CoChat
Sign Up
Cited by 35Bioinformatics and Genomic NetworksDementia and Cognitive Impairment ResearchSingle-cell and spatial transcriptomics

Multi-omic integration via similarity network fusion to detect molecular subtypes of ageing

Open access
Yang, Mu, Matan-Lithwick, Stuart, Wang, Yanling, De Jager, Philip L, Bennett, David A, Felsky, Daniel·2023·Brain Communications
DOI 10.1093/braincomms/fcad110PMC PMC10110975
Open this paper in CoChat — free account

Abstract

Molecular subtyping of brain tissue provides insights into the heterogeneity of common neurodegenerative conditions, such as Alzheimer’s disease. However, existing subtyping studies have mostly focused on single data modalities and only those individuals with severe cognitive impairment. To address these gaps, we applied similarity network fusion, a method capable of integrating multiple high-dimensional multi-omic data modalities simultaneously, to an elderly sample spanning the full spectrum of cognitive ageing trajectories. We analyzed human frontal cortex brain samples characterized by five omic modalities: bulk RNA sequencing (18 629 genes), DNA methylation (53 932 CpG sites), histone acetylation (26 384 peaks), proteomics (7737 proteins) and metabolomics (654 metabolites). Similarity network fusion followed by spectral clustering was used for subtype detection, and subtype numbers were determined by Eigen-gap and rotation cost statistics. Normalized mutual information determined the relative contribution of each modality to the fused network. Subtypes were characterized by associations with 13 age-related neuropathologies and cognitive decline. Fusion of all five data modalities (n = 111) yielded two subtypes (nS1 = 53, nS2 = 58), which were nominally associated with diffuse amyloid plaques; however, this effect was not significant after correction for multiple testing. Histone acetylation (normalized mutual information = 0.38), DNA methylation (normalized mutual information = 0.18) and RNA abundance (normalized mutual information = 0.15) contributed most strongly to this network. Secondary analysis integrating only these three modalities in a larger subsample (n = 513) indicated support for both three- and five-subtype solutions, which had significant overlap, but showed varying degrees of internal stability and external validity. One subtype showed marked cognitive decline, which remained significant even after correcting for tests across both three- and five-subtype solutions (pBonf = 5.9 × 10−3). Comparison to single-modality subtypes demonstrated that the three-modal subtypes were able to uniquely capture cognitive variability. Comprehensive sensitivity analyses explored influences of sample size and cluster number parameters. We identified highly integrative molecular subtypes of ageing derived from multiple high dimensional, multi-omic data modalities simultaneously. Fusing RNA abundance, DNA methylation, and histone acetylation measures generated subtypes that were associated with cognitive decline. This work highlights the potential value and challenges of multi-omic integration in unsupervised subtyping of post-mortem brain.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1093/braincomms/fcad110

Cites

9 of 9 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

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?

  • Similarity network fusion analyses of brain tissue have identified clusters associated with immune activation, mitochondrial impairment, and lipid metabolism abnormalities.

    “For example, similarity network fusion analyses of brain tissue have identified clusters associated with immune activation, mitochondrial impairment, and lipid metabolism abnormalities352.”

    From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [352]

    The Results section reports molecular subtypes identified by similarity network fusion of human brain-tissue data. It describes subtype-associated molecular features, but does not characterize the clusters as associated with immune activation, mitochondrial impairment, or lipid metabolism abnormalities. Page numbers are unavailable in our copy. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • Analyses using similarity network fusion have identified Alzheimer's disease subtypes characterized by immune activation, mitochondrial dysfunction, lipid dysregulation, and blood-brain barrier disruption.

    “These analyses have identified subtypes characterized by immune activation, mitochondrial dysfunction, lipid dysregulation, and BBB disruption342,352,399,400.”

    From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [352]

    The Results section (‘Fully integrated five-modal network identifies two molecular subtypes’; page not provided) confirms that similarity network fusion identified molecular subtypes. The Results section on ‘Molecular features defining three-modal subtypes’ characterizes them using epigenetic marks and RNA transcripts, but our copy does not characterize the subtypes by immune activation, mitochondrial dysfunction, lipid dysregulation, or blood-brain barrier disruption. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • Proteomic clustering has identified subgroups particularly responsive to drugs that modulate synaptic function and restore blood-brain barrier integrity.

