31,780 Alzheimer's and dementia studies in plain English, each scored out of 100.
Early identification of individuals at risk of developing Alzheimer's disease (AD) dementia is important for developing disease-modifying therapies. 4 key findings →
Design: cross-sectional Participants: 1,677 Evidence: moderate
Full summary, score breakdown and related studies → Original source Free full text
The most novel characteristics of our machine learning model compared to previous ones are the following: 1) our deep learning model is multi-tasking, in the sense that it jointly learns to simultaneously predict both MCI to AD conversion as well as AD vs. healthy controls classification, which facilitates relevant feature extraction for AD prognostication; 2) the neural network classifier employs fewer parameters than other deep learning architectures which significantly limits data-overfitting (we use ∼550,000 network parameters, which is orders of magnitude lower than other network designs); 3) both structural MRI images and their warp field characteristics, which quantify local volumetric changes in relation to the MRI template, were used as separate input streams to extract as much information as possible from the MRI data. 1 key finding →
Participants: 785 Evidence: low
Full summary, score breakdown and related studies → Original source Free full text
We propose a new method of measuring atrophy of brain structures by simultaneously segmenting longitudinal magnetic resonance (MR) images. 4 key findings →
Design: longitudinal study Participants: 206 Evidence: moderate
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Amyloid positron-emission-tomography (PET) offers an important research and diagnostic tool for investigating Alzheimer's disease (AD). 1 key finding →
Design: longitudinal study Participants: 316 Evidence: moderate
Full summary, score breakdown and related studies → Original source
Positive cerebrospinal fluid (CSF) biomarkers of tau and amyloid beta42 suggest possible active underlying Alzheimer's disease (AD) including neurometabolic dysfunction and neurodegeneration leading to eventual cognitive decline. 4 key findings →
Design: cohort study Participants: 82 Evidence: moderate
Full summary, score breakdown and related studies → Original source Free full text
Alzheimer's disease (AD), the most common type of dementia, is a severe neurodegenerative disorder. 4 key findings →
Design: longitudinal study Evidence: moderate
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The aim of this paper is to systematically evaluate a biased sampling issue associated with genome-wide association analysis (GWAS) of imaging phenotypes for most imaging genetic studies, including the Alzheimer's Disease Neuroimaging Initiative (ADNI). 3 key findings →
Design: case-control study Evidence: moderate
Full summary, score breakdown and related studies → Original source
Hippocampal volumetry derived from structural MRI is increasingly used to delineate regions of interest for functional measurements, assess efficacy in therapeutic trials of Alzheimer's disease (AD) and has been endorsed by the new AD diagnostic guidelines as a radiological marker of disease progression. 4 key findings →
Design: longitudinal study Evidence: moderate
Full summary, score breakdown and related studies → Original source Free full text
Spatial patterns of brain atrophy in mild cognitive impairment (MCI) and Alzheimer's disease (AD) were measured via methods of computational neuroanatomy. 3 key findings →
Evidence: moderate
Full summary, score breakdown and related studies → Original source Free full text
Longitudinal neuroimaging (LNI) studies are rapidly becoming more prevalent and growing in size. 1 key finding →
Design: longitudinal study Evidence: moderate
Full summary, score breakdown and related studies → Original source
Scanning the entire genome in search of variants related to imaging phenotypes holds great promise in elucidating the genetic etiology of neurodegenerative disorders. 3 key findings →
Design: longitudinal study Evidence: moderate
Full summary, score breakdown and related studies → Original source
Predicting Alzheimer's disease (AD) in individuals with some symptoms of cognitive decline may have great influence on treatment choice and disease progression. 4 key findings →
Evidence: low
Full summary, score breakdown and related studies → Original source Free full text
We propose a novel framework for the automatic propagation of a set of manually labeled brain atlases to a diverse set of images of a population of subjects. 4 key findings →
Evidence: low
Full summary, score breakdown and related studies → Original source Free full text
Functional connectomes reveal biomarkers of individual psychological or clinical traits. 5 key findings →
Participants: 2,000 Evidence: low
Full summary, score breakdown and related studies → Original source
Alzheimer's Disease (AD) is characterized by a cascade of biomarkers becoming abnormal, the pathophysiology of which is very complex and largely unknown. 2 key findings →
Design: cross-sectional Evidence: low
Full summary, score breakdown and related studies → Original source
Analysis of incomplete data is a big challenge when integrating large-scale brain imaging datasets from different imaging modalities. 1 key finding →
Participants: 780 Evidence: low
Full summary, score breakdown and related studies → Original source
However, most existing research focuses on only a single modality of biomarkers for diagnosis of AD and MCI, although recent studies have shown that different biomarkers may provide complementary information for the diagnosis of AD and MCI. 4 key findings →
Evidence: low
Full summary, score breakdown and related studies → Original source
