Connectome-based biophysics models of Alzheimer’s disease diagnosis and prognosis

Torok, Justin; Anand, Chaitali; Verma, Parul; Raj, Ashish

doi:10.1016/j.trsl.2022.08.008

Cited by 2 publications

(1 citation statement)

References 174 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As mentioned previously, we focused our scoping review on models that considered AD progression, or a proxy of AD progression, as a state variable. Other reviews have discussed the mathematical modeling of different elements involved in AD progression, such as amyloid beta aggregation and tau proteins (Vosoughi et al, 2020 ), the impact of interregional connectivity (Torok et al, 2023 ), and protein misfolding (Carbonell et al, 2018 ). Our approach was different, as we aimed to focus on assessing the validity of mathematical models of AD, including their adequacy with clinical results.…”

Section: Discussionmentioning

confidence: 99%

A scoping review of mathematical models covering Alzheimer's disease progression

Moravveji,

Doyon,

Mashreghi

et al. 2024

Front. Neuroinform.

View full text Add to dashboard Cite

Alzheimer's disease is a complex, multi-factorial, and multi-parametric neurodegenerative etiology. Mathematical models can help understand such a complex problem by providing a way to explore and conceptualize principles, merging biological knowledge with experimental data into a model amenable to simulation and external validation, all without the need for extensive clinical trials. We performed a scoping review of mathematical models describing the onset and evolution of Alzheimer's disease as a result of biophysical factors following the PRISMA standard. Our search strategy applied to the PubMed database yielded 846 entries. After using our exclusion criteria, only 17 studies remained from which we extracted data, which focused on three aspects of mathematical modeling: how authors addressed continuous time (since even when the measurements are punctual, the biological processes underlying Alzheimer's disease evolve continuously), how models were solved, and how the high dimensionality and non-linearity of models were managed. Most articles modeled Alzheimer's disease at the cellular level, operating on a short time scale (e.g., minutes or hours), i.e., the micro view (12/17); the rest considered regional or brain-level processes with longer timescales (e.g., years or decades) (the macro view). Most papers were concerned primarily with amyloid beta (n = 8), few described both amyloid beta and tau proteins (n = 3), while some considered more than these two factors (n = 6). Models used partial differential equations (n = 3), ordinary differential equations (n = 7), and both partial differential equations and ordinary differential equations (n = 3). Some did not specify their mathematical formalism (n = 4). Sensitivity analyses were performed in only a small number of papers (4/17). Overall, we found that only two studies could be considered valid in terms of parameters and conclusions, and two more were partially valid. This puts the majority (n = 13) as being either invalid or with insufficient information to ascertain their status. This was the main finding of our paper, in that serious shortcomings make their results invalid or non-reproducible. These shortcomings come from insufficient methodological description, poor calibration, or the impossibility of experimentally validating or calibrating the model. Those shortcomings should be addressed by future authors to unlock the usefulness of mathematical models in Alzheimer's disease.

show abstract

Section: Discussionmentioning

confidence: 99%

A scoping review of mathematical models covering Alzheimer's disease progression

Moravveji,

Doyon,

Mashreghi

et al. 2024

Front. Neuroinform.

View full text Add to dashboard Cite

show abstract

Beyond the usual suspects: multi-factorial computational models in the search for neurodegenerative disease mechanisms

Khan,

Iturria-Medina

2024

Transl Psychiatry

View full text Add to dashboard Cite

From Alzheimer’s disease to amyotrophic lateral sclerosis, the molecular cascades underlying neurodegenerative disorders remain poorly understood. The clinical view of neurodegeneration is confounded by symptomatic heterogeneity and mixed pathology in almost every patient. While the underlying physiological alterations originate, proliferate, and propagate potentially decades before symptomatic onset, the complexity and inaccessibility of the living brain limit direct observation over a patient’s lifespan. Consequently, there is a critical need for robust computational methods to support the search for causal mechanisms of neurodegeneration by distinguishing pathogenic processes from consequential alterations, and inter-individual variability from intra-individual progression. Recently, promising advances have been made by data-driven spatiotemporal modeling of the brain, based on in vivo neuroimaging and biospecimen markers. These methods include disease progression models comparing the temporal evolution of various biomarkers, causal models linking interacting biological processes, network propagation models reproducing the spatial spreading of pathology, and biophysical models spanning cellular- to network-scale phenomena. In this review, we discuss various computational approaches for integrating cross-sectional, longitudinal, and multi-modal data, primarily from large observational neuroimaging studies, to understand (i) the temporal ordering of physiological alterations, i(i) their spatial relationships to the brain’s molecular and cellular architecture, (iii) mechanistic interactions between biological processes, and (iv) the macroscopic effects of microscopic factors. We consider the extents to which computational models can evaluate mechanistic hypotheses, explore applications such as improving treatment selection, and discuss how model-informed insights can lay the groundwork for a pathobiological redefinition of neurodegenerative disorders.

show abstract

Connectome-based biophysics models of Alzheimer’s disease diagnosis and prognosis

Cited by 2 publications

References 174 publications

A scoping review of mathematical models covering Alzheimer's disease progression

A scoping review of mathematical models covering Alzheimer's disease progression

Beyond the usual suspects: multi-factorial computational models in the search for neurodegenerative disease mechanisms

Contact Info

Product

Resources

About