    “Proteomic clustering has identified subgroups particularly responsive to drugs that modulate synaptic function and restore BBB integrity342,345,347,350,352,399,400.”

    From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [352]

    The Methods section ('Multi-omic data used for subtyping'; page unavailable) says the study used proteomics alongside four other data types to identify molecular subtypes. The Results section ('Comparison with single modality subtypes and sensitivity analysis'; page unavailable) compares subtype associations with cognition and neuropathology. Neither passage establishes that any subgroup was particularly responsive to drugs targeting synaptic function or blood-brain barrier integrity; the claim also recasts multi-omic subtyping as proteomic clustering. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • Unsupervised learning techniques such as hierarchical clustering, k-means, and latent class analysis applied to integrated multi-omics datasets have revealed molecularly distinct Alzheimer's disease subgroups characterized by divergent patterns of neuroinflammation, metabolic dysregulation, and synaptic dysfunction.

    “Unsupervised learning techniques such as hierarchical clustering, k-means, and latent class analysis applied to integrated multi-omics datasets have revealed molecularly distinct AD subgroups characterized by divergent patterns of neuroinflammation, metabolic dysregulation, and synaptic dysfunction.”

    From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [352]

    Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

    Experimental — accuracy not yet measured

  • Integration across data modalities can introduce biases, as seen in joint analyses of transcriptomic and epigenomic features, which may complicate interpretation.

    “Integration across data modalities can introduce biases, as seen in joint analyses of transcriptomic and epigenomic features, which may complicate interpretation352,396.”

    From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [352]

    The Methods (Uniform multi-omic feature post-processing; page not provided) identify a risk that differences in preprocessing could leave modalities with unbalanced covariate effects, and describe correcting for it. The paper jointly analyzes RNAseq and epigenetic data, but our copy does not report that this integration actually introduced bias or explicitly link such bias to difficulty interpreting the results. (LLM, conf=0.81)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 66