For the last decade, it has been shown that neuroimaging can be a potential tool for the diagnosis of Alzheimer's Disease (AD) and its prodromal stage, Mild Cognitive Impairment (MCI), and also fusion of different modalities can further provide the complementary information to enhance diagnostic accuracy. 3 key findings →
Evidence: low
Full summary, score breakdown and related studies → Original source
The genetics dataset yielded two subtypes of AD characterized mainly by the presence/absence of the apolipoprotein E (APOE) ε4 genotype, but also involving differential presence of risk alleles of CD2AP, SPON1 and LOC39095 SNPs that were associated with differences in the respective patterns of brain atrophy, especially in the precuneus. 2 key findings →
Evidence: low
Full summary, score breakdown and related studies → Original source
However, a more recent work, derived in part from functional neuroimaging studies, has suggested that other brain structures make up a large-scale network that appears to support successful encoding and retrieval of episodic memories. 5 key findings →
Evidence: low
Full summary, score breakdown and related studies → Original source
The early identification of brain anatomy deviating from the normal pattern of growth and atrophy, such as in Alzheimer's disease (AD), has the potential to improve clinical outcomes through early intervention. 1 key finding →
Evidence: low
Full summary, score breakdown and related studies → Original source
Many machine learning and pattern classification methods have been applied to the diagnosis of Alzheimer's disease (AD) and its prodromal stage, i.e., mild cognitive impairment (MCI). 1 key finding →
Evidence: low
Full summary, score breakdown and related studies → Original source
Mild cognitive impairment (MCI) is a transitional stage between age-related cognitive decline and Alzheimer's disease (AD). 3 key findings →
Evidence: low
Full summary, score breakdown and related studies → Original source
Neurodegenerative disorders, such as Alzheimer's disease, are associated with changes in multiple neuroimaging and biological measures. 2 key findings →
Evidence: low
Full summary, score breakdown and related studies → Original source
There are growing numbers of studies using machine learning approaches to characterize patterns of anatomical difference discernible from neuroimaging data. 1 key finding →
Evidence: low
Full summary, score breakdown and related studies → Original source
Free full text links and the Full text may need a subscription label come from Unpaywall, Europe PMC and OpenAlex records of free copies, checked 10 Oct 2026. A study with neither is unknown, not paywalled. Show only studies that are free to read
This is alzhub's own score. It is not an official metric, not an impact factor, and no journal, publisher or ranking body endorses it. We built it so that a reader who is not a scientist can tell a landmark trial from a conference abstract without having to already know the difference. Every point comes from one of 20 components, all of them printed on the study itself, so you can disagree with our arithmetic rather than having to trust the total.
Journals are placed in ordered tiers, never given a numeric impact factor — we could not verify current impact factors from a source we control, and an invented one would be worse than none. Tier 1 is the top-tier general journals (NEJM, The Lancet, JAMA, Nature, Science, Nature Medicine); tier 2 is the leading specialty journals; tier 3 is every other peer-reviewed journal. A separate part scores how much the journal's own papers are cited, which tells the tier-3 journals apart.
Two rules we don't bend. First, we never fill in a missing value: if a record does not state a sample size or a design, that component scores zero, and if we simply have no data for a part, such as a citation count, that part is left out and the study is scored on the parts we could check. The study card says which. Second, anything that is not a peer-reviewed article is capped at 16/100 and carries a warning chip — so a conference abstract or a company press release can never outrank a peer-reviewed paper, no matter how good it looks.
We rank a rigorous null result — a good trial showing that something does not work — above an early positive finding. Someone deciding whether to buy a supplement is served by that, and the rigour is already counted by the design and journal components.
The score is computed from the published data alone and is reproducible: the same file always yields the same numbers.
See the whole rubric drawn out → — four charts covering all 31,780 studies: how the scores are spread and where the cap bites, which of the 20 components the library is strong and weak on, which subjects rest on stronger evidence, and what kinds of study each subject is actually built from.
This is a large sample of the Alzheimer's literature, not an exhaustive index of it. It holds 31,780 records, built mostly from automated pulls of Europe PMC (66% of the library) and OpenAlex (16%), plus smaller pulls from PubMed, Crossref and medRxiv. If a study, a drug, or a finding isn't here, that is not evidence it doesn't exist in the literature — it may simply not have been pulled in yet.
1,052 records (3.3% of the library) are not peer-reviewed — 973 of those are preprints posted before any journal review, and the rest are conference abstracts, regulatory notices and reports. Every study card on this page states its status plainly, and the score above is capped for anything that isn't a peer-reviewed article.
See the full, machine-readable breakdown of this library's coverage →
Newest studies, as a dated timeline → Atom feed Where this research is being discussed: events