Author

Maxime Bertoux

Université de LilleORCID 0000-0002-5283-503XUnclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Organism:human· 4 Research Focus:benchmarking· 3 Assay:imaging· 3 Research Focus:differential expression· 2 Approach:statistical model· 2 Assay:bulk RNA-seq· 1 Research Focus:clustering· 1 Approach:contrastive learning· 1

Frequent Co-Authors

Alexander GerhardiD· 4 shared Alexandre de MendonçaiD· 4 shared Arabella BouziguesiD· 4 shared Barbara BorroniiD· 4 shared Benedetta NacmiasiD· 4 shared Caroline GraffiD· 4 shared Christopher ButleriD· 4 shared Daniela GalimbertiiD· 4 shared

Activity by Year

2025-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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4 shared papersNeuroscience

Alexander GerhardORCID

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Author profile →

4 papers

Recent·Most cited

Cerebrospinal fluid YWHAG:NPTX2 ratio predicts clinical severity and future phenoconversion in frontotemporal lobar degeneration

Hamilton Se‐Hwee Oh, Joshua D. Downer, Connor D. Dietz, Claire Yballa, Edoardo M. Marcora, Argentina Lario‐Lago, Hilary W. Heuer, Leah K. Forsberg, Jennifer Hsiao‐Nakamoto, Chi‐Lu Chiu, Paul Auger, Casey A. Powers, Gilbert Di Paolo, Fen Huang, Brian Stephen Appleby, , Scott J. Russo

The paper identifies the CSF YWHAG:NPTX2 ratio as a biomarker for predicting clinical severity and phenoconversion in FTLD, showing strong correlation with disease progression and improved trial design potential.

bioRxiv · Aug 31, 2026 · PreprintNeuroscience

Machine-learning MRI stratification of genetic frontotemporal dementia for clinical trial enrichment

Mahdie Soltaninejad, Yasser Iturria‐Medina, Arabella Bouzigues, Lucy Louise Russell, Phoebe H. Foster, Eve Ferry‐Bolder, John Cornelis Van Swieten, Lize Corrine Jiskoot, Harro Seelaar, Raquel Sánchez‐Valle, Robert Jr Laforce, Caroline Graff, Daniela Galimberti, Rik R. Vandenberghe, Alexandre de Mendonça, , GENFI

This paper uses machine learning to stratify genetic frontotemporal dementia patients into subtypes and provides progression scores that improve trial enrichment efficiency.

bioRxiv · Jul 23, 2026 · PreprintNeuroscience

MRI-based classifier to identify close-to-onset cases in C9orf72 genetic frontotemporal dementia

Mahdie Soltaninejad, Yasser Iturria‐Medina, Reza Rajabli, Gleb Bezgin, Niki Hosseini‐Kamkar, Arabella Bouzigues, Lucy Louise Russell, Phoebe H. Foster, Eve Ferry‐Bolder, John Cornelis Van Swieten, Lize Corrine Jiskoot, Harro Seelaar, Raquel Sánchez‐Valle, Robert Jr Laforce, Caroline Graff, , GENFI

The paper develops an MRI-based machine learning classifier to identify presymptomatic C9orf72 carriers at high risk of disease onset within two years.

bioRxiv · Oct 18, 2025 · PreprintNeuroscience

White Matter Hyperintensities Precede other Biomarkers in GRN Frontotemporal Dementia

Mahdie Soltaninejad, Mahsa Dadar, D. Louis Collins, Reza Rajabli, Vikram Venkatraghavan, Arabella Bouzigues, Lucy Louise Russell, Phoebe H. Foster, Eve Ferry‐Bolder, John Cornelis Van Swieten, Lize Corrine Jiskoot, Harro Seelaar, Raquel Sánchez‐Valle, Robert Jr Laforce, Caroline Graff, , GENFI

The paper shows that white matter hyperintensities (WMHs) appear before other biomarkers in GRN-related frontotemporal dementia, suggesting they are early indicators of disease progression.

Alzheimer s & Dementia · Oct 1, 2025Peer-reviewedNeuroscience