  1. [1]Formánek T, Csajbók Z, Wolfová K, et al Trajectories of depressive symptoms and associated patterns of cognitive decline. Sci Rep. 2020; 10(1): 20888. PMC7705007 33257789
  2. [4]Breijyeh Z, Karaman R. Comprehensive review on Alzheimer’s disease: Causes and treatment. Molecules. 2020; 25(24): 5789. 33302541 PMC7764106
  3. [6]Jiang YZ, Liu Y, Xiao Y, et al Molecular subtyping and genomic profiling expand precision medicine in refractory metastatic triple-negative breast cancer: The FUTURE trial. Cell Res. 2021; 31(2): 178– 186. 32719455 PMC8027015
  4. [7]Zhao L, Lee VHF, Ng MK, Yan H, Bijlsma MF. Molecular subtyping of cancer: Current status and moving toward clinical applications. Brief Bioinform. 2019; 20(2): 572– 584. 29659698
  5. [9]Zheng C, Xu R. Molecular subtyping of Alzheimer’s disease with consensus non-negative matrix factorization. PloS One. 2021; 16(5): e0250278. PMC8136734 34014928
  6. [11]Ma M, Liao Y, Huang X, et al Identification of Alzheimer’s disease molecular subtypes based on parallel large-scale sequencing. Front Aging Neurosci. 2022; 14: 770136. PMC9112923 35592696
  7. [12]Emon MA, Heinson A, Wu P, et al Clustering of Alzheimer’s and Parkinson’s disease based on genetic burden of shared molecular mechanisms. Sci Rep. 2020; 10(1): 19097. PMC7645798 33154531
  8. [13]Picard M, Scott-Boyer MP, Bodein A, Périn O, Droit A. Integration strategies of multi-omics data for machine learning analysis. Comput Struct Biotechnol J. 2021; 19: 3735– 3746. 34285775 PMC8258788
  9. [15]Klein HU, McCabe C, Gjoneska E, et al Epigenome-wide study uncovers large-scale changes in histone acetylation driven by tau pathology in aging and Alzheimer’s human brains. Nat Neurosci. 2019; 22(1): 37– 46. 30559478 PMC6516529
  10. [16]Huo Z, Yu L, Yang J, Zhu Y, Bennett DA, Zhao J. Brain and blood metabolome for Alzheimer’s dementia: Findings from a targeted metabolomics analysis. Neurobiol Aging. 2020; 86: 123– 133. 31785839 PMC6995427
  11. [17]Wang G, Zhou Y, Huang FJ, et al Plasma metabolite profiles of Alzheimer’s disease and mild cognitive impairment. J Proteome Res. 2014; 13(5): 2649– 2658. 24694177
  12. [18]Toledo JB, Arnold M, Kastenmüller G, et al Metabolic network failures in Alzheimer’s disease: A biochemical road map. Alzheimers Dement J Alzheimers Assoc. 2017; 13(9): 965– 984. PMC5866045 28341160
  13. [19]Arnold M, Nho K, Kueider-Paisley A, et al Sex and APOE ε4 genotype modify the Alzheimer’s disease serum metabolome. Nat Commun. 2020; 11(1): 1148. 32123170 PMC7052223
  14. [20]John-Williams L S, Blach C, Toledo JB, et al Targeted metabolomics and medication classification data from participants in the ADNI1 cohort. Sci Data. 2017; 4: 170140. PMC5644370 29039849
  15. [21]Johnson ECB, Carter EK, Dammer EB, et al Large-scale deep multi-layer analysis of Alzheimer’s disease brain reveals strong proteomic disease-related changes not observed at the RNA level. Nat Neurosci. 2022; 25(2): 213– 225. 35115731 PMC8825285
  16. [22]Badhwar A, McFall GP, Sapkota S, et al A multiomics approach to heterogeneity in Alzheimer’s disease: Focused review and roadmap. Brain. 2020; 143(5): 1315– 1331. 31891371 PMC7241959
  17. [23]Singh A, Shannon CP, Gautier B, et al DIABLO: An integrative approach for identifying key molecular drivers from multi-omics assays. Bioinformatics. 2019; 35(17): 3055– 3062. 30657866 PMC6735831
  18. [24]Ma Y, Klein H, De Jager PL. Considerations for integrative multi-omic approaches to explore Alzheimer’s disease mechanisms. Brain Pathol. 2020; 30(5): 984– 981. 32654306 PMC8018035
  19. [25]Rappoport N, Shamir R. Multi-omic and multi-view clustering algorithms: Review and cancer benchmark. Nucleic Acids Res. 2018; 46(20): 10546– 10562. 30295871 PMC6237755
  20. [26]Chauvel C, Novoloaca A, Veyre P, Reynier F, Becker J. Evaluation of integrative clustering methods for the analysis of multi-omics data. Brief Bioinform. 2020; 21(2): 541– 552. 31220206
  21. [27]Wang B, Mezlini AM, Demir F, et al Similarity network fusion for aggregating data types on a genomic scale. Nat Methods. 2014; 11(3): 333– 337. 24464287
  22. [28]Bennett DA, Buchman AS, Boyle PA, Barnes LL, Wilson RS, Schneider JA. Religious orders study and rush memory and aging project. J Alzheimers Dis JAD. 2018; 64(s1): S161– S189. 29865057 PMC6380522
  23. [29]De Jager PL, Ma Y, McCabe C, et al A multi-omic atlas of the human frontal cortex for aging and Alzheimer’s disease research. Sci Data. 2018; 5(1): 180142. PMC6080491 30084846
