27,780 Alzheimer's and dementia studies in plain English, each scored out of 100.
Temporal imaging variables may provide independent prognostic information beyond baseline risk, supporting a dynamic model of haemorrhagic risk in Alzheimer’s disease. 2 key findings →
Participants: 1,069 Evidence: low
Full summary, score breakdown and related studies → Original source
The occurrence of amyloid-related imaging abnormalities (ARIA), found in clinical trials for Aβ-immunotherapy, has been related to the antibody’s effector function on glial activation by the Fc portion of the antibody. 2 key findings →
Evidence: low
Full summary, score breakdown and related studies → Original source
DESI is a robust, fully automated MRI biomarker that sensitively detects CSF redistribution characteristic of DESH in iNPH. 1 key finding →
Design: longitudinal study Participants: 1,032 Evidence: moderate
Full summary, score breakdown and related studies → Original source
Traumatic brain injury (TBI) is the main cause of death and disability in the United States in people younger than 35 years old and is a common cause of wartime injuries. 1 key finding →
Evidence: low
Full summary, score breakdown and related studies → Original source
In this bvFTD cohort, structural volumes of key frontotemporal and perisylvian regions did not predict behavioral or language impairments after correction for multiple comparisons. 3 key findings →
Design: longitudinal study Participants: 87 Evidence: moderate
Full summary, score breakdown and related studies → Original source
Microglial activation and FR expression in APP/PS1 and P301S transgenic mouse models of AD was measured using flow cytometry. 5 key findings →
Evidence: low
Full summary, score breakdown and related studies → Original source
This method establishes a platform for high-throughput longitudinal drug screening in immunocompetent human COs that will enable the identification of new therapeutic candidates to modulate Aβ aggregation, glial reactivity, and neurodegeneration. 5 key findings →
Design: longitudinal study Evidence: moderate
Full summary, score breakdown and related studies → Original source
Vascular risk factors were associated with CSVD burden and progression, and WMH and PVS progression were further associated with cortical atrophy and cognitive decline, underscoring the interconnected contributions of vascular risk, CSVD, and neurodegeneration to cognitive aging and dementia risk. 5 key findings →
Design: cross-sectional Participants: 289 Evidence: moderate
Full summary, score breakdown and related studies → Original source
Multimodal integration of cognitive, neuroimaging, and polygenic data in the MHS-RWD tool yields strong discrimination for both early diagnosis of dementia and AD differential diagnosis. 2 key findings →
Participants: 1,100 Evidence: low
Full summary, score breakdown and related studies → Original source
Significant positive correlations were observed among pain, cognitive impairment, agitation, and neuropsychiatric symptoms (all p < 0.001). 4 key findings →
Participants: 107 Evidence: low
Full summary, score breakdown and related studies → Original source
Affective symptoms, such as depression, anxiety and apathy, are highly prevalent in cognitively impaired Lewy body disease (LBD) groups, including Parkinson’s disease dementia and dementia with Lewy bodies. 2 key findings →
Design: systematic review Participants: 785 Evidence: high
Full summary, score breakdown and related studies → Original source
To distinguish these cases, we trained three types of supervised 3D transformers (ViT3D, MINiT, NIT) and, as a non-collapsing alternative, a frozen self-supervised encoder (DINO), to classify people with dementia or mild cognitive impairment (MCI) versus cognitively normal (CN) individuals from 3D brain MRI, using data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). 2 key findings →
Evidence: low
Full summary, score breakdown and related studies → Original source
The Alzheimer’s Disease Neuroimaging Initiative (ADNI) is widely used to train machine learning models for Alzheimer’s disease, yet whether it provides consistent ground truth for predictive modeling has not been systematically tested. 3 key findings →
Design: longitudinal study Evidence: moderate
Full summary, score breakdown and related studies → Original source
Artificial-intelligence (AI) systems for Alzheimer’s disease (AD) increasingly use neuroimaging or spontaneous speech, but reported performance is difficult to interpret across studies because cohorts, endpoints, languages, preprocessing, and validation protocols differ. 5 key findings →
Evidence: low
Full summary, score breakdown and related studies → Original source
Deep learning classifiers trained on structural magnetic resonance imaging (MRI) are increasingly proposed for early detection of Alzheimer’s disease (AD) and mild cognitive impairment (MCI), yet the pipelines that produce these models depend on shared datasets, pre-trained weights, and federated updates that an adversary can poison. 1 key finding →
Evidence: low
Full summary, score breakdown and related studies → Original source
Alzheimer’s disease (AD) detection from structural magnetic resonance imaging (MRI) using deep learning depends on large, labelled datasets. 1 key finding →
Participants: 295 Evidence: moderate
Full summary, score breakdown and related studies → Original source
White matter hyperintensities (WMHs) are a hallmark of cerebral small vessel disease, commonly seen in cognitively normal older adults and patients with neurodegenerative disorders, and are thought to reflect varying degrees of tissue damage. 2 key findings →
Participants: 45 Evidence: low
Full summary, score breakdown and related studies → Original source
Magnetic resonance imaging (MRI) of the brain routinely combines complementary contrasts—including T1-weighted (T1w), T2-weighted (T2w) and FLAIR sequences—to capture anatomy and pathology. 2 key findings →
Evidence: low
Full summary, score breakdown and related studies → Original source
Our findings indicate that all methods captured AD disconnection patterns but exhibited variability in preserving connectivity profiles, which seemed non-random but rather systematically linked to clinical measures and predictive power for AD classification. 5 key findings →
Evidence: low
Full summary, score breakdown and related studies → Original source
The Alzheimer’s Disease Neuroimaging Initiative (ADNI) provides a comprehensive multimodal neuroimaging resource for studying aging and Alzheimer’s disease (AD). 5 key findings →
Design: longitudinal study Evidence: moderate
Full summary, score breakdown and related studies → Original source
Alzheimer's disease classification by means of Magnetic Resonance Imaging demands strong models that are able to generalise to different stages of dementia. 2 key findings →
Evidence: low
Full summary, score breakdown and related studies → Original source
Explainability methods applied to deep learning models for Alzheimer’s disease neuroimaging produce attribution maps that vary substantially across methods and architectures, yet no validated quantitative framework exists for determining which method most faithfully localises attribution signal within established AD neuroimaging biomarker anatomy at the individual subject level. 5 key findings →
Evidence: moderate
Full summary, score breakdown and related studies → Original source
Reported dementia-classification performance depends on how the label is defined, which clinical information the model sees, and whether participants are separated between training and test data. 5 key findings →
Participants: 150 Evidence: moderate
Full summary, score breakdown and related studies → Original source
HbA1c levels had warning value for cognitive impairment in non diabetes patients with WMH, and a nonlinear correlation exists between HbA1c and WMH-related cognitive impairment. 3 key findings →
Participants: 497 Evidence: low
Full summary, score breakdown and related studies → Original source
Insulin resistance is increasingly recognized as a metabolic factor associated with Alzheimer’s disease (AD); however, its relevance to hippocampal structural changes—a key pathological feature of AD—across disease stages is not fully understood. 4 key findings →
Evidence: low
Full summary, score breakdown and related studies → Original source
10 of the 2,885 studies in this tag, picked by a fixed rule (a hash of each study's address), not by score or date; each page of this list shows a different 10. The same 10 appear here every time.
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 27,780 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 27,780 records, built mostly from automated pulls of Europe PMC (61.1% of the library) and OpenAlex (18.3%), 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.
1,034 records (3.7% of the library) are not peer-reviewed — 962 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 →