies, Male, Middle Aged, Biomarkers
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-18, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-18 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