  24. [30]Bennett DA, Schneider JA, Buchman AS, Barnes LL, Boyle PA, Wilson RS. Overview and findings from the rush memory and aging project. Curr Alzheimer Res. 2012; 9(6): 646– 663. 22471867 PMC3439198
  25. [31]Felsky D, Klein H, Menon V, et al Human peripheral monocytes capture elements of the state of microglial activation in the brain. Res Sq. 2022. preprint. doi: 10.21203/rs.3.rs-1226021/v1 · DOI
  26. [32]Rybnicek J, Chen Y, Millic M, et al CHRNA5 links chandelier cells to protection against amyloid pathology in human aging and Alzheimer’s Disease. BioRxiv. 2022. preprint. doi: 10.1101/2022.05.03.490491 PMC10853183 38331937 · DOI
  27. [34]Wei R, Wang J, Su M, et al Missing value imputation approach for mass spectrometry-based metabolomics data. Sci Rep. 2018; 8(1): 663. 29330539 PMC5766532
  28. [35]Wingo TS, Duong DM, Zhou M, et al Integrating next-generation genomic sequencing and mass spectrometry to estimate allele-specific protein abundance in human brain. J Proteome Res. 2017; 16(9): 3336– 3347. 28691493 PMC5698003
  29. [36]Mertins P, Tang LC, Krug K, et al Reproducible workflow for multiplexed deep-scale proteome and phosphoproteome analysis of tumor tissues by liquid chromatography–mass spectrometry. Nat Protoc. 2018; 13(7): 1632– 1661. 29988108 PMC6211289
  30. [37]Kokla M, Virtanen J, Kolehmainen M, Paananen J, Hanhineva K. Random forest-based imputation outperforms other methods for imputing LC-MS metabolomics data: A comparative study. BMC Bioinformatics. 2019; 20(1): 492. 31601178 PMC6788053
  31. [38]Mostafavi S, Gaiteri C, Sullivan SE, et al A molecular network of the aging human brain provides insights into the pathology and cognitive decline of Alzheimer’s disease. Nat Neurosci. 2018; 21(6): 811– 819. 29802388 PMC6599633
  32. [39]Consensus recommendations for the postmortem diagnosis of Alzheimer’s disease. The National Institute on Aging, and Reagan Institute Working Group on diagnostic criteria for the neuropathological assessment of Alzheimer’s disease. Neurobiol Aging. 1997; 18(Suppl 4): S1– S2. 9330978
  33. [40]Wilson RS, Boyle PA, Yu L, et al Temporal course and pathologic basis of unawareness of memory loss in dementia. Neurology. 2015; 85(11): 984– 991. 26311746 PMC4567465
  34. [41]De Jager PL, Shulman JM, Chibnik LB, et al A genome-wide scan for common variants affecting the rate of age-related cognitive decline. Neurobiol Aging. 2012; 33(5): 1017.e1– 1017.e15. PMC3307898 22054870
  35. [42]Bocancea DI, van Loenhoud AC, Groot C, Barkhof F, van der Flier WM, Ossenkoppele R. Measuring resilience and resistance in aging and Alzheimer disease using residual methods: A systematic review and meta-analysis. Neurology. 2021; 97(10): 474– 488. 34266918 PMC8448552
  36. [43]Consens ME, Chen Y, Menon V, et al Bulk and single-nucleus transcriptomics highlight intra-telencephalic and somatostatin neurons in Alzheimer’s disease. Front Mol Neurosci. 2022; 15: 903175. PMC9231610 35754708
  37. [44]Stefanik L, Erdman L, Ameis SH, et al Brain-behavior participant similarity networks among youth and emerging adults with schizophrenia spectrum, autism spectrum, or bipolar disorder and matched controls. Neuropsychopharmacology. 2018; 43(5): 1180– 1188. 29105664 PMC5854811
  38. [45]Park S, Zhao H. Spectral clustering based on learning similarity matrix. Bioinformatics. 2018; 34(12): 2069– 2076. 29432517 PMC6454479
  39. [46]Huang J, Nie F, Huang H. Spectral rotation versus K-means in spectral clustering. Proc AAAI Conf Artif Intell. 2013; 27(1): 431– 437.
  40. [47]Handl J, Knowles J, Kell DB. Computational cluster validation in post-genomic data analysis. Bioinformatics. 2005; 21(15): 3201– 3212. 15914541
  41. [49]Sekula M, Datta S, Datta S. Optcluster: An R package for determining the optimal clustering algorithm. Bioinformation. 2017; 13(3): 101– 103. 28584451 PMC5450252
  42. [50]Yan L, Yang M, Guo H, et al Single-cell RNA-Seq profiling of human preimplantation embryos and embryonic stem cells. Nat Struct Mol Biol. 2013; 20(9): 1131– 1139. 23934149
  43. [51]Jacobs GR, Voineskos AN, Hawco C, et al Integration of brain and behavior measures for identification of data-driven groups cutting across children with ASD, ADHD, or OCD. Neuropsychopharmacology. 2021; 46(3): 643– 653. 33168947 PMC8027842
  44. [52]Chacón JE, Rastrojo AI. Minimum adjusted Rand index for two clusterings of a given size. Adv Data Anal Classif. 2023; 17(1): 125– 133.
