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Linking Molecular Pathways and Large-Scale Computational Modeling to Assess Candidate Disease Mechanisms and Pharmacodynamics in Alzheimer's Disease
Stefanovski L, Triebkorn P, Spiegler A, Diaz-Cortes MA, Solodkin A, Jirsa V, McIntosh AR, Ritter P et al.. Frontiers in computational neuroscience, 2019-08-13
Topic: Amyloid, tau and blood-test diagnosis
Introduction: While the prevalence of neurodegenerative diseases associated with dementia such as Alzheimer's disease (AD) increases, our knowledge on the underlying mechanisms, outcome predictors, or therapeutic targets is limited. In this work, we demonstrate how computational multi-scale brain modeling links phenomena of different scales and therefore identifies potential disease mechanisms leading the way to improved diagnostics and treatment. Methods: The Virtual Brain (TVB; thevirtualbrain.org) neuroinformatics platform allows standardized large-scale structural connectivity-based simulations of whole brain dynamics.
Key findings
- In this work, we demonstrate how computational multi-scale brain modeling links phenomena of different scales and therefore identifies potential disease mechanisms leading the way to improved diagnostics and treatment.
- Methods: The Virtual Brain (TVB; thevirtualbrain.org) neuroinformatics platform allows standardized large-scale structural connectivity-based simulations of whole brain dynamics.
- We provide proof of concept for a novel approach that quantitatively links the effects of altered molecular pathways onto neuronal population dynamics.
- As a novelty, we connect chemical compounds measured with positron emission tomography (PET) with neural function in TVB addressing the phenomenon of hyperexcitability in AD related to the protein amyloid beta (Abeta).
Why it matters
This study used computational multi-scale brain modeling to link molecular pathways with large-scale brain dynamics and identify potential disease mechanisms in Alzheimer's disease. It is a proof-of-concept modeling study and does not test any treatment, cure, or prevention in patients.
Study details
- Evidence strengthLow
How this study scored 46/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, effect reported, studied in people and randomised / blinded because the record does not state it. We have no data on senior author's track record, so that was left out: it earned 44 of the 96 points we could judge it on, which is 46 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Frontiers in computational neuroscience) |
| Journal's citation record | 1 / 4 | Frontiers in Computational Neuroscience: journal h-index 105 (OpenAlex, 2026-10-29) |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 2 / 5 | 2019 — within 10 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 5 / 7 | Cited 148 times (OpenAlex, 2026-10-10) |
| Citations per year | 3 / 5 | 16.4 citations a year over 9 year(s) since 2019 |
| Impact for its field and year | 3 / 6 | Cited 7.2× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 1 / 6 | Cited by 2 other studies in this library |
| Still being cited | 3 / 4 | Cited 65 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | — / 4 | Senior author's record not available — left out of this score |
| Institution's track record | 1 / 3 | Charité - Universitätsmedizin Berlin: institution h-index 1,116 (OpenAlex) |
| Multi-centre collaboration | 3 / 3 | OpenAlex lists 11 institution(s) in 4 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 31456676) |
| Free to read | 2 / 2 | Free to read |
| Points | 44 / 96 | Scored on 19 of 20 parts. We have no data for the rest, so they are left out: 44 of the 96 points we could judge it on is 46 out of 100. |
| Total | 46 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.3389/fncom.2019.00054
- PubMed: 31456676
- Original source →
- Free full text: Frontiers in Computational Neuroscience →
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. The record's author list ends in “et al.”, so not every author is named. Check the citation against the original before you use it.
APA 7th
Stefanovski, L., Triebkorn, P., Spiegler, A., Diaz-Cortes, M. A., Solodkin, A., Jirsa, V., et al. (2019). Linking Molecular Pathways and Large-Scale Computational Modeling to Assess Candidate Disease Mechanisms and Pharmacodynamics in Alzheimer's Disease. Frontiers in computational neuroscience. https://doi.org/10.3389/fncom.2019.00054
Vancouver
Stefanovski L, Triebkorn P, Spiegler A, Diaz-Cortes MA, Solodkin A, Jirsa V, et al. Linking Molecular Pathways and Large-Scale Computational Modeling to Assess Candidate Disease Mechanisms and Pharmacodynamics in Alzheimer's Disease. Frontiers in computational neuroscience. 2019. doi:10.3389/fncom.2019.00054. PMID: 31456676.
BibTeX
@article{stefanovski2019-31456676,
author = {Stefanovski, L. and Triebkorn, P. and Spiegler, A. and Diaz-Cortes, M. A. and Solodkin, A. and Jirsa, V. and McIntosh, A. R. and Ritter, P. and others},
title = {{Linking Molecular Pathways and Large-Scale Computational Modeling to Assess Candidate Disease Mechanisms and Pharmacodynamics in Alzheimer's Disease}},
journal = {Frontiers in computational neuroscience},
year = {2019},
doi = {10.3389/fncom.2019.00054},
pmid = {31456676},
url = {https://doi.org/10.3389/fncom.2019.00054}
}
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