  45. [53]Cohen JE. The distribution of the chi-squared statistic under clustered sampling from contingency tables. J Am Stat Assoc. 1976; 71(355): 665– 670.
  46. [54]Kent WJ, Sugnet CW, Furey TS, et al The human genome browser at UCSC. Genome Res. 2002; 12(6): 996– 1006. 12045153 PMC186604
  47. [55]Alashwal H, El Halaby M, Crouse JJ, Abdalla A, Moustafa AA. The application of unsupervised clustering methods to Alzheimer’s disease. Front Comput Neurosci. 2019; 13: 31. 31178711 PMC6543980
  48. [56]Lu X, Wang L, Yu C, Yu D, Yu G. Histone acetylation modifiers in the pathogenesis of Alzheimer’s disease. Front Cell Neurosci. 2015; 9: 226. 26136662 PMC4468862
  49. [58]Hamamoto R, Komatsu M, Takasawa K, Asada K, Kaneko S. Epigenetics analysis and integrated analysis of multiomics data, including epigenetic data, using artificial intelligence in the era of precision medicine. Biomolecules. 2019; 10(1): 62. 31905969 PMC7023005
  50. [59]Elliott GO, Johnson IT, Scarll J, et al Quantitative profiling of CpG island methylation in human stool for colorectal cancer detection. Int J Colorectal Dis. 2013; 28(1): 35– 42. 22791128
  51. [60]Leygo C, Williams M, Jin HC, et al DNA methylation as a noninvasive epigenetic biomarker for the detection of cancer. Dis Markers. 2017; 2017: 1– 13. PMC5605861 29038612
  52. [61]Scheubert L, Luštrek M, Schmidt R, Repsilber D, Fuellen G. Tissue-based Alzheimer gene expression markers–comparison of multiple machine learning approaches and investigation of redundancy in small biomarker sets. BMC Bioinformatics. 2012; 13(1): 266. 23066814 PMC3574043
  53. [62]Li QS, De Muynck L. Differentially expressed genes in Alzheimer’s disease highlighting the roles of microglia genes including OLR1 and astrocyte gene CDK2AP1. Brain Behav Immun Health. 2021; 13: 100227. PMC8474442 34589742
  54. [63]Liu D, Dai SX, He K, et al Identification of hub ubiquitin ligase genes affecting Alzheimer’s disease by analyzing transcriptome data from multiple brain regions. Sci Prog. 2021; 104(1): 368504211001146. PMC10454942 33754896
  55. [64]Vastrad B, Vastrad C. Bioinformatics analyses of significant genes, related pathways and candidate prognostic biomarkers in Alzheimer’s disease. BioRxiv. 2021. preprint. doi: 10.1101/2021.05.06.442918 · DOI
  56. [65]Kim DY, Ingano LAM, Kovacs DM. Nectin-1α, an immunoglobulin-like receptor involved in the formation of synapses, is a substrate for presenilin/γ-secretase-like cleavage. J Biol Chem. 2002; 277(51): 49976– 49981. 12376527
  57. [66]De Paepe B, Merckx C, Jarošová J, Cannizzaro M, De Bleecker JL. Myo-inositol transporter SLC5A3 associates with degenerative changes and inflammation in sporadic inclusion body myositis. Biomolecules. 2020; 10(4): 521. 32235474 PMC7226596
  58. [67]Braithwaite SP, Stock JB, Lombroso PJ, Nairn AC. Protein phosphatases and Alzheimer’s disease. Prog Mol Biol Transl Sci. 2012; 106: 343– 379. 22340724 PMC3739963
  59. [68]Kaut O, Schmitt I, Wüllner U. Genome-scale methylation analysis of Parkinson’s disease patients’ brains reveals DNA hypomethylation and increased mRNA expression of cytochrome P450 2E1. Neurogenetics. 2012; 13(1): 87– 91. 22238121
  60. [70]Lokireddy S, Kukushkin NV, Goldberg AL. cAMP-induced phosphorylation of 26S proteasomes on Rpn6/PSMD11 enhances their activity and the degradation of misfolded proteins. Proc Natl Acad Sci U S A. 2015; 112(52): E7176– 85. 26669444 PMC4702992
  61. [71]Giri M, Shah A, Upreti B, Rai JC. Unraveling the genes implicated in Alzheimer’s disease. Biomed Rep. 2017; 7(2): 105– 114. 28781776 PMC5526178
  62. [72]Yuan SX, Li HT, Gu Y, Sun X. Brain-specific gene expression and quantitative traits association analysis for mild cognitive impairment. Biomedicines. 2021; 9(6): 658. 34201204 PMC8229744
  63. [73]Clough RL, Dermentzaki G, Stefanis L. Functional dissection of the α-synuclein promoter: transcriptional regulation by ZSCAN21 and ZNF219. J Neurochem. 2009; 110(5): 1479– 1490. 19549071
  64. [74]Barrachina M, Ferrer I. DNA methylation of Alzheimer disease and tauopathy-related genes in postmortem brain. J Neuropathol Exp Neurol. 2009; 68(8): 880– 891. 19606065
  65. [75]Fang Y, Wang J. Selection of the number of clusters via the bootstrap method. Comput Stat Data Anal. 2012; 56(3): 468– 477.
  66. [76]Horne E, Tibble H, Sheikh A, Tsanas A. Challenges of clustering multimodal clinical data: Review of applications in asthma subtyping. JMIR Med Inform. 2020; 8(5): e16452. PMC7290450 32463370

Cited by

Sorted by citations

Authors
Yang, M., Matan-Lithwick, S., Wang, Y., De Jager, P. L., Bennett, D. A., & Felsky, D.
Affiliations
  • Centre for Addiction and Mental Health (CA)
  • The Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health , Toronto, ON M5T 1R8 , Canada
  • Rush University (US)
  • The Center for Translational and Computational Neuroimmunology, Columbia University Medical Center , New York, NY 10033 , USA
Corresponding author
Felsky, Daniel
ORCID
Venue
Brain Communications
Publisher
Oxford University Press (OUP)
Published
2023-03-02
Volume
5
Issue
2
Pages
fcad110-fcad110
ISSN
2632-1297 (ISSN-L)
DOI
10.1093/braincomms/fcad110
PMC
PMC10110975
OpenAlex
W4362596377
Cited by
35
References
9
Citations by year
2026: 12 · 2025: 15 · 2024: 6 · 2023: 2
FWCI
3.12
Type
journal article
Language
en
Open access
Licence
Locations
  • Brain Communications (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
  • Koerner Family Foundation New Scientist Program
  • Canadian Institutes of Health Research
  • Centre for Addiction and Mental Health
  • National Institutes of Health (P30AG10161, P30AG72975, R01AG15819, R01AG17917, U01AG46152, U01AG61356)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
  • Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
Keywords
cognitive aging, brain aging, frontal cortex, similarity network fusion, multi-omics integration, DNA methylation, histone acetylation, RNA expression, proteomics, metabolomics, cognitive decline, amyloid plaques, postmortem brain, neurodegeneration, Alzheimer's disease, spectral clustering

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.

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